Effects on lifetime of lithium ion batteries using different charging strategies: case study Stockholm Arlanda Airport

DEGREE PROJECT IN THE FIELD OF TECHNOLOGY ENERGY AND ENVIRONMENT AND THE MAIN FIELD OF STUDY ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Effects on lifetime of lithium ion batteries using different charging strategies: case study Stockholm Arlanda Airport VIKTOR ÅKERBERG KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

KTH Royal Institute of Technology Master Thesis TRITA-EECS-EX-2018:662 Effects on lifetime of lithium ion batteries using different charging strategies: case study Stockholm Arlanda Airport Author: Viktor Åkerberg Supervisor: Prof. Lina Bertling Tjernberg October 23, 2018

i Abstract Viktor Åkerberg Effects on lifetime of lithium ion batteries using different charging strategies: case study Stockholm Arlanda Airport Electric vehicles are necessary in the shift away from combustion engine vehicles in order to reduce the transport sectors greenhouse gas emissions. Unlike vehicles with combustion engines, electric vehicles need to be charged, a process that takes variable duration of time depending on the power of the charger, the battery size and the charging pattern. This report focuses on lithium ion batteries in an electric vehicle. Lithium-ion batteries are the most common batteries in electric vehicles due to their high specific energy and energy density. The first chapter explains the composition of a lithium ion battery from a single battery cell up to an entire battery system, and a literature review was made in order to further understand failure causes of lithum ion batteries. The second chapter analyses how the lifetime of a battery is affected by three different charging strategies and the ambient temperature. The analysis was done on a case study of ground support vehicles on Stockholm Arlanda Airport, Sweden. The conclusions drawn from the analysis are that the battery should be charged with state of charge limits close to 50 %, a low charging power and a temperature close to 20 C in order to maximize lifetime. Since charging with low power takes a significantly longer time compared to higher power charging options, future work should focus on weighing the efficiency of the ground support vehicles with the need for a long battery lifetime.

ii Sammanfattning Viktor Åkerberg Effects on lifetime of lithium ion batteries using different charging strategies: case study Stockholm Arlanda Airport Elektriska fordon är nödvändiga vid övergången bort från förbränningsmotorer för att minska utsläppen av växthusgaser inom transportsektorn. Till skillnad från fordon med förbränningsmotorer måste elfordon laddas, en process som tar olika lång tid beroende på effekten av laddaren, batteristorleken och laddningsmönstret. Denna rapport fokuserar på litium-jonbatterier i ett elfordon. På grund av deras höga energitäthet är litium-jonbatterier de vanligaste batterierna i elfordon. Det första kapitlet förklarar sammansättningen av ett litium-jonbatteri från en enda battericell upp till ett helt batterisystem, och en litteraturstudie gjordes för att ytterligare förstå felorsaker till litiumjonbatterier. Det andra kapitlet analyserar hur batteriets livslängd påverkas av tre olika laddningsstrategier och omgivningstemperaturen. Analysen gjordes i en fallstudie av markfordon på Stockholm Arlanda Airport, Sverige. Slutsatserna från analysen är att batteriet bör laddas med laddningstillståndsgränser nära 50 %, låg laddningseffekt och en temperatur nära 20 C för att maximera livslängden. Eftersom laddningen med låg effekt tar betydligt längre tid jämfört med högre laddningsalternativ, bör det framtida arbetet fokusera på att väga effektiviteten hos markfordonen med behovet av lång batterilivslängd.

iii Acknowledgements I would like to express my gratitude to Lina Bertling Tjernberg, who has supervised me and given me guidance during this project. I would also like to thank Julia Damström and Filip Jarnehammar, who has been part of the same research group and have helped me immensely with solving problems and coming up with great ideas. In addition, I want to thank Rakel Wreland Lindström from KTH, Ying He from Vattenfall, Henrik Lagerström from SEK Svensk Elstandard and Giovanni Velotto from ABB, who have helped us in a reference group with advice on various subjects. I would also like to thank my friends and my parents for always being there for me.

iv Contents Abstract Sammanfattning Acknowledgements List of Figures List of Tables Abbreviations i ii iii vii x x 1 Introduction 1 1.1 Background................................. 1 1.2 Overall Objectives............................. 1 1.2.1 Related Work............................ 1 1.2.2 Project Goals............................ 2 1.2.3 Objectives.............................. 2 1.3 Approach & Contents of the Report................... 2 2 Theory and Definitions 3 2.1 Lifetime................................... 3 2.2 State of Charge............................... 3 2.3 Turnaround Event............................. 3 2.4 C-rate.................................... 3 2.5 Failure Mode and Effects Analysis.................... 4 2.6 Reliability Centered Asset Management................. 4 3 Technical Background 5 3.1 Lithium Ion Battery Chemistry...................... 5 3.1.1 Intercalation............................ 6

v 3.1.2 Solid Electrolyte Interphase.................... 7 3.2 Aging of Batteries............................. 7 3.3 Battery Cell Manufacturing........................ 8 3.4 Battery Pack Design............................ 8 3.5 Battery System Design........................... 9 3.6 Battery Management System....................... 10 4 Literature Review on the Reliability of Lithium Ion Batteries 12 4.1 Research Question............................. 12 4.2 Search strategy............................... 12 4.3 Filtering................................... 13 4.4 Results.................................... 13 4.5 Fault Tree.................................. 16 4.6 Analysis Limitations............................ 17 5 Proposed Method 18 5.1 Important Factors............................. 18 5.2 Model Design................................ 19 5.2.1 Battery Model........................... 19 5.2.2 Charging.............................. 20 5.2.3 Discharging............................. 20 5.2.4 Battery Current.......................... 20 5.3 Simulation Time.............................. 21 6 Case Study 22 6.1 Model and Chosen Tools.......................... 22 6.2 Chosen Vehicle............................... 24 6.2.1 Battery Pack............................ 24 6.2.2 Turnaround Event......................... 25 6.2.3 Summary of Vehicle Parameters.................. 25 6.3 Charging Strategies............................. 25 6.3.1 Slow Charging........................... 25 Battery swapping.......................... 26 6.3.2 Fast charging............................ 27 6.3.3 Induction charging......................... 27

vi 6.3.4 Summary of Charging Strategies................. 28 6.4 Temperature Variations.......................... 28 6.5 Generic Battery Model........................... 29 6.6 Results.................................... 31 6.6.1 Scenario 1: 20 C.......................... 31 Slow Charging........................... 31 Fast Charging............................ 32 Induction Charging......................... 33 6.6.2 Scenario 2: 30 C.......................... 34 Slow Charging........................... 35 Fast Charging............................ 36 Induction Charging......................... 37 6.6.3 Scenario 2: 40 C.......................... 38 Slow Charging........................... 38 Fast Charging............................ 39 Induction Charging......................... 40 6.6.4 Summary of Results........................ 41 6.6.5 Validation of Results........................ 41 6.7 Discussion.................................. 41 7 Closure 45 7.1 Conclusions................................. 45 7.2 Future Work................................ 45 References 46 A Appendix A, SIMULINK Images 50 A.1 SIMULINK Images............................. 50 B Appendix B, MATLAB Code 53 B.1 Current Function/MATLAB Function.................. 53 B.2 Plot Function................................ 53 C Appendix C, Battery Model Input Parameters 60

vii List of Figures 3.1 Illustration of the components of a LIB.................. 6 3.2 Two-dimensional intercalation, with the active material shown as grey tiles and lithium ions as orange circles................... 7 3.3 Battery cell roll showing how the separator, anode and cathode are distributed.................................. 8 3.4 A battery cell with a current going from negative electrode to positive electrode................................... 8 3.5 Several battery cells assembled into a battery pack............ 9 3.6 Design overview containing several parts that in conjunction forms a BESS, design adapted from [15]...................... 10 4.1 Cause tree of the fault mechanism of high temperature explained in [24]. 13 4.2 Cause tree of the fault mechanism of over-discharging studied by [25]. 14 4.3 Cause tree of the fault mechanism of low temperature, studied by [26]. 14 4.4 Cause tree of the fault mechanism of copper current collector dissolution caused by over-charging, results of over-charging found by [26], additional effects explained in [25]..................... 15 4.5 Cause tree of the fault mechanism of high temperatures, studied by [30] and [31].................................... 16 4.6 Fault tree based on the literature review on LIBs............. 16 5.1 A flow chart of the proposed method for examining the aging of a battery. 19 5.2 Battery cell connection to a current source................ 21 6.1 The top layer of the Simulink model made for this case study...... 23 6.2 Physical capacity of a battery pack compared to the usable capacity during normal operation of an EV..................... 24 6.3 Flow chart of one turnaround event for the mail truck at Stockholm Arlanda Airport............................... 25 6.4 Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the slow charging strategy, with time in hours on the x-axis................................. 26

viii 6.5 Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the fast charging strategy, with time in hours on the x-axis................................. 27 6.6 Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the induction charging strategy, with time in hours on the x-axis.............................. 28 6.7 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during slow charging with a temperature of 20 C.. 31 6.8 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 20 C, with time in hours on the x-axis................. 32 6.9 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 20 C.. 32 6.10 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 20 C, with time in hours on the x-axis................. 33 6.11 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during induction charging with a temperature of 20 C.. 33 6.12 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 20 C, with time in hours on the x-axis.......... 34 6.13 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during slow charging with a temperature of 30 C.. 35 6.14 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 30 C, with time in hours on the x-axis................. 35 6.15 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 30 C.. 36 6.16 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 30 C, with time in hours on the x-axis................. 36 6.17 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during induction charging with a temperature of 30 C.. 37 6.18 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 30 C, with time in hours on the x-axis.......... 37 6.19 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during slow charging with a temperature of 40 C.. 38 6.20 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 40 C, with time in hours on the x-axis................. 39 6.21 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 40 C.. 39

ix 6.22 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 40 C, with time in hours on the x-axis................. 40 6.23 Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during induction charging with a temperature of 40 C.. 40 6.24 Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 40 C, with time in hours on the x-axis.......... 41 6.25 Current into battery cell and SOC of battery cell during fast charging in the simulated scenario.......................... 42 6.26 Current into battery cell and SOC of battery cell during fast charging in a possible real scenario.......................... 43 A.1 The Battery Pack & Useage Parameters layer of the SIMULINK model used in the case study............................ 50 A.2 The Energy Limit Block layer of the SIMULINK model used in the case study..................................... 50 A.3 The Charge Strategy Selection layer of the SIMULINK model used in the case study................................ 51 A.4 The Current Function layer of the SIMULINK model used in the case study..................................... 51 A.5 The Slow Charging layer of the SIMULINK model used in the case study. 51 A.6 The Fast Charging layer of the SIMULINK model used in the case study. 52 A.7 The Induction Charging layer of the SIMULINK model used in the case study..................................... 52 C.1 The Simulink battery model input parameters used in the case study.. 60 C.2 The Simulink battery model input parameters on discharge used in the case study................................... 61 C.3 The Simulink battery model input parameters on temperature used in the case study................................ 62 C.4 The Simulink battery model input parameters on aging used in the case study..................................... 63

x List of Tables 5.1 The output parameters necessary from the battery model in order to examine the lifetime............................. 19 6.1 Turnaround event input parameter values and EV battery pack capacity. 25 6.2 Input parameters used for each charging strategy............. 29 6.3 Input parameters for the battery cell used in the case study....... 30 6.4 Summary of TTEOL of the battery pack for all scenarios........ 41

xi List of Abbreviations BESS BMS EDS EES EOL EV FMEA FTA LFP Li-ion LIB Renewable Energy Sources RCAM SCADA SEI SOC Battery Energy Storage System Battery Management System Electricity Distribution System Electrical Energy Storage End Of Life Electric Vehicle Failure Mode Effects Analysis Fault Tree Analysis Lithium Ferro Phosphate Lithium-ion Lithium Ion Battery RES Reliability Centered Asset Management Supervisory Control And Data Acquisition Solid Electrolyte Interphase State Of Charge

1 Chapter 1 Introduction 1.1 Background Global warming is one of the greatest challenges the humanity has ever faced, and the core of the issue is reducing the CO 2 emissions in the near future. The road transport sector is currently contributing to 20 % of the EU s CO 2 emissions [1]. There is therefore a grave need for means of road transportation that is environmentally friendly. One of the most promising technologies to reduce CO 2 emissions produced by the transport sectors are Electric Vehicles (EVs). These are vehicles that replace the combustion engine with an electrical motor. EVs continue to be an interesting area of study as the transport sector and the Electrical Distribution System (EDS) needs to adapt to the changes that a high share of EVs in the transport fleet introduces. One of the most important issues in the oncoming transition to EVs is battery technology, as the cost and capacity of the battery will determine the popularity of EVs in the coming years [2]. Even though EVs are more expensive now, EVs are projected to be cheaper than diesel vehicles before the year 2025 [3]. Due to their high specific energy and energy density the most common batteries in EVs are Lithium-Ion Batteries (LIB) [4]. Another area of the transport sector that is in need of reducing their emissions is the flight industry [5]. Since the EU has set a goal of reducing the CO 2 emissions of flying by 75 % per kilometer and aims to make taxiing of flights emission free by 2050 [6], research has to be put into reducing emissions where ever possible. One area of improvement is the vehicles used for airport ground operations, where many combustion engine vehicles may be replaced by EVs in order to reach the EU s goals. 1.2 Overall Objectives 1.2.1 Related Work This project has been part of the Reliability Centered Asset Management (RCAM) research group at KTH Royal Institute of Technology 1 and the new research team on Electrified Transportation (ET) referred to as RCAM/ET led by Professor Lina Bertling Tjernberg. The overall long term goal of this project is to contribute with new methods for infrastructure asset management focusing on the new developments of the infrastructure for energy, e.g. the electric power system, and electrified transportation. 1 Founded by Lina Bertling based on the methodology of Reliability Centered Asset Management (2002).

Chapter 1. Introduction 2 Infrastructure asset management can be expressed as the combination of management, financial, economic, and engineering, applied to physical assets with the objective of providing the required level of service in the most cost-effective manner. It includes management of the whole life cycle of a physical asset from design, construction, commission, operation, maintenance, modification, decommissioning, and disposal. It covers budget issues and focuses on asset management of the electric power system [7]. The overall objectives of the research are to; contribute to a secure and high level of reliability in electricity supply, an efficient use of energy resources and to the reduction of the use of fossil fuels. During the project time five related projects have been performed; two focusing on battery storage, two on life cycle cost of ground transportation, and one on electrified airplanes. The latter is co-supervised by industrial PhD student Andreas Johansson at SAAB. There has been a reference group related to the project on battery storage with members from ABB, Vattenfall and battery researchers at KTH and Uppsala. The project has included a study visit at E-ways learning about new technology for electrical roads. 1.2.2 Project Goals The goals of this project is to contribute to finding solutions for a sustainable society. This thesis is focused on the developments for increased energy efficiency to reduce CO 2 emissions. To reach these goals batteries in EVs are analyzed, and the analysis is focused on the impact of charging on the lifetime of LIBs. 1.2.3 Objectives This thesis aims to give the reader an understanding of LIBs, their composition and use cases, and to answer the following questions: What factors affect the aging of LIBs and how? What method of charging is most beneficial in order to reduce aging? 1.3 Approach & Contents of the Report The first part of this thesis aims to give an understanding of LIBs, their composition, uses and fault mechanisms. This is done through initial research on batteries in general, following this is a literature study. The results of the literature study results are presented in a fault tree made using a Failure Mode and Effects Analysis (FMEA). Then an analysis of the different ways of charging LIBs will be performed, using a case study of ground vehicles on Arlanda airport in Sweden. The case study will compare the impact of different charging strategies on the aging of the batteries. In the analysis, several factors impact the aging, some of these factors, mainly the ambient temperature, are explored in detail.

3 Chapter 2 Theory and Definitions This chapter explains and defines expressions and concepts that are key to understanding this thesis. The definitions made in this section are specific to this thesis and may differ from other studies in the same area of research. 2.1 Lifetime The lifetime of a battery is defined as the time it takes for the battery to reach 80 % of it s original capacity. 2.2 State of Charge The State of Charge (SOC) of battery is the percentage of energy contained in the battery compared to the possible maximum energy capacity of the battery at that moment. 2.3 Turnaround Event A turnaround event (TE) is an event in which a vehicle performs an action, such as transporting people from one place to another, and is complete when the vehicle is back at its original position [8]. 2.4 C-rate The C-rate of a battery is the rate of which a battery can be charged or discharged. The C-rate of a battery is described in the unit 1/h. For example, a C-rate of 1 would mean that the entire battery is charged or discharged in one hour and C-rate of 2 would refer to a charge or discharge time of 30 min. The equation for C-rate is shown in Equation 2.1, where E Battery is the energy of the battery and P Charge is the power used to charge the battery. C-rate = P Charge E Battery [1/h] (2.1)

Chapter 2. Theory and Definitions 4 The C-rate for charging will be denoted as C rate Charge and the C-rate for discharging as C rate Discharge. 2.5 Failure Mode and Effects Analysis In order to examine the critical parts of a li-ion pack a Failure Mode and Effects Analysis (FMEA) is presented in this thesis. An FMEA is a method developed in the 1960s to detect failure in a system and the causes of said failure. Risk analysis together with prioritization and corrective actions is also included in an FMEA. This FMEA will identify in which way the different battery technologies may fail and how to increase the reliability of these systems [9]. A failure mode is how something could fail, and the effect is the consequence of that failure. An FMEA is a tool that together with other tools can be used to perform an Reliability Centered Asset Management (RCAM) analysis. The seven steps for making an FMEA presented in [7] are: 1. Define the system boundaries, identify functions, expected performance and define failures. 2. Identify failure modes for each function in the system. 3. Identify the consequences of the failure modes on the system. 4. Determine how serious each effect is and rank them in order to find the most critical component. 5. Determine the root causes of each failure mode. 6. Identify detection methods for each failure mode. 7. Identify actions that can reduce the severity of the consequences. 2.6 Reliability Centered Asset Management A Reliability Centered Asset Management (RCAM) is a method to reduce the maintenance cost of an asset by using statistical models to predict and prevent failure in critical components. By using preventive maintenance the critical component may be repaired before failure occurs. The goal of implementing this preventive maintenance is to reduce the cost by reducing the number of interruptions or the duration of the interruptions. The intent of RCAM is also to make decisions that maximize profits in the long term [10]. The method of RCAM will be used, along with FMEA, to better understand how a battery pack should be maintained to provide reliable and secure service.

5 Chapter 3 Technical Background This chapter of the thesis will introduce LIBs, their technical backgound, how they are made and how they fit into a larger battery system. 3.1 Lithium Ion Battery Chemistry Batteries can provide electricity using different materials and configurations. The LIBs are comprised of an electrolyte containing lithium ions, electrodes, binder, current collectors and a separator. During charging, the cathode in the battery is positively charged and the anode is negatively charged. The materials in the anode and cathode are called active materials, these materials are bound together with a binder. The anode and cathode are coated in a material that conducts electricity, often aluminum on the cathode and copper on the anode. Between the cathode and anode is a separator that prevents a short circuit. An electrolyte is then added which allows lithium ions to flow from anode to cathode. When the battery is charged, lithium ions flow from the cathode to the anode, creating a current from the anode to the cathode [11]. This is shown in Figure 3.1.

Chapter 3. Technical Background 6 Figure 3.1: Illustration of the components of a LIB. While all LIBs store and release energy by transferring lithium ions from anode to cathode and back, the materials of the anode and cathode impact the characteristics of the battery. The active materials are often summed up in acronyms of the chemicals on the anode and cathode. For example, an LFP LIB contains lithium (Li), iron (Fe) and Phosphate (Po), and an NMC battery contains nickel (Ni), manganese (Mn) and cobalt (Co) [12]. 3.1.1 Intercalation The li-ions are bound in the electrodes through a process called intercalation. Intercalation is a reversible process in which ions or molecules are introduced into layers or holes in the electrode. This happens during the charging and discharging of the battery, where lithium-ions move between electrodes. The process can happen in either one, two or three-dimensions [13]. A two-dimensional intercalation is illustrated in Figure 3.2, where the orange circles are ions that are intercalated in an electrode, illustrated by grey blocks that the ions can move around in.

Chapter 3. Technical Background 7 Figure 3.2: Two-dimensional intercalation, with the active material shown as grey tiles and lithium ions as orange circles. 3.1.2 Solid Electrolyte Interphase A solid electrolyte interphase (SEI) is a film created on the negative electrode of the battery. The film is created during the first cycle of the battery and increases as it goes through more cycles. As the SEI grows, the internal resistance of the battery will increase, more energy will be dissipated as heat in the battery and it loses capacity [13]. 3.2 Aging of Batteries The aging of a battery depends on many factors, some of the most critical being the active materials chosen and the stability of the SEI. The active materials can transform into inactive materials, this will lessen the amount of li-ions that can be intercalated in the anode and cathode, which in turn reduces the capacity of the battery. When charging the battery, a large range between the upper and lower SOC limits will increase the internal resistance of the battery. Another factor that will impact the aging during charging is the temperature of the battery. These aging mechanisms will reduce the maximum capacity of the battery, requiring more frequent charging [14]. The aging of a battery can be categorized into two distinct areas, when the battery is being used and goes through cycles and when it is in storage and not being used [14]. This report will focus on the aging caused by cycling, as this is affected by the different charging strategies available for EVs.

Chapter 3. Technical Background 8 3.3 Battery Cell Manufacturing The anode, cathode, separator, electrode and current collectors are then packed into rolls or rectangular prisms, with the anode and cathode separated to avoid a shortcircuit. A roll structure is shown in Figure 3.3. When the anode and cathode are rolled up, it is encased in metal or plastic, with exhausts that can release gas if the pressure changes or malfunctions cause gas to be produced in the cell [11]. Figure 3.3: Battery cell roll showing how the separator, anode and cathode are distributed. 3.4 Battery Pack Design Once the battery cell is made, it has to be assembled into a battery pack in order to provide enough power to supply an EV. The battery cells are connected together either by soldering or mechanical means of fastening them together [11]. Two parameters that should be considered when choosing battery cell for the pack is the voltage and current of the battery cell. Figure 3.4 shows the current and voltage of a battery cell. Figure 3.4: A battery cell with a current going from negative electrode to positive electrode. Since most cells can not reach the desired voltage by themselves, they have to be connected in series until the desired voltage is achieved. In addition, the batteries have to be connected in parallel in order to reach the desired current. An example of this configuration is shown in Figure 3.5.

Chapter 3. Technical Background 9 Figure 3.5: Several battery cells assembled into a battery pack. The voltage and current of the battery pack are calculated according to the following formulas: U cell = N series U pack [V] (3.1) I cell = N series I pack [A] (3.2) 3.5 Battery System Design Once the battery pack is assembled, the next step is to connect the pack to the other components, forming a Battery Energy Storage System (BESS). Aside from the battery pack, other components have to be included in the battery system in order to allow for safe operation [15]. These components, shown in Figure 3.6 are all part of the BESS.

Chapter 3. Technical Background 10 Figure 3.6: Design overview containing several parts that in conjunction forms a BESS, design adapted from [15]. The battery pack has to be connected to the EV via power electronics that convert the direct current of the batteries to a current that can drive the electric motor. The battery contains the battery pack, the battery control and monitoring that measures voltages, temperatures and currents and protects the battery pack from harmful conditions. The thermal management controls ventilation and other functions that make sure that the temperature inside the battery is within operating limits. The power electronics contain all equipment needed for the necessary current and voltage conversion. The power electronics need to convert the currents and voltages that the battery pack outputs into values appropriate for the electric motor that the BESS is connected to. The system operation contains computers that control and monitor the battery pack, in addition to handling thermal management and communication with systems outside the BESS [15]. 3.6 Battery Management System The Battery Management System (BMS) is a system of controllers that manages how the batteries are charged and discharged in order to ensure the longevity and safety of the batteries. While there are many different ways of categorizing what is included in the BMS, this section will concern the Battery Control & Monitoring in Fig. 3.6. The BMS protects the batteries using computer software, a controller and sensors that measure current, voltage and temperature and any other values of importance. The software and controller are programmed to make sure the battery is operating within safe conditions and collects data about the batteries. A BMS is programmed to

Chapter 3. Technical Background 11 protect against overcharging, overdischarging, high temperatures, low temperatures, short circuiting and other ways the battery may get harmed [11]. One of the responsibilities of the BMS is to balance the SOC of the battery cells. Even if all cells in a battery are of the same type, differences in SOC arising from manufacturing irregularities will cause the battery cells to have small differences in capacity. These differences will increase as the battery cycles and may cause the system to fail if these differences are not reduced. Batteries connected in parallel will automatically balance their SOC to be the same across the entire parallel coupled branch, but batteries connected in series will not. Due to this, control algorithms and power electronics has to be employed in order to balance the SOC of the batteries [16].

12 Chapter 4 Literature Review on the Reliability of Lithium Ion Batteries A review on available literature on LIBs was made in order to make a state-of-art on the reliability performance of LIBs. The results are presented in a fault tree. The articles found in this review were summarized and cause trees were made if the articles presented a relevant cause for a fault. All relevant cause trees were then consolidated into a fault tree. The fault tree is used in this thesis to give further understanding of the reliability performance of LIBs. The fault tree was designed using the FMEA tool presented in Section 2.5 and [7]. 4.1 Research Question The research question for this literature review is: Under what conditions do lithium-ion batteries fail to operate, and what are the reasons for these conditions? In order to determine if the articles found in the literature search could answer the research question, two sub-questions were made. These questions gives way to decide whether or not to include the articles found in the review. The questions are formulated in a way that the answer has to be "yes" on both of them in order to include the examined article. The questions are: Does the article address factors that may cause a LIB system to fail? Are the results presented in the article applicable on a large scale LIB system? 4.2 Search strategy The literature search was done in KTH Primo, a search engine that pulls articles from Science Direct, Scopus and IEEE and other databases. Only articles in English were included in the search. The search looked for articles with "li-ion batter*" or "lithium ion batter*" in the title. In addition, the words failure or fault also had to be present in the title. Before this search was decided upon, searches for the words "storage", "reliability" and "FMEA" were done, but without satisfactory results. Due to time constraints, only articles from years 2017 and 2018 were included.

Chapter 4. Literature Review on the Reliability of Lithium Ion Batteries 13 4.3 Filtering There were six articles that were removed from the review due to a "no" answer on one or more of the questions mentioned in Section 4.1. Three of these ([17], [18], [19]) were about how to predict when a battery would fail using statistical methods. The other three articles were excluded due to not being relevant in the context of large scale storage ([20], [21], [22]). [23] was excluded in the second read-through. 4.4 Results This section summarizes all the included articles and presents a cause tree if the article contains a relevant fault cause. [24] presents a way to analyze the safety of LIBs using rheology-mutation theory and Fault Tree Analysis (FTA). This analysis is focused on explosions of LIBs, other types of failure are not presented. The FTA is most relevant to this review as it presents many instances of LIB failure. The article identifies three categories of failure that cause an explosion: high temperature, internal short circuit and external short circuit. The article also mentions that under-voltage and over-discharge may result in lithium plating. [24] explains the fault mechanism of high temperatures as when the batteries reach a temperature higher than 70 C, the Solid Electrolyte Interface (SEI) starts to decompose, releasing more energy. This causes a thermal runaway that will cause the heat to further increase. At 100 C the intercalated lithium reacts with the electrolyte, releasing more energy until 130 C when the battery explodes. Figure 4.1: Cause tree of the fault mechanism of high temperature explained in [24]. [25] studies the fault mechanisms of LIBs during over-discharge. The paper states that over-discharge may lead to electrode material destruction, over-discharge mainly

Chapter 4. Literature Review on the Reliability of Lithium Ion Batteries 14 results in a loss of active material at the anode and a growth of the SEI, increasing the internal resistance of the battery. To analyze over-discharge batteries were first charged to full, then discharged to 100 %. When the batteries were fully discharged, they were discharged by an additional 10 % and 20 % depth of discharge. This causes accelerated capacity loss, failure and in some cases the batteries caught on fire. This fault mechanism is shown in Figure 4.2. Figure 4.2: Cause tree of the fault mechanism of over-discharging studied by [25]. Low temperature could cause lithium plating [26], which is the creation of metallic lithium on the anode, reducing its surface area and may lead to the separator being pierced [27]. This fault mechanism is shown in Figure 4.3. Figure 4.3: Cause tree of the fault mechanism of low temperature, studied by [26]. The same article also mentions that over-discharge may cause the copper current collector to dissolve, oxidizing Cu into Cu 2. These ions may penetrate the separator and cause a short circuit [25]. This fault mechanism is shown in Figure 4.4.

Chapter 4. Literature Review on the Reliability of Lithium Ion Batteries 15 Figure 4.4: Cause tree of the fault mechanism of copper current collector dissolution caused by over-charging, results of over-charging found by [26], additional effects explained in [25]. [28] mentions the failure causes as over-charge, over-discharge, internal short circuit and external short circuit. The short circuit errors are more likely to cause a thermal runaway, which is defined as when the solid electrolyte interface begins to decompose, which occurs at around 90 C. [29] proposes a system that performs fault diagnosis for LIBs in electric vehicles. The report defines the failure causes as over-charge, over-discharge, over-current and over-temperature. The system issues a warning at 50 C, and stops the charging or discharging if the batteries reach 60 C or more. The system also contains functions that stops charging or discharging if the voltage goes beyond pre-determined limits. [30] analyses the swelling of LIBs caused by formation of gas caused by electrolyte evaporation. This swelling can separate the materials in the electrode and removing contact between materials, causing safety issues. The swelling was present in a battery which had been unused in storage for two years and then used for one year. The effect of high temperatures on LIBs was examined by [31]. It was found that exposing cells to temperatures of 90 C and 100 C caused swelling. The swelling is caused by gas formation that together with other chemical processes occurring at these temperatures reduces battery capacity. Applying a compressive force to the cells increases pressure inside the cells and reduces gas formations, thereby reducing capacity loss. The fault mechanism of swelling is shown in Figure 4.5.

Chapter 4. Literature Review on the Reliability of Lithium Ion Batteries 16 Figure 4.5: Cause tree of the fault mechanism of high temperatures, studied by [30] and [31]. 4.5 Fault Tree Figure 4.6 presents a fault tree containing the cause trees and expands upon them in order to give an overview of fault mechanisms of LIBs. The purpose of this fault tree is to give an overview of the faults associated with LIBs. The tree is split into four levels. The highest level is the function level, which explains the function that will be lost in the event of a fault. The function is to provide sufficient energy and power. This function will be impaired if the battery is short circuited, physically damaged or if the capacity of the battery is too low. The next level is the failure mode level, which contains the different kinds of modes that lead to a loss of function. The third level, the failure event level, describes the events that connect the failure mode to the failure cause. The failure cause, the lowest level, contains the causes that ultimately lead to a loss of function. The failure causes are external actions that are within control of the battery operator or time-dependent factors. The fault tree does not include mechanical damage due to this being deemed not relevant for the case study of this thesis. Figure 4.6: Fault tree based on the literature review on LIBs.

Chapter 4. Literature Review on the Reliability of Lithium Ion Batteries 17 4.6 Analysis Limitations Making an FTA of LIBs is a large task due to the many different materials used and the multitude of application areas. This review aims to make a fault tree that is easy to understand, but this will cause it to loose some complexity. This fault tree does not contain information about the specific materials used in LIBs. The active material on both the positive and negative electrodes can differ depending on the application, which in turn affects the performance of the battery. This would presumably change how the batteries fail, but few articles in this review mentioned what kind of active materials were used in the battery examined. This could possibly be because the active materials used may not affect the failure cause to a large extent, although this should be examined further before drawing any conclusions. This review is limited to the faults in LIBs found by the search described in section 4.2. This search was limited 2017 and 2018, which in turn will reduce the width of the results. Grey literature was not included in this review, further limiting the results. Making a fault tree of LIBs is complicated and needs more time and effort in order to be entirely comprehensive. Since some failure cause reactions are exothermic and produce heat [28], these faults cannot be singled out into just one failure cause, they will be a combination of high temperature and the original failure cause. Making a fault tree that takes all reactions into account is a complex task that did not fit into the time-frame of this review. Since this review does not take a quantitative approach to examining the failure causes of LIBs, no conclusion about the likelihood of the different failure causes is made.

18 Chapter 5 Proposed Method This chapter proposes a method for analyzing the lifetime of a LIB, it goes through what factors that are deemed important for this analysis and how a model for examining the lifetime can be designed. 5.1 Important Factors In order to investigate the lifetime of a battery, it is important to identify the factors that impact the aging and the ways of measuring the age of the battery. [32] outlines factors that affect the lifetime in batteries as: Temperature State of Charge Depth of Discharge Electric Current In order to take these factors into account, they need to be translated into variables that fit the application area. Three different ambient Temperatures are defined for each scenario and their effect on battery aging are investigated. State of Charge is the percentage of energy contained in the battery compared to the possible maximum capacity of the battery at that moment. Depth of Discharge is how deep the charging range is, in other words, how many percentage points there are between the maximum SOC and the minimum SOC. The State of Charge and Depth of Discharge factors were included by defining upper and lower limits of SOC for the battery for each charging strategy. This is because the range of SOC that the battery is operating in can significantly affect the aging of the battery [33]. The Electric Current depends on the size of the battery that is being investigated, and the power of the charging and discharging. The factors that affect charging current is explained in Section 6.2.1 about the battery pack of the vehicle and 5.2.2 about the different charging strategies. The discharging is affected by the way the battery is used, in this case how the vehicle is driven, for how long and with what power consumption, this is explained in Section 6.2.2. These factors will degrade the performance of the battery, [32] describes that the degradation can be measured in: Capacity Internal Resistance

Chapter 5. Proposed Method 19 The capacity loss of the battery is the main degradation factor that will be investigated in this thesis. 5.2 Model Design In order to examine how the capacity loss is affected by different charging strategies and the temperature, a proposed method is shown in Figure 5.1. Figure 5.1: A flow chart of the proposed method for examining the aging of a battery. The desired input parameters are inserted into the charging/discharging function and the battery model. The model then runs through one loop, the charging/discharging function determines the current I Battery that goes into the battery, the battery then reacts to the current, and outputs parameters such as the battery s SOC, which is used as input to the charging/discharging function to determine whether the current should change. Since this model s purpose is to examine the lifetime of a battery, several loops from battery model to charging/discharging and back to the battery model are necessary in order to simulate several years. 5.2.1 Battery Model The battery model contains the battery which is to be analyzed. This battery model needs to be able to react to different currents and ambient temperatures. The input parameters of the battery model depends on the simulation program in which the model is made in. The output parameters necessary from the battery model in order to analyze the lifetime are presented in Table 5.1. Table 5.1: The output parameters necessary from the battery model in order to examine the lifetime. Parameter Description Unit V Battery Voltage of the battery. V I Battery Current into the battery. A E MaxBattery Maximum capacity of the battery. A h SOC Battery SOC of the battery. % T Battery Temperature of the battery. C

Chapter 5. Proposed Method 20 5.2.2 Charging EVs are charged using different kinds of charging adapters connected to the EDS. The power of these chargers can range from anywhere between 2.8 kw [34] to to 72 kw [35]. The power depends on the intended purpose of the charger and how much power the EDS can output at the location of the charger. The C-rate when charging in this model will be determined by the power of the charger P Charge divided by the capacity of the battery pack E BatteryP ack. C Rate Charge = P Charge E BatteryP ack [1/h] (5.1) The model was made based on an aging model that charges the battery cell from a lower limit SOC to a higher limit SOC for 1000 h [36]. This means that the battery is always either charging or discharging, there is always a charge or discharge current going through the battery. Thus functions of a BMS such as overcharge and undercharge protection described in Section 3.6 are implemented in the model. 5.2.3 Discharging The discharging of the battery is determined by the energy consumption of the vehicle and the total energy is the battery pack E BatteryP ack. The energy consumption of the vehicle is defined by how much energy each Turnaround Event (TE) consumes (E T E ) multiplied by how many turnaround events one vehicle can perform in one hour N T E. A TE is an event in which a vehicle performs an action, such as transporting people from one place to another, and is complete when the vehicle is back at its original position. P Discharge = E T E N T E [kw] (5.2) This gives the power consumption P Discharge of the vehicle per hour, which will be used to find the C-rate when discharging of the battery using the following formula. C Rate Discharge = P Discharge E BatteryP ack [1/h] (5.3) 5.2.4 Battery Current The battery current I Battery is changed depending on if the battery needs to be charged or discharged. The capacity of the battery in the battery model is denoted as E Battery. If the current is defined as going from negative electrode to positive electrode as shown in Figure 5.2, the charging current is negative and the discharging current is positive.

Chapter 5. Proposed Method 21 Figure 5.2: Battery cell connection to a current source. How the charging and discharging currents are calculated is shown in Equation 5.4 and 5.5. I BatteryCharge = C Rate Charge E Battery [A] (5.4) I BatteryDischarge = C Rate Discharge E Battery [A] (5.5) 5.3 Simulation Time Since the purpose of this model is to compare how charging strategies affect battery capacity loss, it is important to make sure that the power output of the battery is consistent across charging strategies. In other words, the battery is used the same way in all scenarios. This is not the case if the time simulated is the same for all charging strategies. In the case of fast charging, the downtime of the battery is less than in the case of battery swapping, thus fitting in more battery cycles in for same simulated time of fast charging compared to slow charging. In order to make the comparison fair across all three scenarios, the power output has to be the same for all three charging strategies. This is accomplished by integrating the output power P Out of the battery and stopping the simulation at t end when the total output power reaches the power limit E T otal decided by the amount of work days N Days and turnaround events per hour E T E N T E. The integration is shown in Equation 5.6. tend 0 P out dt = E T E N T E 24 N Days = E T otal [kw h] (5.6) When the simulation stops, N Days with N T E number of turnaround events per hour has passed. If the time t from 0 to t end is assumed to increase consistently, this time can be used together with the battery model output to calculate the EOL of the battery.

22 Chapter 6 Case Study The second part of this thesis is a case study of Stockholm Arlanda Airport, Sweden. Currently the ground vehicles on the airport are propelled by gasoline, the study aims to examine charging strategies for a ground vehicle if this vehicle was fully electric. The aim of the case study is to answer the following questions: How do different charging strategies affect battery aging? What impact does the ambient temperature have on battery aging for different charging strategies? 6.1 Model and Chosen Tools The model was made in Simulink and MATLAB, two integrated applications made by Mathworks. Simulink and MATLAB was chosen because they are powerful tools that provide different toolboxes and simulation blocks for electrical circuitry. Simulink has been used for several projects involving different battery powered systems. [37] used Simulink and MATLAB to model a hybrid vehicle which was used for examining fuel consumption for varying driving patterns and by [38] in connection with a photovoltaic system. The battery model in Simulink was used by [39] and [40] to examine how the battery reacts to different charging patterns. The top layer of the Simulink model is shown in Figure 6.1. All images of the Simulink model and the MATLAB code made for this case study are available in Appendix A and Appendix B.

Chapter 6. Case Study 23 Figure 6.1: The top layer of the Simulink model made for this case study.

Chapter 6. Case Study 24 6.2 Chosen Vehicle The vehicle chosen for this case study is the mail truck that transports mail to and from the airplanes. The current vehicle at Stockholm Arlanda Airport which performs this task is a Volkswagen Amarok [41]. In the case study this vehicle was replaced with the fully electric Renault Kangoo Z.E. 33. 6.2.1 Battery Pack As explained in Section 3.4, the battery cells are arranged in series and parallel to form a battery pack. The total capacity of this pack depends on how many cells are connected together in the pack. The Renault Kangoo Z.E. 33 has an advertised capacity of 33 kwh [42]. This is assumed to be the capacity that the consumer can use freely. To protect the battery from damage the EV manufacturer may have limited the usable battery capacity. This limit is usually 80 % of the physical capacity of the battery pack [43]. Thus this case study defines the usable capacity E P ackusable of the EV battery pack as 80 % of the physical capacity E BatteryP ack, as shown in Figure 6.2. Figure 6.2: Physical capacity of a battery pack compared to the usable capacity during normal operation of an EV. Because of this, the physical battery pack size E BatteryP ack used in this case study is assumed to be 41.25 kwh, as calculated in Equation 6.1. E BatteryP ack = 41.25 = 33/0.8 [kw h] (6.1)

Chapter 6. Case Study 25 6.2.2 Turnaround Event A turnaround event for the mail truck is a 2.5 km drive from the garage to the airplane, where it leaves and picks up mail. The truck then drives back 2.5 km to the garage where it leaves mail and picks up mail that is to be loaded on the next airplane as shown in 6.3. The entire turnaround event takes 25 minutes [41]. The turnaround event cycle is illustrated in Figure 6.3. Figure 6.3: Flow chart of one turnaround event for the mail truck at Stockholm Arlanda Airport. 6.2.3 Summary of Vehicle Parameters A summary of the data presented in Section 6.2 is presented in Table 6.1. Table 6.1: Turnaround event input parameter values and EV battery pack capacity. Parameter Description Value Unit Source E T E Turnaround events per hour 2 h 1 [41] for the mail truck. N T E Energy expended per 1.45 kw [41] turnaround event. E BatteryP ack Physical energy in the EV battery pack. 41.25 kw [42] 6.3 Charging Strategies Three different charging strategies were developed, slow charging, fast charging and induction charging. The following subsections go into detail into each of these strategies. 6.3.1 Slow Charging Many different EV manufacturers offer chargers that are often called home chargers, which charge the EV with a low power. For example BMW offers 22 kw, 11 kw and 7.4 kw home chargers for the BMW i3 [44], the Nissan Leaf home charger outputs 7 kw [45] and the Tesla lists 7.7 kw, 11.5 kw and 17.3 kw as the most common home charging powers [34]. The charger used for this strategy will be called a slow charger

Chapter 6. Case Study 26 in this case study. The power that the Renault Kangoo can be charged with varies from 2.3 kw to 43 kw. In this case study the charger that represents slow charging is set to 7 kw [42]. In Figure 6.4 the charging currents into a single battery cell are shown in conjunction with the SOC limits in the battery cell. A positive current means that the battery is being discharged, and during a negative current the battery is charged. The SOC limits set for slow charging are 10 % and 90 %. As seen in Figure 6.4, the time of charging and discharging are almost equal. Figure 6.4: Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the slow charging strategy, with time in hours on the x-axis. Battery swapping As seen in Figure 6.4, close to a third of the time is spent charging when a slow charging strategy is used. In order to circumvent the issue of having to spend time charging, a technology called "battery swapping" can be used. In battery swapping the vehicle battery is changed when it needs to charge, a battery with low SOC is swapped for a charged battery with high SOC. This allows for the vehicle to continue its task as fast as the battery can be swapped [46]. Using battery swapping reduces the need for the vehicle to be stationary near a charging station, while allowing the battery to be charged using a less powerful charger.

Chapter 6. Case Study 27 6.3.2 Fast charging Fast charging is a way of charging where a large current into the battery charges the battery much faster than slow charging. This charging method is identified by a high current during a short time, usually taking less than one hour. It is a also common to only charge the battery up to 80 % SOC since it takes an excessively longer time to charge the last 20 % [43]. Since this strategy should prioritize speed, the SOC-limits will be defined as from 10 % to 80 % in this strategy. There is no clear definition of fast charging [43], but it is often advertised as charging a large portion of the battery in less than one hour [47], [44], [45]. The power output of the charging stations varies between 43 kw [42],[45],[44] to 72 kw [35]. The Renault Kangoo Van has a maximum charging power of 43 kw [42], thus the fast charging in this case study will be set to P Charge = 43 kw. As seen in Figure 6.5, the time of charging is significantly shorter compared to slow charging. This is mainly because of the higher value of P Charge, but also because the battery is only charged up to 80 % in the fast charging strategy, compared to 90 % in the slow charging strategy. Figure 6.5: Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the fast charging strategy, with time in hours on the x-axis. 6.3.3 Induction charging The third charging strategy is induction charging, a way to wireless power transfer from a coil underneath the ground to the battery. This strategy has many advantages

Chapter 6. Case Study 28 compared to a wired charging; allowing the vehicle to drive around while being charged gives more freedom to the driver, as charging happens passively without having to connect the vehicle to a stationary charger. An induction road has been installed at the Korea Advanced Institute of Science and Technology (KAIST) campus and in Gumi, South Korea. This induction road powers public transport buses and has a 100 kw output power with an efficiency of 80 % [48] [49]. This gives a power output of 80 kw, which is the power that will be used for induction charging in this strategy. During induction charging, the time between each charging instance will be much shorter than during slow and fast charging, this can be seen in Figure 6.6. This is due to the smaller SOC window in which the battery is charged, only charging the battery between 40 % and 60 % means that the battery has to have a higher access to charging compared to the fast charging strategy. Figure 6.6: Current into the battery cell on the left y-axis and SOC of the battery on the right y-axis during the induction charging strategy, with time in hours on the x-axis. 6.3.4 Summary of Charging Strategies In order to get an overview of the differences of the three cases a summary of the charging strategies is presented in Table 6.2. 6.4 Temperature Variations As seen from the fault tree in Section 4.5, temperature has a significant affect on the lifetime of a battery, this is also supported by [32] [50]. Because of this, each charging

Chapter 6. Case Study 29 Table 6.2: Input parameters used for each charging strategy. Charging Strategy P Charge SOC Max SOC Min Slow Charging 7 kw 90 % 10 % Fast Charging 43 kw 80 % 10 % Induction Charging 80 kw 60 % 40 % strategy will be simulated at 20 C, 30 C and 40 C in order to examine the impact of temperature change in conjunction with the different charging strategies. 6.5 Generic Battery Model The generic battery model used in this case study is developed by Mathworks. The model is a single battery cell with a positive and negative electrical connection available. The battery cell also has an input port for ambient temperature and it is possible to output the voltage, current, SOC, cell temperature, age in equivalent full cycles and the cells maximum capacity in real time [51]. The battery model has been compared to real batteries and tested in a simulated EV by [52] and concluded that the battery is an adequate representation of a battery s real behaviour in an EV. The temperature variations output from the simulated battery was investigated by [53], and the results show good agreement between the simulated battery and an experimental battery. The battery model is experimentally validated and has a maximum error of 5 % when the SOC is between 10 % and 100 % for charge 0 through 2 C and discharge 0 through 5 C. The assumptions made by Mathworks when developing this model are [51]: The internal resistance is assumed constant during the charge and discharge cycles and does not vary with the amplitude of the current. The parameters of the model are derived from discharge characteristics and assumed to be the same for charging. The capacity of the battery does not change with the amplitude of current (No Peukert effect). The self-discharge of the battery is not represented. It can be represented by adding a large resistance in parallel with the battery terminals. The battery has no memory effect. Due to these limitations and assumptions, along with the SOC limits explained in Section 6.2.1, extra care has been put into the model to avoid going above 90 % SOC and below 10 % SOC as this will cause the model to behave in unintended ways. The battery cell chosen in this case study is a LiNiO2 battery cell given in the Simulink library. The battery cell resembles the Panasonic G 18650 Battery [54] in terms of voltage and capacity with both having a capacity of 3.6 A h and a voltage of 3.6 V. Panasonic is the supplier for Tesla battery cells [55]. Because of this it is assumed that the LiNiO2 battery cell in the Simulink is suitable for use in an electric vehicle and used for the cases presented in this thesis. Some of the parameters of the chosen battery are presented in Table 6.3, all parameters of the chosen battery are presented in Appendix C.

Chapter 6. Case Study 30 Table 6.3: Input parameters for the battery cell used in the case study. Parameter Description Unit Value V CellNom Nominal voltage of the battery cell. V 3.6 E CellNom Nominal capacity of the battery cell. A h 3.6 E Cell EOL Capacity of the battery cell at EOL. A h 2.88 R Cell Internal Resistance of the battery cell. Ω 0.00889 R Cell EOL Internal Resistance of the battery cell at EOL Ω 0.010668 Because one battery cell has a capacity of 3.6 A h and the battery pack capacity defined in Section 6.2.1 has a capacity of 41.25 kw h, several thousands of battery cells are needed to make one battery pack 1. Since simulating thousands of battery cells requires more computing power than what was available for this case study, one battery cell is simulated and scaled up to represent an entire battery pack. This means that it is assumed that all battery cells in the battery pack behave the same way. 1 This calculation is present in Figure A.2 in Appendix A

Chapter 6. Case Study 31 6.6 Results The results shown is the capacity loss of the battery cell and a graph showing how the charging and discharging currents affect the temperature. The sample of temperature and charging current is taken eight hours into the simulation. The capacity loss is presented in kw h, with a blue line representing the capacity loss and a reference line of 80 % of the original battery pack capacity at 20 C in red. 6.6.1 Scenario 1: 20 C The first scenario simulates the three charging strategies with a temperature of 20 C. Slow Charging Figure 6.7: Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during slow charging with a temperature of 20 C.

Chapter 6. Case Study 32 Figure 6.8: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 20 C, with time in hours on the x-axis. Fast Charging Figure 6.9: Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 20 C.

Chapter 6. Case Study 33 Figure 6.10: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 20 C, with time in hours on the x-axis. Induction Charging Figure 6.11: Graph of capacity loss of a battery pack on the y- axis, and time in years on the x-axis during induction charging with a temperature of 20 C.

Chapter 6. Case Study 34 Figure 6.12: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 20 C, with time in hours on the x-axis. 6.6.2 Scenario 2: 30 C The second scenario simulates the three charging strategies with a temperature of 30 C.

Chapter 6. Case Study 35 Slow Charging Figure 6.13: Graph of capacity loss of a battery pack on the y- axis, and time in years on the x-axis during slow charging with a temperature of 30 C. Figure 6.14: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 30 C, with time in hours on the x-axis.

Chapter 6. Case Study 36 Fast Charging Figure 6.15: Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 30 C. Figure 6.16: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 30 C, with time in hours on the x-axis.

Chapter 6. Case Study 37 Induction Charging Figure 6.17: Graph of capacity loss of a battery pack on the y- axis, and time in years on the x-axis during induction charging with a temperature of 30 C. Figure 6.18: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 30 C, with time in hours on the x-axis.

Chapter 6. Case Study 38 6.6.3 Scenario 2: 40 C The third scenario simulates the three charging strategies with a temperature of 40 C. Slow Charging Figure 6.19: Graph of capacity loss of a battery pack on the y- axis, and time in years on the x-axis during slow charging with a temperature of 40 C.

Chapter 6. Case Study 39 Figure 6.20: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during slow charging with a temperature of 40 C, with time in hours on the x-axis. Fast Charging Figure 6.21: Graph of capacity loss of a battery pack on the y-axis, and time in years on the x-axis during fast charging with a temperature of 40 C.

Chapter 6. Case Study 40 Figure 6.22: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during fast charging with a temperature of 40 C, with time in hours on the x-axis. Induction Charging Figure 6.23: Graph of capacity loss of a battery pack on the y- axis, and time in years on the x-axis during induction charging with a temperature of 40 C.

Chapter 6. Case Study 41 Figure 6.24: Current into the battery cell on the left y-axis and temperature of the battery cell on the right y-axis during induction charging with a temperature of 40 C, with time in hours on the x-axis. 6.6.4 Summary of Results The results are summarized in Table 6.4, where the time to EOL, hereon referred to as the lifetime, of the battery pack is presented for all scenarios. Table 6.4: Summary of TTEOL of the battery pack for all scenarios. Temperature Charging Strategy 20 C 30 C 40 C Slow Charging 8.9 years 7.3 years 6.2 years Fast Charging 7.4 years 6.1 years 5.1 years Induction Charging 9.2 years 7.5 years 6.2 years 6.6.5 Validation of Results A similar case study was made by [56], also using the generic battery model in MAT- LAB/Simulink. The results on lifetime of a 41.25 kw h in [56] for 20 and 40 C are within ±15% of the results presented in Section 6.6.4. 6.7 Discussion This section will discuss the results presented in Section 6.6. The objective questions in Section 6 were:

Chapter 6. Case Study 42 How do different charging strategies affect battery aging? What impact does the ambient temperature have on battery aging for different charging strategies? In order to answer these questions, the case is simplified into a problem that can be solved within the time frame of this thesis, with the equipment available. This means that some aspects of the analysis are not applicable to a real life situation. Since this analysis uses a battery model from Simulink, the results are dependent on this model begin valid. Building a battery model from scratch is outside the scope of this thesis, this process could take several years and requires extensive knowledge of chemistry along with testing equipment for validation of the model [57]. As explained in Section 5.2.2, the battery pack is charged fully when the battery pack goes below the predefined lower SOC limit, and discharged fully once the upper SOC is reached. This is shown in Figure 6.25. Figure 6.25: Current into battery cell and SOC of battery cell during fast charging in the simulated scenario. In a real world scenario, excluding induction charging, the EV can only be charged when it is at the charger. The EV would also presumably perform a TE if it has enough battery charge to perform one TE, even if this means that the charging is interrupted. This means that the charging patterns shown in Figure 6.25 may not be how an EV at Arlanda will charge and discharge. Since the airport has more frequent traffic during the day than during the night [58], the usage of the EV will be more frequent during the day. A mock-up of a possible scenario is shown in Figure 6.26.

Chapter 6. Case Study 43 Figure 6.26: Current into battery cell and SOC of battery cell during fast charging in a possible real scenario. As seen in Figure 6.26, a real scenario may contain breaks where the EV is neither being charged nor driven, since the battery pack is well above 10 % SOC and no turnaround event is in progress. Note that even during this scenario, the EV is capable of performing more than 2 TE per hour. An event not present in the case study is the one shown in the red circle, where the charging of the EV is interrupted by one turnaround event. These sporadic events are not present in the model due to difficulty implementing them. While these events could affect the result of this thesis, they do not affect the upper and lower SOC limits, and the temperature may not change drastically during these events. Since SOC limits and temperature are some of the most important factors when examining aging of batteries [14], one could argue that this assumption should not greatly impact the result of this thesis. The effect of the ambient temperature is apparent in Table 6.4, where a loss of roughly one lifetime year for a temperature increase of 20 C occurred for all three charging strategies. Figure 6.8, 6.10 and 6.12 show that during an ambient temperature of 20 C, the temperature of the battery increases if the battery is charging with a high power, and continues to increase with time in the fast and induction charging scenarios. The temperature is increased by three to four C in the fast and induction charging scenarios, and by less than one C during slow charging. Since temperature control is an important factor for increasing the lifetime of a battery, attention should be focused on reducing the temperature during charging. This could be through the internal thermal control of the EV, where the thermal control system in conjuction BMS make sure the battery temperature is optimal. The other method of reducing the battery temperature is to control the ambient temperature in the garage where the EV is charging. If the temperature in the garage can be kept around 20 C regardless of

Chapter 6. Case Study 44 the outside temperature, the lifetime of the battery may increase while also providing a pleasant work environment for the people working in the garage. Another assumption made in this thesis is that aging due to storage, which in the case of an EV is when the EV is neither being charged nor driven, is neglected. This aging due to storage has been neglected partly because the model in Simulink does not support calendar aging. The reason for why this may not influence the results is because it is assumed that it is in Arlanda s best interest to use the available post trucks as often as possible in order to reduce investment costs, therefore the EV in this case will often be either driving or charging. Shifting the vehicle fleet of Arlanda from combustion to electric engines may prove difficult as the shift will require the EVs to charge at potentially inconvenient times. It is therefore safe to assume that from the perspective of logistics, the charging time should be as short as possible, requiring high power charging. Aside from the aforementioned issues with temperature a high power charging causes, a large amount of high power charging of several EVs will increase the load on the EDS. In order to reduce this load on the EDS, charging patterns for all EVs at the airport has to be optimized for to account for air traffic and EDS load, this is analyzed further in [8] and [41]. Another aspect of choosing the charging strategy is the availability of the technology. Charging stations with slow and fast charging speeds are available for most EVs on the market [44] [45] [47]. The technology for battery swapping still needs development, in order for the technology to become realized, the market needs to decide on a standardized battery pack and a reasonable battery swapping architecture [46]. Induction charging exist in Korea, but require a significant investment into power supply units, inverters and inductive transmitter units that have to be installed under the road [48]. On the basis of the results and research made in this report, the most promising charging strategy could be an amalgamation of the fast charging strategy and the induction charging strategy. During the induction charging strategy proposed in this study, the EVs only need to be charged for 11.1 % of the time. This means that if each vehicle is in the garage at least 11.1 % of the time, the SOC of the battery can be kept within 40 % and 60 %, assuming that the charging power is 80 kw. Performing two TE per hour, with each TE taking 25 minutes to compelete, would mean that the garage time of the EV is 16.67 %. If a charging power of 43 kw and the same SOC limits as induction charging was used, the EV needs to be charged 13.6 % of the time. Thus an optimal charging strategy, considering availability of technology, lifetime of the battery and logistics, may be to use a charging power of 43 kw and SOC limits of 40 % and 60 %. If the BMS can be programmed to only charge the battery when the SOC is near or goes below 40 %, and stop charging when the SOC is above 60 %, no human action except plugging the EV into the charger when it is in the garage is needed.

45 Chapter 7 Closure 7.1 Conclusions The background study and the literature study performed in this thesis concludes that high temperatures, low temperatures and over-discharging are some of the factors that can lead to failure of a LIB. The research following the literature review found that high temperatures is critical factor that may reduce the lifetime of a LIB. The results of the case study on Stockholm Arlanda Airport show that in the Kangoo Z.E., the proposed slow and induction charging strategies result in a longer lifetime than the fast charging strategy for ambient temperatures of 20, 30 and 40 C. An increased ambient temperature reduced the lifetime of the battery for all three charging strategies in the case study. While the results of the case study in this thesis suggest that a lower charging power in conjunction with small range of SOC limits near 50 % SOC may increase the lifetime of a battery pack, the optimal charging power and SOC limits should be investigated further. 7.2 Future Work This thesis examines the shift from combustion engines to EVs at airports and other facilities with similar needs and scale. Although this thesis, [8], [41] and [56] all contain pieces that give insight into the challenges of this shift, future work should focus on using these reports to study how the findings of these reports can be connected. The need for efficient transports across the airport should be weighed with the benefits of a EV battery with a longer lifetime. This report contains assumptions that were made in order to fit the scope of the study, if time and data is available, a study with greater detail and less assumptions can be made. Studies should to be made into how real charging and driving patterns affect EV battery lifetime, and a model that contains a thermal management system and real ambient temperatures at the location of the case study should be developed.

46 References [1] European Commission. Road transport: Reducing CO2 emissions from vehicles. https://ec.europa.eu/clima/policies/transport/vehicles, 2014. [2] Gopalakrishnan Duleep, Huib van Essen, Bettina Kampman, and Max Grünig. Impacts of Electric Vehicles - Assessment of electric vehicle and battery technology. (April), 2011. [3] WSP Sverige AB. ELTRANSPORTER. 2018. [4] Lele Peng, Yue Zhu, Dahong Chen, Rodney S Ruoff, and Guihua Yu. Two- Dimensional Materials for Beyond-Lithium-Ion Batteries. pages 1 21, 2016. [5] European Commission. Reducing emissions from aviation - European Commission. https://ec.europa.eu/clima/policies/transport/aviation_en, 2015. [6] Marek Darecki, Charles Edelstenne, Tom Enders, Emma Fernandez, Peter Hartman, Jean-Paul Herteman, Michael Kerkloh, Ian King, Patrick Ky, Michel Mathieu, Giuseppe Orsi, Gerald Schotman, Colin Smith, and Johann-Dietrich Wörner. Flightpath 2050. Flightpath 2050 Europe s Vision for Aviation, page 28, 2011. [7] Lina Bertling Tjernberg. Infrastructure Asset Management with Power System Applications. CRC Press, Taylor & Francis Group, Boca Raton, FL, first edition, 2018. [8] Filip Jarnehammar. Asset management of electrical transportation systems using alternative charging technologies: case study stockholm arlanda airport. 2018. TRITA-EECS-EX-2018:661. [9] H.-C Liu. FMEA Using Uncertainty Theories and MCDM Methods. [10] L. Bertling, R. Allan, and R. Eriksson. A reliability-centred asset maintenance method for assessing the impact of maintenance in power distribution systems. IEEE Power Engineering Society General Meeting, 2005, 20(1):1 1, 2005. [11] John Warner. Lithium-Ion and Other Cell Chemistries. In The Handbook of Lithium-Ion Battery Pack Design, number Cid, pages 65 89. 2015. [12] Battery University. Bu-205: Types of lithium-ion. https://batteryuniversity. com/learn/article/types_of_lithium_ion. [13] Christian Julien, Alain Mauger, Ashok; Vijh, and Karim Zaghib. Lithium Batteries. 2016. [14] M. Broussely, Ph Biensan, F. Bonhomme, Ph Blanchard, S. Herreyre, K. Nechev, and R. J. Staniewicz. Main aging mechanisms in Li ion batteries. Journal of Power Sources, 146(1-2):90 96, 2005.

REFERENCES 47 [15] Liang Huang and Chang Yao. Failure Prediction Modeling of Lithium Ion Battery toward Distributed Parameter Estimation. Chinese Journal of Chemical Physics, 30(5):547 552, 2017. [16] Damien F. Frost and David A. Howey. Completely Decentralized Active Balancing Battery Management System. IEEE Transactions on Power Electronics, 33(1):729 738, 2018. [17] David E Acuña and Marcos E Orchard. Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring. Mechanical Systems and Signal Processing, 85:827 848, 2017. [18] Stephen J Harris, David J Harris, and Chen Li. Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells. Journal of Power Sources, 342:589 597, 2017. [19] Liang Huang and Chang Yao. Failure Prediction Modeling of Lithium Ion Battery toward Distributed Parameter Estimation. Chinese Journal of Chemical Physics, 30(5):547 552, 2017. [20] J.E. Miller, Frankel Lyons, Eric L. Christiansen, and Dana M. Lear. Failure mechanisms of Ni-H2 and Li-Ion batteries under hypervelocity impacts. Procedia Engineering, 204:239 246, 2017. [21] Saurabh Saxena, Yinjiao Xing, and Michael Pecht. A Unique Failure Mechanism in the Nexus 6P Lithium-Ion Battery. Energies, 11(4):841, 2018. [22] Jae Gil Lee, Jongjung Kim, Jeong Beom Lee, Hosang Park, Hyun-Seung Kim, Ji Heon Ryu, Dong Sub Jung, Eun Kyung Kim, and Seung M Oh. Mechanical Damage of Surface Films and Failure of Nano-Sized Silicon Electrodes in Lithium Ion Batteries. Journal of The Electrochemical Society, 164(1):A6103 A6109, 2017. [23] Taesic Kim, Darshan Makwana, Amit Adhikaree, Jitendra Vagdoda, and Young Lee. Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems. Energies, 11(1):125, 2018. [24] Chuang Qi, Yan Li Zhu, Fei Gao, Song Cen Wang, Kai Yang, and Qing Jie Jiao. Safety analysis of lithium-ion battery by rheology-mutation theory coupling with fault tree method. Journal of Loss Prevention in the Process Industries, 49:603 611, 2017. [25] Wu Chao, Zhu Chunbo, Sun Jinlei, and Jiang Jianhu. Fault mechanism study on Li-ion battery at over-discharge and its diagnosis approach. IET Electrical Systems in Transportation, 7(1):48 54, 2017. [26] Chao Wu, Chunbo Zhu, and Yunwang Ge. A New Fault Diagnosis and Prognosis Technology for High-Power Lithium-Ion Battery. IEEE Transactions on Plasma Science, 45(7):1533 1538, 2017. [27] Christian Von Lüders, Veronika Zinth, Simon V Erhard, Patrick J Osswald, Michael Hofmann, Ralph Gilles, and Andreas Jossen. Lithium plating in lithiumion batteries investigated by voltage relaxation and in situ neutron diffraction. Journal of Power Sources, 342:17 23, 2017.

REFERENCES 48 [28] Xiaoyu Li and Zhenpo Wang. A novel fault diagnosis method for lithium-ion battery packs of electric vehicles. Measurement: Journal of the International Measurement Confederation, 116(October 2017):402 411, 2018. [29] Zuchang Gao, Cheng Siong Chin, Joel Hay King Chiew, Junbo Jia, and Caizhi Zhang. Design and implementation of a smart lithium-ion battery system with real-time fault diagnosis capability for electric vehicles. Energies, 10(10):15 18, 2017. [30] Seon-Hong Lee and In-Hwan Ko. Failure Analysis of Swelling in Prismatic Lithium-Ion Batteries During Their Cycle Life After Long-Term Storage. Journal of Failure Analysis and Prevention, 2018. [31] Yan Zhao, Yatish Patel, Ian A Hunt, Kristina M Kareh, Alexander A Holland, Christian Korte, John P Dear, Yan Yue, and Gregory J Offer. Preventing lithium ion battery failure during high temperatures by externally applied compression. Journal of Energy Storage, 13:296 303, 2017. [32] E. Sarasketa-Zabala, I. Laresgoiti, I. Alava, M. Rivas, I. Villarreal, and F. Blanco. Validation of the methodology for lithium-ion batteries lifetime prognosis. 2013 World Electric Vehicle Symposium and Exhibition, EVS 2014, pages 1 12, 2014. [33] Jens Groot. State-of-health estimation of Li-ion batteries: cycle life test methods. PhD, CHALMERS UNIVERSITY OF TECHNOLOGY, page 138, 2012. [34] Home charging installation. https://www.tesla.com/support/home-charginginstallation, 2018. [35] Bjørn Nyland. Supercharger speed on 90d/p90d has been increased. https://youtu.be/rbfpcnbex5q, 2016. [36] Souleman Njoya M. and Louis-A. Dessaint. 12.8 v, 40 ah, lithium-ion (lifepo4) battery aging model (1000 h simulation). mathworks.com, 2018. [37] Zifan Liu, Andrej Ivanco, and Zoran S Filipi. Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48V Mild Hybrid Vehicle. SAE International Journal of Alternative Powertrains, 5(2):2016 01 1166, 2016. [38] Gourab Majumder and Kaveendra Kumar. Solar Photo-Voltaic Model Using Soft -Switching Techniques For Battery Charging Application. In 2016 INTERNA- TIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNI- CATION, COMPUTER AND OPTIMIZATION TECHNIQUES (ICEECCOT), pages 73 77, 2016. [39] Jarernporn Poonsuk and Surapong Pongyupinpanich. Design and Estimation of State-Charging Applied for Lithium-Ion Battery Based on Matlab-Simulink. pages 176 179, 2016. [40] A S Martyanov, D Korobatov V, and E A Sirotkin. Modeling of Battery Charging Algorithms. In 2016 2ND INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, APPLICATIONS AND MANUFACTURING (ICIEAM), volume 2014, pages 2014 2017, 2016. [41] Hampus Wirén. Asset management of electrical transportation systems with life cycle cost analysis for ground support equipment: Case study stockholm arlanda airport. 2018. TRITA-EECS-EX-2018:664.

REFERENCES 49 [42] Kangoo van brochure july. https://www.cdn.renault.com/content/dam/renault/ UK/brand-and-editorial/Brochures/Vehicles/kangoo-van-brochure-july.pdf, 2018. [43] Abdilbari Shifa Mussa. Durability Aspects of Fast Charging, Mechanical Constraint and Inhomogenity in Lithium-Ion Batteries. PhD thesis, KTH Royal Institute of Technology, 2018. [44] Range and charging in the bmw i3. https://www.bmw.co.uk/bmw-cars/bmw-i/ 2017-bmw-i3/range-charging-efficiency, 2018. [45] Range & charging. https://www.nissan.co.uk/vehicles/new-vehicles/leaf/ range-charging.html, 2018. [46] Sujie Shao, Shaoyong Guo, and Xuesong Qiu. A mobile battery swapping service for electric vehicles based on a battery swapping van. Energies, 10(10), 2017. [47] Supercharger. https://www.tesla.com/supercharger, 2018. [48] Stefan Helber, Justine Broihan, Young Jang, Peter Hecker, and Thomas Feuerle. Location Planning for Dynamic Wireless Charging Systems for Electric Airport Passenger Buses. Energies, 11(2):258, 2018. [49] Jaegue Shin, Seungyong Shin, Yangsu Kim, Seungyoung Ahn, Seokhwan Lee, Guho Jung, Seong Jeub Jeon, and Dong Ho Cho. Design and implementation of shaped magnetic-resonance-based wireless power transfer system for roadwaypowered moving electric vehicles. IEEE Transactions on Industrial Electronics, 61(3):1179 1192, 2014. [50] A. A. Andreev, A. G. Vozmilov, and V. A. Kalmakov. Simulation of lithium battery operation under severe temperature conditions. Procedia Engineering, 129:201 206, 2015. [51] Implement generic battery model. https://se.mathworks.com/help/ physmod/sps/powersys/ref/battery.html, 2018. [52] Olivier Tremblay and Louis A. Dessaint. Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 3(2):289 298, 2009. [53] L. H. Saw, K. Somasundaram, Y. Ye, and A. A.O. Tay. Electro-thermal analysis of Lithium Iron Phosphate battery for electric vehicles. Journal of Power Sources, 249:231 238, 2014. [54] Voltaplex lithium ion battery packs. https://voltaplex.com/panasonic-g-18650- battery-ncr18650g, 2018. [55] Panasonic flags battery cell shortages as tesla model 3 output picks up. https://www.reuters.com/article/us-panasonic-batteries/panasonic-flagsbattery-cell-shortages-as-tesla-model-3-output-picks-up-iduskbn1jo09n, 2018. [56] Julia Damström. Life cycle cost analysis of lithium ion batteries using different charging strategies: a case study on stockholm arlanda airport. 2018. TRITA- EECS-EX-2018:660. [57] Rakel Wreland Lindström. Interview about battery chemistry, 2018. [58] Swedavia Ab. Miljörapport 2017. 2017.

50 Appendix A Appendix A, SIMULINK Images A.1 SIMULINK Images Figure A.1: The Battery Pack & Useage Parameters layer of the SIMULINK model used in the case study. Figure A.2: The Energy Limit Block layer of the SIMULINK model used in the case study.

Appendix A. Appendix A, SIMULINK Images 51 Figure A.3: The Charge Strategy Selection layer of the SIMULINK model used in the case study. Figure A.4: The Current Function layer of the SIMULINK model used in the case study. Figure A.5: The Slow Charging layer of the SIMULINK model used in the case study.

Appendix A. Appendix A, SIMULINK Images 52 Figure A.6: The Fast Charging layer of the SIMULINK model used in the case study. Figure A.7: The Induction Charging layer of the SIMULINK model used in the case study.

53 Appendix B Appendix B, MATLAB Code B.1 Current Function/MATLAB Function function [I_Battery,ChargeVariable, Cycles] = fcn(soclimits,i_discharge,i_charge,soc,charge SOCmax = SOClimits(1); SOCmin = SOClimits(2); %Save cycles Cycles = CyclesPrev; %Keep same charging pattern if nothing changes: ChargeVariable=ChargeVariablePrev; %If the battery SOC is too low, charge it: if(chargevariableprev==0 && SOC<=(SOCmin)) ChargeVariable=1; end %If the battery SOC is too high, stop charging it: if(chargevariableprev==1 && SOC>=SOCmax) ChargeVariable=0; Cycles = Cycles + 1; end if (ChargeVariablePrev==0) %Discharge I_Battery=I_DisCharge; else %Charge I_Battery= - I_Charge; end B.2 Plot Function clc

Appendix B. Appendix B, MATLAB Code 54 chargestrategyarray = ["Slow","3"; "Fast","2"; "Induction","1"]; degsymbol = sprintf('%c', char(176)); %% Change these values at start of simulation % close plots after each scenario is done simulating, 1 = yes, 0 = no closeplots = 1; % plots and tables or not? 1 = yes, 0 = no savecaplossplots = 0; savetotalcaplossplot = 0; savetables = 0; % save current and SOC plot/current and temperature plot? savecursocplot = 1; savecurtempplot = 1; % Percent at EOl percateol = 0.80; % Time simulated simyears = 0.05; % Temperatures simulated temperaturearray = ["20","30","40"]; % run all temperatures? alltemp = 1; if alltemp == 1 temp = [1,2,3]; else temp = [1]; end %% Create table of results resulttablecell = cell(5); resulttablecell(1,1) = {'ChargingStrategy'}; resulttablecell(1,2) = {'Tempertature1'}; resulttablecell(1,3) = {'Tempertature2'}; resulttablecell(1,4) = {'Tempertature3'}; resulttablecell(1,5) = {'ChargeTime'}; % Results with simulation time instead of "usage time" resulttablecellsimtime = resulttablecell; % and vectors that save previous results timesavevector = [];

Appendix B. Appendix B, MATLAB Code 55 voltagesavevector = []; currentsavevector = []; SOCSaveVector = []; celltempsavevector = []; ageineqcyclessavevector = []; maxcapacitysavevector = []; %% Load model model = 'ChargingStrategiesSim'; load_system(model); i=0; for temp = temp for ch = [1,2,3] % Induction charging has a longer TTEOL if ch == 3 yearssimulateddouble = simyears;%*1.5; else yearssimulateddouble = simyears; end %% simulation has to start with temp = 20 if temp == 1 && temperaturearray(temp) ~= "20" disp("you have to start the simulation with 20 C") end set_param('chargingstrategiessim/years','value',num2str(yearssimulateddouble)); %% Reset table at points where it should write resulttablecell(2,1+temp) = {''}; resulttablecell(2+ch,1) = {''}; resulttablecell(2+ch,5) = {''}; %% Set temperature and charging strategy for the iteration i=i+1; set_param('chargingstrategiessim/ambient Temperature','Value',temperatureArray(temp)) set_param('chargingstrategiessim/charge Strategy','Value',chargeStrategyArray(ch,2)) %originalmaxcapacity = str2double(get_param(... % 'ChargingStrategiesSim/Battery Cell Capacity in Ah','Value')); originalpackcapacity = str2double(get_param(... 'ChargingStrategiesSim/Battery Pack & Usage Parameters/Battery Pack Capacity in kwh'...,'value')); yearssimulated = get_param('chargingstrategiessim/years','value'); yearssimulatedstring = strrep(yearssimulated,"/","_"); yearssimulateddouble = str2double(yearssimulated); disp(strcat("now simulating ",chargestrategyarray(ch,1),...

Appendix B. Appendix B, MATLAB Code 56 " charging with temperature ",temperaturearray(temp)," degrees C",... " for ",yearssimulatedstring," years.")) sim(model) %% Saves variables from the scope in the simulation % time time = ScopeData1.time; %timesavevector{i} = time; % Voltage voltage = ScopeData1.signals(1).values; %voltagesavevector{i} = voltage; % Current current = ScopeData1.signals(2).values; %currentsavevector{i} = current; % SOC SOC = ScopeData1.signals(3).values; %SOCSaveVector{i} = SOC; % celltemp celltemp = ScopeData1.signals(4).values; %celltempsavevector{i} = celltemp; % ageineqcycles ageineqcycles = ScopeData1.signals(5).values; %ageineqcyclessavevector{i} = ageineqcycles; % maxcapacity maxcapacity = ScopeData1.signals(6).values; %maxcapacitysavevector{i} = maxcapacity; %% Store the max capacity of the battery at 20 deg C, at time ~1 sec, if temp == 1 && ch == 1 originalmaxcapacity20c = maxcapacity(25); end %% Time Conversion secondspermonth = 60*60*24*365/12; secondsperyear = secondspermonth*12; timeinmonths = time/secondspermonth; timesize = size(time); timesize = timesize(1); timeinyears = linspace(0,yearssimulateddouble,timesize); %% Plot saving % file path fpath = "C:\Users\Viktor\"; %% Saves plot of capacity loss caplossfilename = strcat(yearssimulatedstring,"yearswithtemp",... temperaturearray(temp),chargestrategyarray(ch,1),"chargingcaploss",".jpg"); maxcapinpack = maxcapacity/originalmaxcapacity20c*originalpackcapacity; EOLOfPack = originalpackcapacity*percateol*ones(size(timeinyears))';

Appendix B. Appendix B, MATLAB Code 57 intersectionplace = find(diff(sign(maxcapinpack-eolofpack))); timeateol = timeinyears(intersectionplace); timeateolroundedstr = sprintf('%.2f',timeateol); if ch == 1 timeateolroundedstr1 = timeateolroundedstr; elseif ch == 2 timeateolroundedstr2 = timeateolroundedstr; else timeateolroundedstr3 = timeateolroundedstr; end figure(100+ch*10+temp) plot(timeinyears,maxcapinpack...,timeinyears,originalpackcapacity*percateol*ones(size(timeinyears)),'--r'...,timeateol,maxcapinpack(intersectionplace),"*") caplossplottitle = {strcat("maximum capacity loss during "...,lower(chargestrategyarray(ch,1))," charging "); strcat(" with an ambient temperature of ",temperaturearray(temp),degsymbol,"c")}; title(caplossplottitle) grid on axis([0 inf originalpackcapacity*0.7 originalpackcapacity*1.1]) xlabel('time in Years') ylabel('maximum capacity of the battery pack in kwh') legend("capacity loss", strcat(num2str(percateol*100)," % of original capacity"),... strcat("eol of battery pack, occurs at ",timeateolroundedstr," years")) if savecaplossplots == 1 saveas(gcf,strcat(fpath,caplossfilename)) end if closeplots == 1 close(100+ch*10+temp) end %% Save plot of current/soc timeplace3e4 = find(diff(sign(time-3*10^4))); timeplace3e4 = timeplace3e4(1); timeplace12e4 = find(diff(sign(time-12*10^4))); timeplace12e4 = timeplace12e4(1); timeinhours = (12e4-3e4)/3600; I_SOC_timeSpan = linspace(0,25,timeplace12e4-timeplace3e4)'; currentspan = current(timeplace3e4:timeplace12e4-1); SOCSpan = SOC(timePlace3e4:timePlace12e4-1); cursocfilename = strcat("cursocgraphwith",chargestrategyarray(ch,1),"charging.jpg"); % Find amount of time the car needs to be charged

Appendix B. Appendix B, MATLAB Code 58 cursqarewaveedges = find(diff(sign(currentspan-max(current)))); cursquarewavelengthintime = zeros(1,16); for i = [1:size(curSqareWaveEdges)-1] cursquarewavelengthintime = [I_SOC_timeSpan(curSqareWaveEdges(i+1))-... I_SOC_timeSpan(curSqareWaveEdges(i)) cursquarewavelengthintime]; end chargetimepercent = 100*curSquareWaveLengthInTime(1)/curSquareWaveLengthInTime(2); chargetimepercent = sprintf('%.1f',chargetimepercent); disp(strcat("during ",lower(chargestrategyarray(ch,1)),... " charging, the battery needs to be charged ",chargetimepercent, " % of the time.")) % if temp == 1 figure(200+ch*10+temp) hold on grid on yyaxis left plot(i_soc_timespan,currentspan) cursocplottitle = strcat("current into battery cell and SOC of battery cell during "...,lower(chargestrategyarray(ch,1))," charging"); title(cursocplottitle) axis([0 timeinhours min(current)-0.5 max(current)+0.5]) xlabel("time in hours") ylabel("current into battery cell in A") yyaxis right axis([0 timeinhours 0 100]) plot(i_soc_timespan,socspan) ylabel("soc of battery cell in percent") if savecursocplot == 1 saveas(gcf,cursocfilename) end if closeplots == 1 close(200+ch*10+temp) end end %% Save plot of current/temp I_SOC_timeSpan = linspace(0,25,timeplace12e4-timeplace3e4)'; currentspan = current(timeplace3e4:timeplace12e4-1); TempSpan = celltemp(timeplace3e4:timeplace12e4-1); curtempfilename = strcat("curtempgraphwith",chargestrategyarray(ch,1),... "Charging",temperatureArray(temp),degSymbol,"C.jpg"); figure(300+ch*10+temp) hold on

Appendix B. Appendix B, MATLAB Code 59 grid on yyaxis left plot(i_soc_timespan,currentspan) cursocplottitle = strcat(... "Current into battery cell and temperature of battery cell during "...,lower(chargestrategyarray(ch,1))," charging"); title(cursocplottitle) axis([0 timeinhours min(current)-0.5 max(current)+0.5]) xlabel("time in hours") ylabel("current into battery cell in A") yyaxis right tempint = str2num(temperaturearray(temp)); axis([0 timeinhours tempint-5 tempint+5]) plot(i_soc_timespan,tempspan) ylabel("temperature of battery cell in C") if savecurtempplot == 1 saveas(gcf,curtempfilename) end if closeplots == 1 close(300+ch*10+temp) end %% Fill in table of results resulttablecell(2,1+temp) = {temperaturearray(temp)}; resulttablecell(2+ch,1) = {chargestrategyarray(ch,1)}; if temp == 1 resulttablecell(2+ch,5) = {chargetimepercent}; end resulttablecell(2+ch,1+temp) = {timeateol}; end end %% Save tables resulttable = cell2table(resulttablecell(2:end,:)); resulttable.properties.variablenames = resulttablecell(1,:); resulttable if savetables == 1 writetable(resulttable,strcat(fpath,yearssimulatedstring,"resulttable.xls")) end disp("done!")

60 Appendix C Appendix C, Battery Model Input Parameters Figure C.1: The Simulink battery model input parameters used in the case study.

Appendix C. Appendix C, Battery Model Input Parameters 61 Figure C.2: The Simulink battery model input parameters on discharge used in the case study.

Appendix C. Appendix C, Battery Model Input Parameters 62 Figure C.3: The Simulink battery model input parameters on temperature used in the case study.

Appendix C. Appendix C, Battery Model Input Parameters 63 Figure C.4: The Simulink battery model input parameters on aging used in the case study.