Lithium-ion battery packs are essential to the electrification of cars, especially electric vehicles (EVs), as they provide the required energy storage for longer driving distances and improved performance. The performance and design trends for Li-ion battery packs for electric vehicles are presented in this article in detail. This review paper examines the contemporary movements in the boundaries of Li-ion battery technology for EV applications, which involve a range of factors, such as design specifications of a battery pack and other safety measures, the importance of battery management systems (BMS), and thermal management systems (TMS). The design levels of Li-ion battery packs are discussed based on a selection of the right cells and application criteria, which include the weight of the battery in an electric vehicle (EV) and the placement of the battery in the vehicle, which can affect the handling and power efficiency of the vehicle. Moreover, the Li-ion battery intelligence system, referred to as the Battery Management System (BMS), is extensively discussed, including its architecture, functions, and conventional and advanced optimized algorithms to protect and mitigate battery failures. The reliability of precise estimation of state-of-charge and cell balancing is investigated, which integrates with artificial intelligence and machine learning techniques. In this article, the temperature consequences of Li-ion batteries during internal and external fault operating conditions investigated, and various advanced battery thermal management systems emphasized to prevent thermal runaway in EV applications. The research results provide a roadmap for academics, engineers, and other industry participants to comprehend and traverse the rapidly developing field of battery technology, fostering the development of effective, secure, and environmentally friendly energy storage options for electric vehicles.
BanikARangaJShrivastavaA, et al.Novel energy-efficient hybrid green energy scheme for future sustainability. In: 2021 International Conference on Technological Advancements and Innovations (ICTAI), Tashkent, Uzbekistan, 2021, 428-433.
3.
HemavathiSSrinivasSPrakashAS. Importance of battery pack design and battery management systems in electric vehicles. In: Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles. Global, 2023.
4.
GaneshVKrishnaVMASenthilmuruganS, et al. Modeling of electric vehicle DC fast charger. In: Electric Transportation Systems in Smart Power Grids. Taylor & Francis Group, 2022.
5.
HemavathiSUthirasamyRSharmeelaC, et al. Advanced converter topologies for EV fast charging. In: Electric Transportation Systems in Smart Power Grids. Taylor & Franics Group, 2022.
6.
KumarTVVPHaidariMVimalaRSubramanyaRLakshminarayanaMHemavathi. Low-cost Battery Management System for E-vehicles. In: International Conference on Edge Computing and Applications, ICECAA 2022 - Proceedings. IEEE, 2022.
7.
Mahesh PrasannaKKarthicRSBabuBH, et al.Hemavathi, machine learning model for the prediction of an E-vehicle’s battery life cycle. In: International Conference on Edge Computing and Applications, ICECAA 2022 - Proceedings, 2022.
8.
HemavathiS. Modeling of analog battery management system for single cell lithium-ion battery. Energy Storage. 2021; 3: 1–9.
9.
HemavathiSSriramaSPrakashAS. Present and future generation of secondary batteries: a review. CBEN2023; 10: 1123–1145.
10.
RajEFIKarthikaJKumarNK, et al.A hybrid solar-battery power source with nano - grid smart home with plug-in electric vehicle. AIP Conf Proc2023; 2690(1): 020039.
11.
ChangJWeiZHeH. Lithium-ion battery parameter identification and state of charge estimation based on equivalent circuit model. In: Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, IEEE, 2020.
12.
KrugerDDBeerRC, Prismatic-cell battery pack with integral coolant passages, US patent, US2010/0143782 A1, 2010.
13.
Thiru KumaranAHemavathiS. Optimization of Lithium-ion battery thermal performance using dielectric fluid immersion cooling technique. Process Saf Environ Prot2024; 189: 768–781.
14.
HemavathiSThiru KumaranASrinivasS, et al.Synthetic ester-based forced flow immersion cooling technique for fast discharging lithium-ion battery packs. J Energy Storage2024; 97: 112852.
15.
HemavathiSSrinivasSPrakashAS. Performance evaluation of a hydrostatic flow immersion cooling system for high-current discharge Li-ion batteries. J Energy Storage2023; 72: 108560.
16.
WeberDRSpencerSJSpacherPF, Battery cooling plate design with discrete channels, US patent, US2009/0258289 A1, 2009.
17.
DipanAAlaaEl-SSatyamP. Development of time-temperature analysis algorithm for estimation of lithium-ion battery useful life. In: WCX SAE World Congress Experience, SAE International. SAE International, 2024.
18.
HemavathiSJeevandossCRSrinivasS, et al.Experimental studies on Li-ion battery pack for temperature distribution analysis during fast discharging and various ambient temperature conditions. Heat Transf Res2024; 55(2): 41–54.
19.
SrividyaKSHemavathiSPreetha RoselynJ. Design of an efficient energy management system for renewables-based wireless electric vehicle charging station. Energy Storage2023; 5(6): e454.
20.
SunJFangJWangY, et al.Multi memristors in series, parallel and series-parallel connection. J Comput Theor Nanosci2016; 13: 3926–3935.
21.
VanCNVinhTN. Soc estimation of the lithium-ion battery pack using a sigma point Kalman filter based on a cell’s second order dynamic model. Applied Sciences (Switzerland). 2020; 10: 1–16.
22.
HuoHXingYPechtM, et al.Safety requirements for transportation of lithium batteries. Energies2017; 10: 793.
23.
WengJOuyangDYangX, et al.Alleviation of thermal runaway propagation in thermal management modules using aerogel felt coupled with flame-retarded phase change material. Energy Convers Manag2019; 200: 112071.
24.
LiNZhangYHeF, et al.Review of lithium-ion battery state of charge estimation. Global Energy Interconnection2021; 4: 619–630.
25.
HannanMALipuMSHHussainA, et al.A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew Sustain Energy Rev2017; 78: 834–854.
26.
SayeedFHanumanthakariSOommenS, et al.A novel and comprehensive mechanism for the energy management of a Hybrid Micro-grid System. Energy Rep2022; 8: 847–862.
27.
XiongRCaoJYuQ, et al.Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access2017; 6: 1832–1843.
28.
NagilaAMgSDeepakFD, et al.Ultra-fast charging E-vehicle batteries from PV using DC-DC converter. In: 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, pp. 711–716.
29.
TongSLacapJHParkJW. Battery state of charge estimation using a load-classifying neural network. J Energy Storage2016; 7: 236–243.
30.
ChakrabortyDGhivariMDatarM, et al. A hybrid system and method for estimating state of charge of a battery. SAE Int J Commer Veh2021; 14.
31.
HemavathiS. Modeling and estimation of lithium-ion battery state of charge using intelligent techniques. In: Lecture Notes in Electrical Engineering. Singapore: Springer, 2020.
32.
Kamal HossainMRakiul IslamSM. Battery impedance measurement using electrochemical impedance spectroscopy board. In: 2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017. IEEE, 2017.
33.
ChenLLüZLinW, et al. A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity. Measurement. 2018; 116: 586–595.
34.
HemavathiS. Li-ion battery health estimation based on battery internal impedance measurement. In: MuthukumarPSarkarDKDeD, et al. (eds). Innovations in Sustainable Energy and Technology. Singapore: Springer Singapore, 2021, pp. 183–193.
35.
BarrerasJVde CastroRWanY, et al.A consensus algorithm for multi-objective battery balancing. Energies2021; 14: 4279.
36.
HeFMaL. Thermal management in hybrid power systems using cylindrical and prismatic battery cells. Heat Transf Eng2016; 37: 581–590.
37.
LiuZTanCLengF. A reliability-based design concept for lithium-ion battery pack in electric vehicles. Reliab Eng Syst Saf2015; 134: 169–177.
38.
KhalidMSheikhSSJanjuaAK, et al. Performance validation of electric vehicle’s battery management system under state of charge estimation for lithium-ion battery. Proceedings 2018 International Conference Computing, Electronic and Electrical Engineering (ICE Cube). IEEE, 2018.
39.
HeFAmsAARoosienY, et al.Reduced-order thermal modeling of liquid-cooled lithium-ion battery pack for EVs and HEVs. In: 2017 IEEE Transportation and Electrification Conference and Expo, ITEC 2017, IEEE, 2017.
40.
SarmahSBKalitaPDasB, et al.Numerical and experimental investigation of state of health of Li-ion battery. Int J Green Energy2020; 17: 510–520.
41.
HemavathiS. Modeling and energy optimization of hybrid energy storage system. In: SahooU (ed) Hybrid Renewable Energy Systems. John Wiley & Sons, Inc., 2021.
42.
OmaribaZBZhangLSunD. Review of battery cell balancing methodologies for optimizing battery pack performance in electric vehicles. IEEE Access2019; 7: 129335–129352.
43.
KirubadeviSHemalathaNSankaranE, et al.Autonomous smart grid management and control by using IoT. Int J Syst Assur Eng Manag2022; 13: 625–632.
44.
WuJFangLCDongGZ, et al.State of health estimation for lithium-ion batteries in real-world electric vehicles. Sci China Technol Sci2023; 66: 47–56.
45.
ZhuMHuWKarNC. The SOH estimation of LiFePO4 battery based on internal resistance with Grey Markov chain. In: 2016 IEEE Transportation Electrification Conference and Expo, ITEC 2016, IEEE, 2016.
46.
DasUKShrivastavaPTeyKS, et al.Advancement of lithium-ion battery cells voltage equalization techniques: a review. Renew Sustain Energy Rev2020; 134: 110227.
47.
ReiterCWassiliadisNLienkampM. Design of thermal management systems for battery electric vehicles. In: 2019 14th International Conference on Ecological Vehicles and Renewable Energies, EVER 2019. IEEE, 2019.
48.
JerouschekDTanÖKennelR, et al.Data preparation and training methodology for modeling lithium-ion batteries using a long short-term memory neural network for mild-hybrid vehicle applications. Applied Sciences (Switzerland)2020; 10: 7880.
49.
KumarMSManasaTRRajaB, et al.Estimation of state of charge and terminal voltage of li-ion battery using extended Kalman filter. In: 6th IEEE International Energy Conference, ENERGYCon 2020, IEEE, 2020.
ZunCYParkSUMokHS. New cell balancing charging system research for lithium-ion batteries. Energies2020; 13: 1393.
52.
ZhengLZhuJWangG, et al. Model predictive control based balancing strategy for series-connected lithium-ion battery packs. In: 2017 19th European Conference on Power Electronics and Applications, EPE 2017 ECCE Europe. IEEE, 2017.
53.
WeiYDaiSWangJ, et al.Switch matrix algorithm for series lithium battery pack equilibrium based on derived acceleration information Gauss-seidel. Math Probl Eng2019; 2019: 8075453.
54.
NigamPSharmaPKkumar GuptaR, et al.Intelligent trust based electrical vehicles using 6G. J Pharm Negat Results2022; 2022: 583–591.
55.
SheikhSSShahFAAtharSO, et al.A data-driven comparative analysis of lithium-ion battery state of health and capacity estimation. Elec Power Compon Syst2023; 51(1): 1–11.
56.
YusofMSTohaSFKamisanNA, et al. Battery cell balancing optimisation for battery management system. In: IOP Conf Ser Mater Sci Eng. IOP Publishing Ltd, 2017.
57.
ProbstlAParkSNarayanaswamyS, et al. SOH-Aware active cell balancing strategy for high power battery packs. In: Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018. IEEE, 2018.
58.
SenthilkumarSHaidariMDeviG, et al., Wireless bidirectional power transfer for E-vehicle charging system. In: 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, 705–710.
59.
KaliaperumalMDharanendrakumarMSPrasannaS, et al.Cause and mitigation of lithium-ion battery failure—a review. Materials2021; 14: 5676.
60.
DaiHZhaoGLinM, et al.A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Trans Ind Electron2019; 66: 7706–7716.
61.
HartingNSchenkendorfRWolffN, et al.State-of-Health identification of lithium-ion batteries based on nonlinear frequency response analysis: first steps with machine learning. Applied Sciences (Switzerland)2018; 8: 821.
62.
ChenZXueQXiaoR, et al.State of health estimation for lithium-ion batteries based on fusion of autoregressive moving average model and Elman neural network. IEEE Access2019; 7: 102662–102678.
63.
ShahFASheikhSSMirUI, et al. Battery health monitoring for commercialized electric vehicle batteries: lithium-ion. In: Proc. 2019 Int. Conf. Power Generation Systems and Renewable Energy Technologies (PGSRET). IEEE, 2019.
64.
FengXWengCHeX, et al.Online state-of-health estimation for Li-ion battery using partial charging segment based on support vector machine. IEEE Trans Veh Technol2019; 68: 8583–8592.
65.
MathewMJanhunenSRashidM, et al.Comparative analysis of lithium-ion battery resistance estimation techniques for battery management systems. Energies2018; 11: 1490.
66.
PiaoCWangZCaoJ, et al.Lithium-ion battery cell-balancing algorithm for battery management system based on real-time outlier detection. Math Probl Eng2015; 2015: 1–12.
67.
HemavathiS. Overview of cell balancing methods for Li-ion battery technology. Energy Storage. 2021; 3: 1–12.
68.
AhmadABOoiCAIshakD, et al. Cell balancing topologies in battery energy storage systems: a review. In: Lecture Notes in Electrical Engineering. Singapore: Springer, 2019.
69.
KumarMYadavVKMathuriyaK, et al.A brief review on cell balancing for Li-ion battery pack (BMS). In: 2022 IEEE 10th Power India International Conference, PIICON 2022, IEEE, 2022.
70.
TyrpeklMZavrelMKindlV. Comparison of active and passive battery balancing. In: Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika. 2022.
71.
WangDBaoYShiJ. Online lithium-ion battery internal resistance measurement application in state-of-charge estimation using the extended kalman filter. Energies2017; 10: 1284.
72.
RenHZhaoYChenS, et al.Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation. Energy2019; 166: 908–917.
73.
WenFDuanBZhangC, et al.High-accuracy parameter identification method for equivalent-circuit models of lithium-ion batteries based on the stochastic theory response reconstruction. Electronics (Switzerland)2019; 8: 834.
74.
WeiMWangQYeM, et al.State of charge estimation for lithium-ion battery using dynamic neural networks. In: Proceedings - International Conference on Artificial Intelligence and Electromechanical Automation. AIEA, 2020, vol 2020, pp. 23–26.
75.
ZengMZhangPYangY, et al.SOC and SOH joint estimation of the power batteries based on fuzzy unscented Kalman filtering algorithm. Energies2019; 12: 3122.
76.
ZhangXZhangWLeiG. A review of li-ion battery equivalent circuit models. Transactions on Electrical and Electronic Materials2016; 17: 311–316.
77.
DuanJTangXDaiH, et al.Building safe lithium-ion batteries for electric vehicles: a review. Electrochem Energ Rev2020; 3: 1–42.
78.
HemavathiSShinishaA. A study on trends and developments in electric vehicle charging technologies. J Energy Storage2022; 52: 105013.
79.
KrishnakumarSKumarASelvaraniNSatishGRamanChandanRadhaHemavathi. IoT-based battery management system for E-vehicles. In: 3rd International Conference on Smart Electronics and Communication, ICOSEC 2022 - Proceedings. IEEE, 2022.
80.
HemavathiSSrinivasSPrakashAS. Experimental studies of a static flow immersion cooling system for fast-charging Li-ion batteries. Exp Heat Transf2023; 37: 830–850.
81.
RodriguesRAlbuquerqueVFerreiraJC, et al.Mining electric vehicle adoption of users. World Electric Vehicle Journal2021; 12: 233.
82.
WengJHeYOuyangD, et al.Honeycomb-inspired design of a thermal management module and its mitigation effect on thermal runaway propagation. Appl Therm Eng2021; 195: 117147.
83.
HouJYangMWangD, et al.Fundamentals and challenges of lithium-ion batteries at temperatures between −40 and 60°C. Adv Energy Mater2020; 10: 1904152.
84.
LuZYuXLWeiLC, et al.A comprehensive experimental study on temperature-dependent performance of lithium-ion battery. Appl Therm Eng2019; 158: 113800.
85.
MaSJiangMTaoP, et al.Temperature effect and thermal impact in lithium-ion batteries: a review. Prog Nat Sci Mater Int2018; 28: 653–666.
86.
LiWChenSPengX, et al.A comprehensive approach for the clustering of similar-performance cells for the design of a lithium-ion battery module for electric vehicles. Engineering2019; 5: 795–802.
87.
Najafi KhaboshanHJaliliantabarFAbdullahAA, et al.Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models. Appl Therm Eng2024; 247: 123080.
88.
QinPSunJYangX, et al.Battery thermal management system based on the forced-air convection: a review. ETransportation2021; 7: 100097.
89.
FengXOuyangMLiuX, et al.Thermal runaway mechanism of lithium-ion battery for electric vehicles: a review. Energy Storage Mater2018; 10: 246–267.
90.
YazdanpourMTaheriPMansouriA, et al.A circuit-based approach for electro-Thermal modeling of lithium-ion batteries. In: Annual IEEE Semiconductor Thermal Measurement and Management Symposium, IEEE, 2016.
91.
WengJHeYOuyangD, et al.Thermal performance of PCM and branch-structured fins for cylindrical power battery in a high-temperature environment. Energy Convers Manag2019; 200: 112106.
92.
DhakalRParameswaranSMuthukumarR, et al. Performance analysis of electrical vehicle battery thermal management system. In: SAE Technical Papers. SAE International, 2022.
93.
HuangC-SChengZChowM-Y. A robust and efficient state-of-charge estimation methodology for serial-connected battery packs: most significant cell methodology. IEEE Access2021; 9: 74360–74369.
94.
GhijiMNovozhilovVMoinuddinK, et al.A review of lithium-ion battery fire suppression. Energies2020; 13: 5117.
95.
NizamMPutraMRA. Analysis of lithium-ion indirect liquid cooling battery thermal management system with high discharge rate. Int J Power Electron Drive Syst2023; 14: 1414.
96.
RyuMKimTParkS, et al.A study on the characteristics of the indirect air-cooling system of a lithium-ion battery module for vehicle. Transactions of the Korean Society of Automotive Engineers2018; 26: 663–675.
97.
SoffientiniLChiusoloCGottiM. Lithium-ion batteries: analysis of intrinsic Hazards and evaluation of minimum safety requirements through risk assessment of storage and testing installations. Chem Eng Trans2022; 96: 181–186.
98.
TengHYeowK. Design of direct and indirect liquid cooling systems for high-capacity, high-power lithium-ion battery packs. In: SAE International Journal of Alternative Powertrains. SAE International, 2012.
99.
WangQJiangBLiB, et al.A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renew Sustain Energy Rev2016; 64: 106–128.
100.
KhalfaouiMHamlatA. Extended kalman filter for the estimation of the state of charge of lithium-ion batteries. In: Lecture Notes in Networks and Systems. Springer, 2021.
101.
ChangCYangJLiY, et al.Research of supercapacitor voltage equalization strategy on rubber-tyred gantry crane energy saving system. Energy Power Eng2010; 02: 25–30.
102.
SawLHTayAAOZhangLW. Thermal management of lithium-ion battery pack with liquid cooling. In: 2015 31st Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2015, pp. 298–302.
103.
WangQJiangBXueQF, et al.Experimental investigation on EV battery cooling and heating by heat pipes. Appl Therm Eng2014; 88: 54–60.
104.
ZouHWangWZhangG, et al.Experimental investigation on an integrated thermal management system with heat pipe heat exchanger for electric vehicle. Energy Convers Manag2016; 118: 88–95.
105.
TranTHHarmandSDesmetB, et al.Experimental investigation on the feasibility of heat pipe cooling for HEV/EV lithium-ion battery. Appl Therm Eng2014; 63: 551–558.
106.
GrecoACaoDJiangX, et al.A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes. J Power Sources2014; 257: 344–355.
107.
HuangQLiXZhangG, et al.Experimental investigation of the thermal performance of heat pipe assisted phase change material for battery thermal management system. Appl Therm Eng2018; 141: 1092–1100.
108.
TaleleVMoralıUNajafi KhaboshanH, et al.Improving battery safety by utilizing composite phase change material to delay the occurrence of thermal runaway event. Int Commun Heat Mass Tran2024; 155: 107527.
109.
DivyaSSivaRMMohanaJ, et al.Ravishankar Sathyamurthy, Analyzing the performance of combined solar photovoltaic power system with phase change material. Energy Rep2022; 8: 43–56.
110.
VakilzadehAHSarvestaniABJavaherdehK, et al.Heat transfer and fluid flow in a PCM-filled enclosure: effect of heated wall configuration. J Energy Storage2024; 87: 111448.
111.
AliHAARaijmakersLHJChayambukaK, et al.A comparison between physics-based Li-ion battery models. Electrochim Acta2024; 493: 144360.
112.
GaoTLuW. Reduced-order electrochemical models with shape functions for fast, accurate prediction of lithium-ion batteries under high C-rates. Appl Energy2024; 353(Part A): 121954.
113.
WuLLiuKPangH, et al.Online SOC estimation based on simplified electrochemical model for lithium-ion batteries considering current Bias. Energies2021; 14(17): 5265.
114.
LiuJYadavSSalmanM, et al.Review of thermal coupled battery models and parameter identification for lithium-ion battery heat generation in EV battery thermal management system. Int J Heat Mass Tran2024; 218: 124748.
115.
GuoFCoutoLDMulderG, et al.A systematic review of electrochemical model-based lithium-ion battery state estimation in battery management systems. J Energy Storage2024; 101(Part A): 113850.
116.
LiuKGaoYZhuC, et al.Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Eng Pract2022; 124: 105176.
117.
DiniPColicelliASaponaraS. Review on modeling and SOC/SOH estimation of batteries for automotive applications. Batteries2024; 10(1): 34.
118.
SchmidtS. Use of battery swapping for improving environmental balance and price-performance ratio of electric vehicles. eTransportation2021; 9: 100128.
119.
BruckerJGasperRBesslerWG. A grey-box model with neural ordinary differential equations for the slow voltage dynamics of lithium-ion batteries: application to single-cell experiments. J Power Sources2024; 614: 234918.
120.
CancelliereFGirardSBourinetJ-M, et al. Grey-box approach for the prognostic and health management of lithium-ion batteries. Annual Conference of the PHM Society. 2023; 15(1): 1–8.
121.
FangBXuLLuoY, et al.A method for short-term electric load forecasting based on the FMLP-iTransformer model. Energy Rep2024; 12: 3405–3411.
122.
HemavathiS. India's lithium-ion battery landscape strategic opportunities, market dynamics, and innovation pathways. Energy Storage2025; 7(6): e70244.
123.
WangFZhaiZZhaoZ, et al.Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun2024; 15: 4332.
124.
SureshCAwasthiAKumarB, et al.Advances in battery thermal management for electric vehicles: a comprehensive review of hybrid PCM-metal foam and immersion cooling technologies. Renew Sustain Energy Rev2025; 208: 115021.
125.
RoeCFengXWhiteG, et al.Immersion cooling for lithium-ion batteries – a review. J Power Sources2022; 525: 231094.
