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Analysis of battery management system issues in electric vehicles

V Karkuzhali 1 , P Rangarajan 2 , V Tamilselvi 3 and P Kavitha 1

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 994 , International Conference on Recent Developments in Robotics, Embedded and Internet of Things (ICRDREIOT2020) 16- 17 October 2020, Tamil Nadu, India Citation V Karkuzhali et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 994 012013 DOI 10.1088/1757-899X/994/1/012013

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1 Assistant Professor Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

2 Professor Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

3 H. O. D Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

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Battery technology has dramatically advanced over a decade and many high performance batteries are being developed. Electric vehicles (EV) require high power batteries with suitable battery management systems (BMS) for safe and reliable operations. Intention of this paper is to discuss about the batteries used in electric vehicles and the key issues of battery management systems and to compare the Lithium ion (Li-ion) battery & Nickel metal hydride battery in terms of aging and effect of temperature using their state of charge (SOC) and open circuit voltage (OCV).

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A brief review on key technologies in the battery management system of electric vehicles

• Review Article
• Open access
• Published: 02 April 2018
• Volume 14 , pages 47–64, ( 2019 )

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• Kailong Liu 1 ,
• Kang Li 1 ,
• Qiao Peng 2 &
• Cheng Zhang 3  

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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State of charge estimation techniques for battery management system used in electric vehicles: a review

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Abada S, Marlair G, Lecocq A, et al. Safety focused modeling of lithium-ion batteries: A review. Journal of Power Sources, 2016, 306: 178–192

Google Scholar  

Rao Z, Wang S. A review of power battery thermal energy management. Renewable & Sustainable Energy Reviews, 2011, 15 (9): 4554–4571

Park B, Lee C H, Xia C, et al. Characterization of gel polymer electrolyte for suppressing deterioration of cathode electrodes of Li ion batteries on high-rate cycling at elevated temperature. Electrochimica Acta, 2016, 188: 78–84

Li J, Han Y, Zhou S. Advances in Battery Manufacturing, Services, and Management Systems. Hoboken: John Wiley-IEEE Press, 2016

Lu L, Han X, Li J, et al. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, 2013, 226: 272–288

Rahman M A, Anwar S, Izadian A. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources, 2016, 307: 86–97

Sung W, Shin C B. Electrochemical model of a lithium-ion battery implemented into an automotive battery management system. Computers & Chemical Engineering, 2015, 76: 87–97

Shen W J, Li H X. Parameter identification for the electrochemical model of Li-ion battery. In: Proceedings of 2016 International Conference on System Science and Engineering (ICSSE). Puli: IEEE, 2016, 1–4

Mastali M, Samadani E, Farhad S, et al. Three-dimensional multiparticle electrochemical model of LiFePO4 cells based on a resistor network methodology. Electrochimica Acta, 2016, 190: 574–587

Han X, Ouyang M, Lu L, et al. Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model. Journal of Power Sources, 2015, 278: 802–813

Zou C, Manzie C, Nešić D. A framework for simplification of PDE-based lithium-ion battery models. IEEE Transactions on Control Systems Technology, 2016, 24(5): 1594–1609

Yuan S, Jiang L, Yin C, et al. A transfer function type of simplified electrochemical model with modified boundary conditions and Padé approximation for Li-ion battery: Part 2. Modeling and parameter estimation. Journal of Power Sources, 2017, 352: 258–271

Bartlett A, Marcicki J, Onori S, et al. Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery. IEEE Transactions on Control Systems Technology, 2016, 24(2): 384–399

Zhang L, Wang Z, Hu X, et al. A comparative study of equivalent circuit models of ultracapacitors for electric vehicles. Journal of Power Sources, 2015, 274: 899–906

Nejad S, Gladwin D T, Stone D A. A systematic review of lumpedparameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources, 2016, 316: 183–196

Zhang X, Lu J, Yuan S, et al. A novel method for identification of lithium-ion battery equivalent circuit model parameters considering electrochemical properties. Journal of Power Sources, 2017, 345: 21–29

Widanage W D, Barai A, Chouchelamane G H, et al. Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 1: Signal design. Journal of Power Sources, 2016, 324: 70–78

Gong X, Xiong R, Mi C C. A data-driven bias-correction-methodbased lithium-ion battery modeling approach for electric vehicle applications. IEEE Transactions on Industry Applications, 2016, 52(2): 1759–1765

Wang Q K, He Y J, Shen J N, et al. A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach. Energy, 2017, 138: 118–132

Deng Z, Yang L, Cai Y, et al. Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery. Energy, 2016, 112: 469–480

Sbarufatti C, Corbetta M, Giglio M, et al. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks. Journal of Power Sources, 2017, 344: 128–140

Li Y, Chattopadhyay P, Xiong S, et al. Dynamic data-driven and model-based recursive analysis for estimation of battery state-ofcharge. Applied Energy, 2016, 184: 266–275

Richter F, Kjelstrup S, Vie P J, et al. Thermal conductivity and internal temperature profiles of Li-ion secondary batteries. Journal of Power Sources, 2017, 359: 592–600

Dai H, Zhu L, Zhu J, et al. Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard-cased batteries. Journal of Power Sources, 2015, 293: 351–365

Raijmakers L H, Danilov D L, van Lammeren J P, et al. Non-zero intercept frequency: An accurate method to determine the integral temperature of Li-ion batteries. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3168–3178

Lee K T, Dai M J, Chuang C C. Temperature-compensated model for lithium-ion polymer batteries with extended Kalman filter stateof- charge estimation for an implantable charger. IEEE Transactions on Industrial Electronics, 2018, 65(1): 589–596

Mehne J, Nowak W. Improving temperature predictions for Li-ion batteries: Data assimilation with a stochastic extension of a physically-based, thermo-electrochemical model. Journal of Energy Storage, 2017, 12: 288–296

Guo M, Kim G H, White R E. A three-dimensional multi-physics model for a Li-ion battery. Journal of Power Sources, 2013, 240: 80–94

Jeon D H, Baek S M. Thermal modeling of cylindrical lithium ion battery during discharge cycle. Energy Conversion and Management, 2011, 52(8–9): 2973–2981

Jaguemont J, Omar N, Martel F, et al. Streamline threedimensional thermal model of a lithium titanate pouch cell battery in extreme temperature conditions with module simulation. Journal of Power Sources, 2017, 367: 24–33

Lin X, Perez H E, Siegel J B, et al. Online parameterization of lumped thermal dynamics in cylindrical lithium ion batteries for core temperature estimation and health monitoring. IEEE Transactions on Control Systems Technology, 2013, 21(5): 1745–1755

Shah K, Vishwakarma V, Jain A. Measurement of multiscale thermal transport phenomena in Li-ion cells: A review. Journal of Electrochemical Energy Conversion and Storage, 2016, 13(3): 030801

Chen D, Jiang J, Li X, et al. Modeling of a pouch lithium ion battery using a distributed parameter equivalent circuit for internal non-uniformity analysis. Energies, 2016, 9(11): 865

Muratori M, Canova M, Guezennec Y, et al. A reduced-order model for the thermal dynamics of Li-ion battery cells. IFAC Proceedings Volumes, 2010, 43(7): 192–197

Kim Y, Mohan S, Siegel S J, et al. The estimation of temperature distribution in cylindrical battery cells under unknown cooling conditions. IEEE Transactions on Control Systems Technology, 2014, 22(6): 2277–2286

Hu X, Asgari S, Yavuz I, et al. A transient reduced order model for battery thermal management based on singular value decomposition. In: Proceedings of 2014 IEEE Energy Conversion Congress and Exposition (ECCE). Pittsburgh: IEEE, 2014, 3971–3976

Lin X, Perez H E, Mohan S, et al. A lumped-parameter electrothermal model for cylindrical batteries. Journal of Power Sources, 2014, 257: 1–11

Perez H, Hu X, Dey S, et al. Optimal charging of Li-ion batteries with coupled electro-thermal-aging dynamics. IEEE Transactions on Vehicular Technology, 2017, 66(9): 7761–7770

Dey S, Ayalew B. Real-time estimation of lithium-ion concentration in both electrodes of a lithium-ion battery cell utilizing electrochemical-thermal coupling. Journal of Dynamic Systems, Measurement, and Control, 2017, 139(3): 031007

Goutam S, Nikolian A, Jaguemont J, et al. Three-dimensional electro-thermal model of Li-ion pouch cell: Analysis and comparison of cell design factors and model assumptions. Applied Thermal Engineering, 2017, 126: 796–808

Jiang J, Ruan H, Sun B, et al. A reduced low-temperature electrothermal coupled model for lithium-ion batteries. Applied Energy, 2016, 177: 804–816

Basu S, Hariharan K S, Kolake SM, et al. Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system. Applied Energy, 2016, 181: 1–13

Xiong R, Cao J, Yu Q, et al. Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access: Practical Innovations, Open Solutions, 2017, PP(99): 1

Baccouche I, Jemmali S, Manai B, et al. Improved OCV model of a Li-ion NMC battery for online SOC estimation using the extended Kalman filter. Energies, 2017, 10(6): 764

Lin C, Yu Q, Xiong R, et al. A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries. Applied Energy, 2017, 205: 892–902

Grandjean T R, McGordon A, Jennings P A. Structural identifiability of equivalent circuit models for Li-ion batteries. Energies, 2017, 10(1): 90

Tang S X, Camacho-Solorio L, Wang Y, et al. State-of-charge estimation from a thermal-electrochemical model of lithium-ion batteries. Automatica, 2017, 83: 206–219

MathSciNet   MATH   Google Scholar  

Li J, Wang L, Lyu C, et al. State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack. Energy, 2017, 133: 572–583

Wang Y, Zhang C, Chen Z. On-line battery state-of-charge estimation based on an integrated estimator. Applied Energy, 2017, 185: 2026–2032

Acuña D E, Orchard ME. Particle-filtering-based failure prognosis via sigma-points: Application to lithium-ion battery state-of-charge monitoring. Mechanical Systems and Signal Processing, 2017, 85: 827–848

Zou C, Manzie C, Nešić D, et al. Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery. Journal of Power Sources, 2016, 335: 121–130

Xiong B, Zhao J, Su Y, et al. State of charge estimation of vanadium redox flow battery based on sliding mode observer and dynamic model including capacity fading factor. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1658–1667

Ye M, Guo H, Cao B. A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter. Applied Energy, 2017, 190: 740–748

Arabmakki E, Kantardzic M. SOM-based partial labeling of imbalanced data stream. Neurocomputing, 2017, 262: 120–133

Roscher M A, Assfalg J, Bohlen O S. Detection of utilizable capacity deterioration in battery systems. IEEE Transactions on Vehicular Technology, 2011, 60(1): 98–103

Coleman M, Hurley W G, Lee C K. An improved battery characterization method using a two-pulse load test. IEEE Transactions on Energy Conversion, 2008, 23(2): 708–713

Zhang J, Lee J. A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 2011, 196(15): 6007–6014

Jiang J, Lin Z, Ju Q, et al. Electrochemical impedance spectra for lithium-ion battery ageing considering the rate of discharge ability. Energy Procedia, 2017, 105: 844–849

Mingant R, Bernard J, Sauvant-Moynot V. Novel state-of-health diagnostic method for Li-ion battery in service. Applied Energy, 2016, 183: 390–398

Xiong R, Tian J, Mu H, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries. Applied Energy, 2017, 207: 372–383

Berecibar M, Gandiaga I, Villarreal I, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable & Sustainable Energy Reviews, 2016, 56: 572–587

Bi J, Zhang T, Yu H, et al. State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter. Applied Energy, 2016, 182: 558–568

Wang D, Yang F, Tsui K L, et al. Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Transactions on Instrumentation and Measurement, 2016, 65 (6): 1282–1291

Gholizadeh M, Salmasi F R. Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model. IEEE Transactions on Industrial Electronics, 2014, 61(3): 1335–1344

Plett G L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation. Journal of Power Sources, 2006, 161(2): 1356–1368

Remmlinger J, Buchholz M, Soczka-Guth T, et al. On-board stateof- health monitoring of lithium-ion batteries using linear parameter- varying models. Journal of Power Sources, 2013, 239: 689–695

Kim I S. A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer. IEEE Transactions on Power Electronics, 2010, 25(4): 1013–1022

Hu C, Youn B D, Chung J. A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy, 2012, 92: 694–704

Du J, Liu Z, Wang Y, et al. An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles. Control Engineering Practice, 2016, 54: 81–90

Wang J, Liu P, Hicks-Garner J, et al. Cycle-life model for graphite- LiFePO4 cells. Journal of Power Sources, 2011, 196(8): 3942–3948

Todeschini F, Onori S, Rizzoni G. An experimentally validated capacity degradation model for Li-ion batteries in PHEVs applications. IFAC Proceedings Volumes, 2012, 45(20): 456–461

Omar N, Monem M A, Firouz Y, et al. Lithium iron phosphate based battery—Assessment of the aging parameters and development of cycle life model. Applied Energy, 2014, 113: 1575–1585

Ecker M, Gerschler J B, Vogel J, et al. Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data. Journal of Power Sources, 2012, 215: 248–257

Suri G, Onori S. A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries. Energy, 2016, 96: 644–653

Ouyang M, Feng X, Han X, et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery. Applied Energy, 2016, 165(C): 48–59

Gao Y, Jiang J, Zhang C, et al. Lithium-ion battery aging mechanisms and life model under different charging stresses. Journal of Power Sources, 2017, 356: 103–114

Wu L, Fu X, Guan Y. Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies. Applied Sciences, 2016, 6(6): 166

Rezvanizaniani S M, Liu Z, Chen Y, et al. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of Power Sources, 2014, 256: 110–124

Nuhic A, Terzimehic T, Soczka-Guth T, et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources, 2013, 239: 680–688

Klass V, Behm M, Lindbergh G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. Journal of Power Sources, 2014, 270: 262–272

Hu C, Jain G, Zhang P, et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Applied Energy, 2014, 129: 49–55

You G, Park S, Oh D. Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach. Applied Energy, 2016, 176: 92–103

Hu C, Jain G, Schmidt C, et al. Online estimation of lithium-ion battery capacity using sparse Bayesian learning. Journal of Power Sources, 2015, 289: 105–113

Ng S S Y, Xing Y, Tsui K L. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Applied Energy, 2014, 118: 114–123

Liu D, Zhou J, Liao H, et al. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2015, 45(6): 915–928

Saha B, Goebel K, Poll S, et al. Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 2009, 58(2): 291–296

He W, Williard N, Osterman M, et al. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 2011, 196(23): 10314–10321

Zhang G, Ge S, Xu T, et al. Rapid self-heating and internal temperature sensing of lithium-ion batteries at low temperatures. Electrochimica Acta, 2016, 218: 149–155

Martinez-Cisneros C, Antonelli C, Levenfeld B, et al. Evaluation of polyolefin-based macroporous separators for high temperature Li-ion batteries. Electrochimica Acta, 2016, 216: 68–78

Li Z, Zhang J, Wu B, et al. Examining temporal and spatial variations of internal temperature in large-format laminated battery with embedded thermocouples. Journal of Power Sources, 2013, 241: 536–553

Lee C Y, Lee S J, Tang MS, et al. In situ monitoring of temperature inside lithium-ion batteries by flexible micro temperature sensors. Sensors (Basel), 2011, 11(12): 9942–9950

Kim Y, Mohan S, Siegel J B, et al. The estimation of temperature distribution in cylindrical battery cells under unknown cooling conditions. IEEE Transactions on Control Systems Technology, 2014, 22(6): 2277–2286

Samadani E, Farhad S, Scott W, et al. Empirical modeling of lithium-ion batteries based on electrochemical impedance spectroscopy tests. Electrochimica Acta, 2015, 160: 169–177

Srinivasan R, Carkhuff B G, Butler M H, et al. Instantaneous measurement of the internal temperature in lithium-ion rechargeable cells. Electrochimica Acta, 2011, 56(17): 6198–6204

Srinivasan R. Monitoring dynamic thermal behavior of the carbon anode in a lithium-ion cell using a four-probe technique. Journal of Power Sources, 2012, 198: 351–358

Zhu J G, Sun Z C, Wei X Z, et al. A new lithium-ion battery internal temperature on-line estimate method based on electrochemical impedance spectroscopy measurement. Journal of Power Sources, 2015, 274: 990–1004

Raijmakers L H, Danilov D L, van Lammeren J P M, et al. Nonzero intercept frequency: An accurate method to determine the integral temperature of Li-ion batteries. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3168–3178

Beelen H, Raijmakers L, Donkers M, et al. A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries. Applied Energy, 2016, 175: 128–140

Liu K, Li K, Deng J. A novel hybrid data-driven method for Li-ion battery internal temperature estimation. In: Proceedings of 2016 UKACC 11th International Conference on Control (CONTROL). Belfast: IEEE, 2016

Bizeray A, Zhao S, Duncan S, et al. Lithium-ion battery thermalelectrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter. Journal of Power Sources, 2015, 296: 400–412

Zhang C, Li K, Deng J. Real-time estimation of battery internal temperature based on a simplified thermoelectric model. Journal of Power Sources, 2016, 302: 146–154

Berndt D. Maintenance-free Batteries: Lead-acid , Nickel-cadmium, Nickel-metal Hydride. Taunton: Research Studies Press, 1997

Hua A C C, Syue B Z W. Charge and discharge characteristics of lead-acid battery and LiFePO 4 battery. In: Proceedings of 2010 International Power Electronics Conference (IPEC). Sapporo: IEEE, 2010, 1478–1483

Notten P, Veld J H G O, Beek J R G. Boostcharging Li-ion batteries: A challenging new charging concept. Journal of Power Sources, 2005, 145(1): 89–94

Kim T H, Park J S, Chang S K, et al. The current move of lithium ion batteries towards the next phase. Advanced Energy Materials, 2012, 2(7): 860–872

Li L, Tang X, Qu Y, et al. CC-CV charge protocol based on spherical diffusion model. Journal of Central South University of Technology, 2011, 18(2): 319–322

Liu K, Li K, Yang Z, et al. Battery optimal charging strategy based on a coupled thermoelectric model. In: Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver: IEEE, 2016, 5084–5091

Cope R C, Podrazhansky Y. The art of battery charging. In: Proceedings of the Fourteenth Annual Battery Conference on Applications and Advances. Long Beach: IEEE, 1999, 233–235

Ikeya T, Sawada N, Takagi S, et al. Multi-step constant-current charging method for electric vehicle, valve-regulated, lead/acid batteries during night time for load-levelling. Journal of Power Sources, 1998, 75(1): 101–107

Ikeya T, Sawada N, Murakami J, et al. Multi-step constant-current charging method for an electric vehicle nickel/metal hydride battery with high-energy efficiency and long cycle life. Journal of Power Sources, 2002, 105(1): 6–12

Al-Haj Hussein A, Batarseh I. A review of charging algorithms for nickel and lithium battery chargers. IEEE Transactions on Vehicular Technology, 2011, 60(3): 830–838

Lee K T, Chuang C C, Wang Y H, et al. A low temperature increase transcutaneous battery charger for implantable medical devices. Journal of Mechanics in Medicine and Biology, 2016, 16(5): 1650069

Lee Y D, Park S Y. Rapid charging strategy in the constant voltage mode for a high power Li-ion battery. In: Proceedings of 2013 IEEE Energy Conversion Congress and Exposition. Denver: IEEE, 2013, 4725–4731

Yong S O, Rahim N A. Development of on-off duty cycle control with zero computational algorithm for CC-CV Li ion battery charger. In: Proceedings of IEEE Conference on Clean Energy and Technology (CEAT). Lankgkawi: IEEE, 2013, 422–426

Abdollahi A, Han X, Avvari G V, et al. Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model. Journal of Power Sources, 2016, 303: 388–398

Hsieh G C, Chen L R, Huang K S. Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller. IEEE Transactions on Industrial Electronics, 2001, 48(3): 585–593

Liu K, Li K, Yang Z, et al. An advanced lithium-ion battery optimal charging strategy based on a coupled thermoelectric model. Electrochimica Acta, 2017, 225: 330–344

Liu K, Li K, Ma H, et al. Multi-objective optimization of charging patterns for lithium-ion battery management. Energy Conversion and Management, 2018, 159: 151–162

He L, Kim E, Shin K G. Aware charging of lithium-ion battery cells. In: Proceedings of 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS). Vienna: IEEE, 2016, 1–10

Chen L R. PLL-based battery charge circuit topology. IEEE Transactions on Industrial Electronics, 2004, 51(6): 1344–1346

MathSciNet   Google Scholar  

Chen L R, Chen J J, Chu N Y, et al. Current-pumped battery charger. IEEE Transactions on Industrial Electronics, 2008, 55(6): 2482–2488

Asadi H, Kaboli S H A, Mohammadi A, et al. Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique. In: Proceedings of Fourth International Conference on Machine Vision (ICMV 11). SPIE, 2012, 834939

Wang S C, Chen Y L, Liu Y H, et al. A fast-charging pattern search for Li-ion batteries with fuzzy-logic-based Taguchi method. In: Proceedings of 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA). Auckland: IEEE, 2015, 855–859

Liu C L, Wang S C, Chiang S S, et al. PSO-based fuzzy logic optimization of dual performance characteristic indices for fast charging of lithium-ion batteries. In: Proceedings of 2013 IEEE 10th International Conference on Power Electronics and Drive Systems (PEDS). IEEE, 2013, 474–479

Wang S C, Liu Y H. A PSO-based fuzzy-controlled searching for the optimal charge pattern of Li-ion batteries. IEEE Transactions on Industrial Electronics, 2015, 62(5): 2983–2993

Liu Y H, Hsieh C H, Luo Y F. Search for an optimal five-step charging pattern for Li-ion batteries using consecutive orthogonal arrays. IEEE Transactions on Energy Conversion, 2011, 26(2): 654–661

Vo T T, Chen X, Shen W, et al. New charging strategy for lithiumion batteries based on the integration of Taguchi method and state of charge estimation. Journal of Power Sources, 2015, 273: 413–422

Liu W, Sun X, Wu H, et al. A multistage current charging method for Li-ion battery bank considering balance of internal consumption and charging speed. In: Proceedings of IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia). Hefei: IEEE, 2016, 1401–1406

Khan A B, Pham V L, Nguyen T T, et al. Multistage constantcurrent charging method for Li-ion batteries. In: Proceedings of IEEE Conference and Expo on Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). Busan: IEEE, 2016, 381–385

Chen Z, Xia B, Mi C C, et al. Loss-minimization-based charging strategy for lithium-ion battery. IEEE Transactions on Industry Applications, 2015, 51(5): 4121–4129

Wu X, Shi W, Du J. Multi-objective optimal charging method for lithium-ion batteries. Energies, 2017, 10(9): 1271

Liu K, Li K, Zhang C. Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model. Journal of Power Sources, 2017, 347: 145–158

Xavier M A, Trimboli M S. Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models. Journal of Power Sources, 2015, 285: 374–384

Zou C, Hu X, Wei Z, et al. Electrochemical estimation and control for lithium-ion battery health-aware fast charging. IEEE Transactions on Industrial Electronics, 2017, PP(99): 1

Zhang C, Jiang J, Gao Y, et al. Charging optimization in lithiumion batteries based on temperature rise and charge time. Applied Energy, 2017, 194: 569–577

Ma H, You P, Liu K, et al. Optimal battery charging strategy based on complex system optimization. In: Li K, Xue Y, Cui S, et al., eds. Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. LSMS 2017, ICSEE 2017. Communications in Computer and Information Science, Vol. 763. Singapore: Springer, 2017, 371–378

Hu X, Li S, Peng H, et al. Charging time and loss optimization for LiNMC and LiFePO 4 batteries based on equivalent circuit models. Journal of Power Sources, 2013, 239: 449–457

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Acknowledgements

This work was financially supported by UK EPSRC under the ‘Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE) project EP/L001063/1 and NSFC under grants Nos. 61673256, 61533010 and 61640316. Kailong Liu would like to thank the EPSRC for sponsoring his research.

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Liu, K., Li, K., Peng, Q. et al. A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng. 14 , 47–64 (2019). https://doi.org/10.1007/s11465-018-0516-8

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Received : 30 September 2017

Accepted : 09 January 2018

Published : 02 April 2018

Issue Date : March 2019

DOI : https://doi.org/10.1007/s11465-018-0516-8

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• battery management system
• battery modelling
• battery state estimation
• battery charging

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