Skip to main content

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

Abstract

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.

References

  1. 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 

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

    Google Scholar 

  3. 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

    Google Scholar 

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

    Google Scholar 

  5. 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

    Google Scholar 

  6. 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

    Google Scholar 

  7. 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

    Google Scholar 

  8. 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

    Google Scholar 

  9. 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

    Google Scholar 

  10. 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

    Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Google Scholar 

  13. 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

    Google Scholar 

  14. 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

    Google Scholar 

  15. 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

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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

    Google Scholar 

  18. 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

    Google Scholar 

  19. 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

    Google Scholar 

  20. 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

    Google Scholar 

  21. 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

    Google Scholar 

  22. 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

    Google Scholar 

  23. 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

    Google Scholar 

  24. 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

    Google Scholar 

  25. 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

    Google Scholar 

  26. 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

    Google Scholar 

  27. 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

    Google Scholar 

  28. 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

    Google Scholar 

  29. 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

    Google Scholar 

  30. 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

    Google Scholar 

  31. 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

    Google Scholar 

  32. 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

    Google Scholar 

  33. 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

    Google Scholar 

  34. 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

    Google Scholar 

  35. 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

    Google Scholar 

  36. 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

    Google Scholar 

  37. 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

    Google Scholar 

  38. 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

    Google Scholar 

  39. 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

    Google Scholar 

  40. 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

    Google Scholar 

  41. 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

    Google Scholar 

  42. 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

    Google Scholar 

  43. 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

    Google Scholar 

  44. 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

    Google Scholar 

  45. 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

    Google Scholar 

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

    Google Scholar 

  47. 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 

  48. 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

    Google Scholar 

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

    Google Scholar 

  50. 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

    Google Scholar 

  51. 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

    Google Scholar 

  52. 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

    Google Scholar 

  53. 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

    Google Scholar 

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

    Google Scholar 

  55. 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

    Google Scholar 

  56. 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

    Google Scholar 

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

    Google Scholar 

  58. 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

    Google Scholar 

  59. 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

    Google Scholar 

  60. 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

    Google Scholar 

  61. 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

    Google Scholar 

  62. 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

    Google Scholar 

  63. 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

    Google Scholar 

  64. 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

    Google Scholar 

  65. 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

    Google Scholar 

  66. 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

    Google Scholar 

  67. 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

    Google Scholar 

  68. 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

    Google Scholar 

  69. 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

    Google Scholar 

  70. 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

    Google Scholar 

  71. 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

    Google Scholar 

  72. 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

    Google Scholar 

  73. 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

    Google Scholar 

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

    Google Scholar 

  75. 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

    Google Scholar 

  76. 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

    Google Scholar 

  77. 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

    Google Scholar 

  78. 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

    Google Scholar 

  79. 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

    Google Scholar 

  80. 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

    Google Scholar 

  81. 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

    Google Scholar 

  82. 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

    Google Scholar 

  83. 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

    Google Scholar 

  84. 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

    Google Scholar 

  85. 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

    Google Scholar 

  86. 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

    Google Scholar 

  87. 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

    Google Scholar 

  88. 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

    Google Scholar 

  89. 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

    Google Scholar 

  90. 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

    Google Scholar 

  91. 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

    Google Scholar 

  92. 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

    Google Scholar 

  93. 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

    Google Scholar 

  94. 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

    Google Scholar 

  95. 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

    Google Scholar 

  96. 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

    Google Scholar 

  97. 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

    Google Scholar 

  98. 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

    Google Scholar 

  99. 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

    Google Scholar 

  100. 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

    Google Scholar 

  101. 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

    Google Scholar 

  102. 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

    Google Scholar 

  103. 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

    Google Scholar 

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

    Google Scholar 

  105. Hua A C C, Syue B Z W. Charge and discharge characteristics of lead-acid battery and LiFePO4 battery. In: Proceedings of 2010 International Power Electronics Conference (IPEC). Sapporo: IEEE, 2010, 1478–1483

    Google Scholar 

  106. 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

    Google Scholar 

  107. 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

    Google Scholar 

  108. 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

    Google Scholar 

  109. 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

    Google Scholar 

  110. 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

    Google Scholar 

  111. 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

    Google Scholar 

  112. 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

    Google Scholar 

  113. 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

    Google Scholar 

  114. 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

    Google Scholar 

  115. 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

    Google Scholar 

  116. 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

    Google Scholar 

  117. 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

    Google Scholar 

  118. 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

    Google Scholar 

  119. 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

    Google Scholar 

  120. 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

    Google Scholar 

  121. 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

    Google Scholar 

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

    MathSciNet  Google Scholar 

  123. 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

    Google Scholar 

  124. 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

    Google Scholar 

  125. 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

    Google Scholar 

  126. 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

    Google Scholar 

  127. 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

    Google Scholar 

  128. 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

    Google Scholar 

  129. 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

    Google Scholar 

  130. 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

    Google Scholar 

  131. 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

    Google Scholar 

  132. 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

    Google Scholar 

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

    Google Scholar 

  134. 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

    Google Scholar 

  135. 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

    Google Scholar 

  136. 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

    Google Scholar 

  137. 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

    Google Scholar 

  138. 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

    Google Scholar 

  139. Hu X, Li S, Peng H, et al. Charging time and loss optimization for LiNMC and LiFePO4 batteries based on equivalent circuit models. Journal of Power Sources, 2013, 239: 449–457

    Google Scholar 

  140. 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

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kang Li.

Additional information

This article is published with open access at link.springer.com and journal.hep.com.cn

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the appropriate credit is given to the original author(s) and the source, and a link is provided to the Creative Commons license, indicating if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

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