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

Open Access
Review Article

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.

Keywords

battery management system battery modelling battery state estimation battery charging 

Notes

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.

References

  1. 1.
    Abada S, Marlair G, Lecocq A, et al. Safety focused modeling of lithium-ion batteries: A review. Journal of Power Sources, 2016, 306: 178–192CrossRefGoogle Scholar
  2. 2.
    Rao Z, Wang S. A review of power battery thermal energy management. Renewable & Sustainable Energy Reviews, 2011, 15 (9): 4554–4571CrossRefGoogle Scholar
  3. 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: 78–84CrossRefGoogle Scholar
  4. 4.
    Li J, Han Y, Zhou S. Advances in Battery Manufacturing, Services, and Management Systems. Hoboken: John Wiley-IEEE Press, 2016CrossRefGoogle Scholar
  5. 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: 272–288CrossRefGoogle Scholar
  6. 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: 86–97CrossRefGoogle Scholar
  7. 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: 87–97CrossRefGoogle Scholar
  8. 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, 1–4Google Scholar
  9. 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: 574–587CrossRefGoogle Scholar
  10. 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: 802–813Google Scholar
  11. 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): 1594–1609CrossRefGoogle Scholar
  12. 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: 258–271CrossRefGoogle Scholar
  13. 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): 384–399Google Scholar
  14. 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: 899–906CrossRefGoogle Scholar
  15. 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: 183–196CrossRefGoogle Scholar
  16. 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: 21–29CrossRefGoogle Scholar
  17. 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: 70–78Google Scholar
  18. 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): 1759–1765Google Scholar
  19. 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: 118–132CrossRefGoogle Scholar
  20. 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: 469–480CrossRefGoogle Scholar
  21. 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: 128–140CrossRefGoogle Scholar
  22. 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: 266–275CrossRefGoogle Scholar
  23. 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: 592–600CrossRefGoogle Scholar
  24. 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: 351–365CrossRefGoogle Scholar
  25. 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): 3168–3178CrossRefGoogle Scholar
  26. 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): 589–596CrossRefGoogle Scholar
  27. 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: 288–296CrossRefGoogle Scholar
  28. 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: 80–94CrossRefGoogle Scholar
  29. 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): 2973–2981CrossRefGoogle Scholar
  30. 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: 24–33CrossRefGoogle Scholar
  31. 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): 1745–1755CrossRefGoogle Scholar
  32. 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): 030801CrossRefGoogle Scholar
  33. 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): 865CrossRefGoogle Scholar
  34. 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): 192–197CrossRefGoogle Scholar
  35. 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): 2277–2286CrossRefGoogle Scholar
  36. 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, 3971–3976CrossRefGoogle Scholar
  37. 37.
    Lin X, Perez H E, Mohan S, et al. A lumped-parameter electrothermal model for cylindrical batteries. Journal of Power Sources, 2014, 257: 1–11CrossRefGoogle Scholar
  38. 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): 7761–7770CrossRefGoogle Scholar
  39. 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): 031007CrossRefGoogle Scholar
  40. 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: 796–808CrossRefGoogle Scholar
  41. 41.
    Jiang J, Ruan H, Sun B, et al. A reduced low-temperature electrothermal coupled model for lithium-ion batteries. Applied Energy, 2016, 177: 804–816CrossRefGoogle Scholar
  42. 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: 1–13CrossRefGoogle Scholar
  43. 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): 1Google Scholar
  44. 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): 764CrossRefGoogle Scholar
  45. 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: 892–902CrossRefGoogle Scholar
  46. 46.
    Grandjean T R, McGordon A, Jennings P A. Structural identifiability of equivalent circuit models for Li-ion batteries. Energies, 2017, 10(1): 90CrossRefGoogle Scholar
  47. 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: 206–219MathSciNetMATHCrossRefGoogle Scholar
  48. 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: 572–583CrossRefGoogle Scholar
  49. 49.
    Wang Y, Zhang C, Chen Z. On-line battery state-of-charge estimation based on an integrated estimator. Applied Energy, 2017, 185: 2026–2032CrossRefGoogle Scholar
  50. 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: 827–848CrossRefGoogle Scholar
  51. 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: 121–130CrossRefGoogle Scholar
  52. 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): 1658–1667CrossRefGoogle Scholar
  53. 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: 740–748CrossRefGoogle Scholar
  54. 54.
    Arabmakki E, Kantardzic M. SOM-based partial labeling of imbalanced data stream. Neurocomputing, 2017, 262: 120–133CrossRefGoogle Scholar
  55. 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): 98–103CrossRefGoogle Scholar
  56. 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): 708–713CrossRefGoogle Scholar
  57. 57.
    Zhang J, Lee J. A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 2011, 196(15): 6007–6014CrossRefGoogle Scholar
  58. 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: 844–849CrossRefGoogle Scholar
  59. 59.
    Mingant R, Bernard J, Sauvant-Moynot V. Novel state-of-health diagnostic method for Li-ion battery in service. Applied Energy, 2016, 183: 390–398CrossRefGoogle Scholar
  60. 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: 372–383CrossRefGoogle Scholar
  61. 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: 572–587CrossRefGoogle Scholar
  62. 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: 558–568CrossRefGoogle Scholar
  63. 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): 1282–1291CrossRefGoogle Scholar
  64. 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): 1335–1344CrossRefGoogle Scholar
  65. 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): 1356–1368CrossRefGoogle Scholar
  66. 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: 689–695CrossRefGoogle Scholar
  67. 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): 1013–1022CrossRefMathSciNetGoogle Scholar
  68. 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: 694–704CrossRefGoogle Scholar
  69. 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: 81–90CrossRefGoogle Scholar
  70. 70.
    Wang J, Liu P, Hicks-Garner J, et al. Cycle-life model for graphite- LiFePO4 cells. Journal of Power Sources, 2011, 196(8): 3942–3948CrossRefGoogle Scholar
  71. 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): 456–461CrossRefGoogle Scholar
  72. 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: 1575–1585CrossRefGoogle Scholar
  73. 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: 248–257CrossRefGoogle Scholar
  74. 74.
    Suri G, Onori S. A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries. Energy, 2016, 96: 644–653CrossRefGoogle Scholar
  75. 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): 48–59CrossRefGoogle Scholar
  76. 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: 103–114CrossRefGoogle Scholar
  77. 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): 166CrossRefGoogle Scholar
  78. 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: 110–124CrossRefGoogle Scholar
  79. 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: 680–688CrossRefGoogle Scholar
  80. 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: 262–272CrossRefGoogle Scholar
  81. 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: 49–55CrossRefGoogle Scholar
  82. 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: 92–103CrossRefGoogle Scholar
  83. 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: 105–113CrossRefGoogle Scholar
  84. 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: 114–123CrossRefGoogle Scholar
  85. 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): 915–928CrossRefGoogle Scholar
  86. 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): 291–296CrossRefGoogle Scholar
  87. 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): 10314–10321CrossRefGoogle Scholar
  88. 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: 149–155CrossRefGoogle Scholar
  89. 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: 68–78CrossRefGoogle Scholar
  90. 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: 536–553CrossRefGoogle Scholar
  91. 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): 9942–9950CrossRefGoogle Scholar
  92. 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): 2277–2286CrossRefGoogle Scholar
  93. 93.
    Lin X, Perez H E, Mohan S, et al. A lumped-parameter electrothermal model for cylindrical batteries. Journal of Power Sources, 2014, 257: 1–11CrossRefGoogle Scholar
  94. 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): 1745–1755CrossRefGoogle Scholar
  95. 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: 169–177CrossRefGoogle Scholar
  96. 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): 6198–6204CrossRefGoogle Scholar
  97. 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: 351–358CrossRefGoogle Scholar
  98. 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: 990–1004CrossRefGoogle Scholar
  99. 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): 3168–3178CrossRefGoogle Scholar
  100. 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: 128–140CrossRefGoogle Scholar
  101. 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, 2016Google Scholar
  102. 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: 400–412CrossRefGoogle Scholar
  103. 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: 146–154CrossRefGoogle Scholar
  104. 104.
    Berndt D. Maintenance-free Batteries: Lead-acid, Nickel-cadmium, Nickel-metal Hydride. Taunton: Research Studies Press, 1997Google Scholar
  105. 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, 1478–1483CrossRefGoogle Scholar
  106. 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): 89–94CrossRefGoogle Scholar
  107. 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): 860–872CrossRefGoogle Scholar
  108. 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): 319–322CrossRefGoogle Scholar
  109. 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, 5084–5091CrossRefGoogle Scholar
  110. 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, 233–235Google Scholar
  111. 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): 101–107CrossRefGoogle Scholar
  112. 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): 6–12CrossRefGoogle Scholar
  113. 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): 830–838CrossRefGoogle Scholar
  114. 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): 1650069CrossRefGoogle Scholar
  115. 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, 4725–4731CrossRefGoogle Scholar
  116. 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, 422–426Google Scholar
  117. 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: 388–398CrossRefGoogle Scholar
  118. 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): 585–593CrossRefGoogle Scholar
  119. 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: 330–344CrossRefGoogle Scholar
  120. 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: 151–162CrossRefGoogle Scholar
  121. 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, 1–10Google Scholar
  122. 122.
    Chen L R. PLL-based battery charge circuit topology. IEEE Transactions on Industrial Electronics, 2004, 51(6): 1344–1346CrossRefGoogle Scholar
  123. 123.
    Chen L R, Chen J J, Chu N Y, et al. Current-pumped battery charger. IEEE Transactions on Industrial Electronics, 2008, 55(6): 2482–2488CrossRefGoogle Scholar
  124. 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, 834939CrossRefGoogle Scholar
  125. 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, 855–859CrossRefGoogle Scholar
  126. 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, 474–479Google Scholar
  127. 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): 2983–2993CrossRefGoogle Scholar
  128. 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): 654–661CrossRefGoogle Scholar
  129. 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: 413–422CrossRefGoogle Scholar
  130. 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, 1401–1406Google Scholar
  131. 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, 381–385CrossRefGoogle Scholar
  132. 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): 4121–4129CrossRefGoogle Scholar
  133. 133.
    Wu X, Shi W, Du J. Multi-objective optimal charging method for lithium-ion batteries. Energies, 2017, 10(9): 1271CrossRefGoogle Scholar
  134. 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: 145–158CrossRefGoogle Scholar
  135. 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: 374–384CrossRefGoogle Scholar
  136. 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): 1Google Scholar
  137. 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: 569–577CrossRefGoogle Scholar
  138. 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, 371–378Google Scholar
  139. 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: 449–457CrossRefGoogle Scholar
  140. 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): 7761–7770CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

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.

Authors and Affiliations

  1. 1.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastUK
  2. 2.School of Physics and Optoelectronic EngineeringNanjing University of Information Science and TechnologyNanjingChina
  3. 3.IDL, Warwick Manufacturing GroupUniversity of WarwickCoventryUK

Personalised recommendations