A brief review on key technologies in the battery management system of electric vehicles
Kailong LIU1, Kang LI1(), Qiao PENG2, Cheng ZHANG3
1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT9 5AH Belfast, UK 2. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China 3. IDL, Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK
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.
. [J]. Frontiers of Mechanical Engineering, 2019, 14(1): 47-64.
Kailong LIU, Kang LI, Qiao PENG, Cheng ZHANG. A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng., 2019, 14(1): 47-64.
1) High temperature can improve charging speed but damage to battery lifetime; 2) charging is dangerous at pretty low temperature, well below freezing
Lead acid
1) Higher temperature leads to lower V-threshold by 3 mV/°C; 2) charging at 0.3 C or less below freezing
NiMH, NiCd
1) Charging acceptance decreases from 70% at 45 °C to 45% at 60 °C, respectively; 2) 0.1 C charging rate between –17 °C and 0 °C; 3) 0.3 C charging between 0 °C and 6 °C
Tab.2
Fig.4
Fig.5
Fig.6
Approach
Advantages
Disadvantages
Key elements
CC
Easy to implement
Capacity utilization is low
1) Charging constant current rate; 2) terminal condition
1) Capacity utilization is high; 2) stable terminal voltage
Difficult to balance objectives such as charging speed, energy loss, temperature variation
1) Constant current rate in CC phase; 2) constant voltage in CV phase; 3) terminal condition
MCC
1) Easy to implement; 2) easy to achieve fast charging
Difficult to balance objectives such as charging speed, capacity utilization and battery lifetime
1) The number of CC stages. 2) constant current rates for each stage.
Tab.3
Fig.7
Fig.8
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 https://doi.org/10.1016/j.jpowsour.2015.11.100
2
Rao Z, Wang S. A review of power battery thermal energy management. Renewable & Sustainable Energy Reviews, 2011, 15(9): 4554–4571 https://doi.org/10.1016/j.rser.2011.07.096
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 https://doi.org/10.1016/j.electacta.2015.11.102
4
Li J, Han Y, Zhou S. Advances in Battery Manufacturing, Services, and Management Systems.Hoboken: John Wiley-IEEE Press, 2016
5
Lu L, Han X, Li J, Hua 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 https://doi.org/10.1016/j.jpowsour.2012.10.060
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 https://doi.org/10.1016/j.jpowsour.2015.12.083
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 https://doi.org/10.1016/j.compchemeng.2015.02.007
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 https://doi.org/10.1109/ICSSE.2016.7551635
9
Mastali M, Samadani E, Farhad S, Fraser R, Fowler M. Three-dimensional multi-particle electrochemical model of LiFePO4 cells based on a resistor network methodology. Electrochimica Acta, 2016, 190: 574–587 https://doi.org/10.1016/j.electacta.2015.12.122
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 https://doi.org/10.1016/j.jpowsour.2014.12.101
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 https://doi.org/10.1109/TCST.2015.2502899
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 https://doi.org/10.1016/j.jpowsour.2017.03.061
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 https://doi.org/10.1109/TCST.2015.2446947
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 https://doi.org/10.1016/j.jpowsour.2014.10.170
15
Nejad S, Gladwin D T, Stone D A. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources, 2016, 316: 183–196 https://doi.org/10.1016/j.jpowsour.2016.03.042
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 https://doi.org/10.1016/j.jpowsour.2017.01.126
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 https://doi.org/10.1016/j.jpowsour.2016.05.015
18
Gong X, Xiong R, Mi C C. A data-driven bias-correction-method-based lithium-ion battery modeling approach for electric vehicle applications. IEEE Transactions on Industry Applications, 2016, 52(2): 1759–1765
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 https://doi.org/10.1016/j.energy.2017.07.035
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 https://doi.org/10.1016/j.energy.2016.06.130
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 https://doi.org/10.1016/j.jpowsour.2017.01.105
22
Li Y, Chattopadhyay P, Xiong S, et al.. Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy, 2016, 184: 266–275 https://doi.org/10.1016/j.apenergy.2016.10.025
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 https://doi.org/10.1016/j.jpowsour.2017.05.045
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 https://doi.org/10.1016/j.jpowsour.2015.05.087
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 doi:10.1109/TIE.2016.2516961
26
Lee K T, Dai M J, Chuang C C. Temperature-compensated model for lithium-ion polymer batteries with extended Kalman filter state-of-charge estimation for an implantable charger. IEEE Transactions on Industrial Electronics, 2018, 65(1): 589–596 https://doi.org/10.1109/TIE.2017.2721880
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 https://doi.org/10.1016/j.est.2017.05.013
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 https://doi.org/10.1016/j.enconman.2011.04.013
30
Jaguemont J, Omar N, Martel F, et al.. Streamline three-dimensional thermal model of a lithium titanate pouch cell battery in extreme temperature conditions with module simulation. Journal of Power Sources, 2017, 367: 24–33 https://doi.org/10.1016/j.jpowsour.2017.09.028
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 https://doi.org/10.1109/TCST.2012.2217143
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 https://doi.org/10.1115/1.4034413
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 doi:10.3390/en9110865
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 https://doi.org/10.3182/20100712-3-DE-2013.00190
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 https://doi.org/10.1109/TCST.2014.2309492
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 https://doi.org/10.1109/ECCE.2014.6953941
37
Lin X, Perez H E, Mohan S, et al.. A lumped-parameter electro-thermal model for cylindrical batteries. Journal of Power Sources, 2014, 257: 1–11 https://doi.org/10.1016/j.jpowsour.2014.01.097
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 https://doi.org/10.1109/TVT.2017.2676044
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 https://doi.org/10.1115/1.4034801
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 https://doi.org/10.1016/j.applthermaleng.2017.07.206
41
Jiang J, Ruan H, Sun B, et al.. A reduced low-temperature electro-thermal coupled model for lithium-ion batteries. Applied Energy, 2016, 177: 804–816 https://doi.org/10.1016/j.apenergy.2016.05.153
42
Basu S, Hariharan K S, Kolake S M, et al.. Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system. Applied Energy, 2016, 181: 1–13 https://doi.org/10.1016/j.apenergy.2016.08.049
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 doi:10.1109/ACCESS.2017.2780258
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 https://doi.org/10.3390/en10060764
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 https://doi.org/10.1016/j.apenergy.2017.08.124
46
Grandjean T R, McGordon A, Jennings P A. Structural identifiability of equivalent circuit models for Li-ion batteries. Energies, 2017, 10(1): 90 https://doi.org/10.3390/en10010090
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 https://doi.org/10.1016/j.automatica.2017.06.030
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 https://doi.org/10.1016/j.energy.2017.05.158
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 https://doi.org/10.1016/j.apenergy.2015.09.015
50
Acuña D E, Orchard M E. 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 https://doi.org/10.1016/j.ymssp.2016.08.029
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 https://doi.org/10.1016/j.jpowsour.2016.10.040
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 https://doi.org/10.1109/TSTE.2017.2699288
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 https://doi.org/10.1016/j.apenergy.2016.12.133
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 https://doi.org/10.1109/TVT.2010.2090370
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 doi:10.1109/TEC.2007.914329
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 https://doi.org/10.1016/j.egypro.2017.03.399
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 https://doi.org/10.1016/j.apenergy.2016.08.118
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 https://doi.org/10.1016/j.apenergy.2017.05.124
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 https://doi.org/10.1016/j.rser.2015.11.042
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 https://doi.org/10.1016/j.apenergy.2016.08.138
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 https://doi.org/10.1109/TIM.2016.2534258
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 https://doi.org/10.1109/TIE.2013.2259779
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 https://doi.org/10.1016/j.jpowsour.2006.06.003
66
Remmlinger J, Buchholz M, Soczka-Guth T, et al.. On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models. Journal of Power Sources, 2013, 239: 689–695 https://doi.org/10.1016/j.jpowsour.2012.11.102
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 https://doi.org/10.1109/TPEL.2009.2034966
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 https://doi.org/10.1016/j.apenergy.2011.08.002
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 https://doi.org/10.1016/j.conengprac.2016.05.014
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 doi:10.1016/j.jpowsour.2010.11.134
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
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 doi:10.1016/j.apenergy.2013.09.003
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 https://doi.org/10.1016/j.jpowsour.2012.05.012
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 https://doi.org/10.1016/j.apenergy.2015.12.063
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 https://doi.org/10.1016/j.jpowsour.2017.04.084
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 https://doi.org/10.3390/app6060166
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 https://doi.org/10.1016/j.jpowsour.2014.01.085
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 https://doi.org/10.1016/j.jpowsour.2012.11.146
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 https://doi.org/10.1016/j.jpowsour.2014.07.116
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 doi:10.1016/j.apenergy.2014.04.077
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 https://doi.org/10.1016/j.apenergy.2016.05.051
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 https://doi.org/10.1016/j.jpowsour.2015.04.166
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 https://doi.org/10.1016/j.apenergy.2013.12.020
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 https://doi.org/10.1109/TSMC.2015.2389757
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 https://doi.org/10.1109/TIM.2008.2005965
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 https://doi.org/10.1016/j.jpowsour.2011.08.040
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 https://doi.org/10.1016/j.electacta.2016.09.117
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 https://doi.org/10.1016/j.electacta.2016.08.105
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 https://doi.org/10.1016/j.jpowsour.2013.04.117
91
Lee C Y, Lee S J, Tang M S, et al.. In situ monitoring of temperature inside lithium-ion batteries by flexible micro temperature sensors. Sensors (Basel), 2011, 11(12): 9942–9950 https://doi.org/10.3390/s111009942
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 https://doi.org/10.1109/TCST.2014.2309492
93
Lin X, Perez H E, Mohan S, et al.. A lumped-parameter electro-thermal model for cylindrical batteries. Journal of Power Sources, 2014, 257: 1–11 https://doi.org/10.1016/j.jpowsour.2014.01.097
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 https://doi.org/10.1109/TCST.2012.2217143
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 https://doi.org/10.1016/j.electacta.2015.02.021
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 https://doi.org/10.1016/j.electacta.2011.03.136
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 https://doi.org/10.1016/j.jpowsour.2011.09.077
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 https://doi.org/10.1016/j.jpowsour.2014.10.182
99
Raijmakers L H, Danilov D L, van Lammeren J P M, 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 https://doi.org/10.1109/TIE.2016.2516961
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 https://doi.org/10.1016/j.apenergy.2016.04.103
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 https://doi.org/10.1109/CONTROL.2016.7737560
102
Bizeray A, Zhao S, Duncan S, et al.. Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter. Journal of Power Sources, 2015, 296: 400–412 https://doi.org/10.1016/j.jpowsour.2015.07.019
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 https://doi.org/10.1016/j.jpowsour.2015.10.052
104
Berndt D. Maintenance-free Batteries: Lead-acid, Nickel-cadmium, Nickel-metal Hydride.Taunton: Research Studies Press, 1997
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 https://doi.org/10.1109/IPEC.2010.5544506
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 https://doi.org/10.1016/j.jpowsour.2004.12.038
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 https://doi.org/10.1002/aenm.201200028
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 https://doi.org/10.1007/s11771-011-0698-2
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 https://doi.org/10.1109/CEC.2016.7748334
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 https://doi.org/10.1109/BCAA.1999.795996
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 https://doi.org/10.1016/S0378-7753(98)00102-5
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 https://doi.org/10.1016/S0378-7753(01)00907-7
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 https://doi.org/10.1109/TVT.2011.2106527
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 https://doi.org/10.1142/S021951941650069X
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 https://doi.org/10.1109/ECCE.2013.6647335
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 https://doi.org/10.1109/CEAT.2013.6775668
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 https://doi.org/10.1016/j.jpowsour.2015.02.075
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 https://doi.org/10.1109/41.925585
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 https://doi.org/10.1016/j.electacta.2016.12.129
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 (in press)
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 https://doi.org/10.1109/ICCPS.2016.7479067
122
Chen L R. PLL-based battery charge circuit topology. IEEE Transactions on Industrial Electronics, 2004, 51(6): 1344–1346 https://doi.org/10.1109/TIE.2004.837891
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 https://doi.org/10.1109/TIE.2008.921685
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
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 https://doi.org/10.1109/ICIEA.2015.7334230
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
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 https://doi.org/10.1109/TIE.2014.2363049
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 https://doi.org/10.1109/TEC.2010.2103077
129
Vo T T, Chen X, Shen W, et al.. New charging strategy for lithium-ion batteries based on the integration of Taguchi method and state of charge estimation. Journal of Power Sources, 2015, 273: 413–422 https://doi.org/10.1016/j.jpowsour.2014.09.108
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 https://doi.org/10.1109/IPEMC.2016.7512495
131
Khan A B, Pham V L, Nguyen T T, et al.. Multistage constant-current 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 https://doi.org/10.1109/ITEC-AP.2016.7512982
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 https://doi.org/10.1109/TIA.2015.2417118
133
Wu X, Shi W, Du J. Multi-objective optimal charging method for lithium-ion batteries. Energies, 2017, 10(9): 1271 https://doi.org/10.3390/en10091271
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 https://doi.org/10.1016/j.jpowsour.2017.02.039
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 https://doi.org/10.1016/j.jpowsour.2015.03.074
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 doi:10.1109/TIE.2017.2772154
137
Zhang C, Jiang J, Gao Y, et al.. Charging optimization in lithium-ion batteries based on temperature rise and charge time. Applied Energy, 2017, 194: 569–577 https://doi.org/10.1016/j.apenergy.2016.10.059
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
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 https://doi.org/10.1016/j.jpowsour.2013.03.157
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 https://doi.org/10.1109/TVT.2017.2676044
