Overview of State of Power Prediction Methods for Lithium-ion Batteries

doi:10.3901/JME.2022.20.361

Abstract

Abstract: With the large-scale application of lithium-ion batteries in smart grid and new energy vehicles, the accurate prediction of their charging and discharging capacity, namely peak power prediction, is very important to maintain the safe and reliable operation of the system. This paper analyzes the state of the art of state of power prediction methods for lithium-ion batteries from the single and system levels: ① For cell prediction methods, mainly including look-up table method, black box method, equivalent circuit model and electrochemical model method. The equivalent model method with multi-parameter constraint is emphatically introduced. The classification and comparative analysis of those methods are also carried out. ② For battery system, viewing from battery system model and state of power estimation methods, the state of power prediction algorithm of series and non-series battery system and the intelligent prediction method driven by big data are discussed. Moreover, the advantages and disadvantages of these methods and the application field are analyzed. ③ Combined with the development trends of next-generation cloud computing, big data, digital twin, etc., the state of power prediction methods of lithium-ionbatteries are forecasted, which provides some ideas for the development and application of battery all life cycle management technology.

Key words: state of power, state prediction, battery system, equivalent model, development trend

CLC Number:

TM912

PENG Simin, XU Lu, ZHANG Weifeng, YANG Ruixin, WANG Qianjin, CAI Xu. Overview of State of Power Prediction Methods for Lithium-ion Batteries[J]. Journal of Mechanical Engineering, 2022, 58(20): 361-378.

References

[1] KIM J，SHIN J，CHUN C，et al. Stable configuration of a li-ion series battery pack based on a screening process for improved voltage/SOC balancing[J]. IEEE Transactions on Power Electronics，2011，27(1):411-424.
[2] SUN F，RUI X，HE H，et al. Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries[J]. Applied Energy，2012，96:378-386.
[3] 刘新天，何耀，曾国建，等. 考虑温度影响的锂电池功率状态估计[J]. 电工技术学报，2016，31(13):155-163. LIU Xintian，HE Yao，ZENG Guojian，et al. State-of-power estimation for li-ion battery considering the effect of temperature[J]. Transactions of China Electrotectechnical Society，2016，31(13):155-163.
[4] 向顺. 锂电池荷电状态、健康状态以及功率状态的在线估计[D]. 成都:西南交通大学，2018. XIANG Shun. Online estimation of state of charge，state of health and state of power of lithium-ion batteries[D]. Chengdu:Southwest Jiaotong University，2018.
[5] FARMANN A，SAUER D. A comprehensive review of on-board state-of-available-power prediction techniques for lithium-ion batteries in electric vehicles[J]. Journal of Power Sources，2016，329:123-137.
[6] 顾启蒙，华旸，潘宇巍，等. 锂离子电池功率状态估计方法综述[J]. 电源技术，2019，43(9):1563-1567. GU Qimeng，HUA Yang，PAN Yuwei，et al. Review of state of power estimation methods for lithium-ion battery[J]. Chinses Journal of Power Sources，2019，43(9):1563-1567.
[7] 胡宇. 电动车动力电池功率状态预测研究[D]. 哈尔滨:哈尔滨理工大学，2012. HU Yu. Prediction status of peak power of battery on HEV[D]. Harbin:Harbin University of Science and Technology，2012.
[8] SUN F，XIONG R，HE H. Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions[J]. Journal of Power Sources，2014，259:166-176.
[9] PLETT G. High-performance battery-pack power estimation using a dynamic cell model[J]. IEEE Transactions on Vehicular Technology，2004，53(5): 1586-1593.
[10] 电动汽车用电池管理系统技术条件[M]. 国内-国家标准-国家市场监督管理总局CN-GB，2020. Technical specifications of battery management system for electric vehicles [M]. Domestic-National Standards-State Administration for Market Regulation CN-GB，2020.
[11] 金鑫娜，顾启蒙，潘宇巍，等. 锂离子动力电池SOP在线估计方法研究[J]. 电源技术，2019，43(9):1448-1452. JIN Xinna，GU Qimeng，PAN Yuwei，et al. Online state of power estimation methods for lithium-ion batteries in EV[J]. Chinses Journal of Power Sources，2019，43(9):1448-1452.
[12] 娄婷婷，郭翔，孙丙香，等. 混合动力轿车用锂离子电池峰值功率预测模型研究[J]. 国网技术学院学报，2018，21(5):19-23. LOU Tingting，GUO Xiang，SUN Bingxiang，et al. Research on lithium ion battery peak power prediction model for hybrid electric vehicles[J]. Jouranl of State Grid Technology College，2018，21(5):19-23.
[13] 戴群亮，赵丁选. 基于BP神经网络优化算法的工程车辆挡位判断的训练及仿真[J]. 机械工程学报，2002，38(11):124-127. DAI Qunliang，ZHAO Dingxuan. Training and simulation on gear position decision for vehicle based on optimal algorithm of BP newwork[J]. Jounral of Mechanical Engineering，2002，38(11):124-127.
[14] 陈长征，莫长涛. 激光准直测量中二维位置敏感器件非线性修正算法研究[J]. 机械工程学报，2004，40(4):127-130. CHEN Changzheng，MO Changtao. Study on correction of non-linearity to two-dimension position sensitive detector for laser alignment measurement[J]. Jounral of Mechanical Engineering，2004，40(4):127-130.
[15] 胡伍生，沙月进. 神经网络BP算法的误差分级迭代法[J]. 东南大学学报，2003，33(3): 376-378. HU Wusheng，SHA Yuejin. Error grade iterative method of BP neural networks[J]. Journal of Southeast University，2003，33(3):376-378.
[16] 杨易，高骏，谷正气，等. 基于GA-BP的汽车风振噪声声品质预测模型[J]. 机械工程学报，2021，57(24):241-249. YANG Yi，GAO Jun，GU Zhengqi，et al. Research on sound quality prediction model of automobile wind buffeting noise based on GA-BP[J]. Jounral of Mechanical Engineering，2021，57(24):241-249.
[17] 朱浩，张文博，邓元望，等. 基于SA+BP混合算法的动力电池放电峰值功率估算[J]. 江苏大学学报，2020，41(2):192-198. ZHU Hao，ZHANG Wenbo，DENG Yuanwang，et al. Peak power estimation of power battery discharge based on SA+BP hybrid algorithm [J]. Journal of Jiangsu University，2020，41(2): 192-198.
[18] WEI Z，MENG S，TSENG K，et al. An adaptive model for vanadium redox flow battery and its application for online peak power estimation[J]. Journal of Power Sources，2017，344:195-207.
[19] 张文博. 电动汽车动力锂离子电池峰值功率研究[D]. 长沙:湖南大学，2019. ZHANG Wenbo. The research on peak power about lithium ion battery for electric vehicles[D]. Changsha:Hunan University，2019.
[20] MASOUD V，MASOOD Y，JAMES R，et al. Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study[J]. Renewable Energy，2020，163(1):807-824.
[21] 孙丙香，高科，姜久春，等. 基于ANFIS和减法聚类的动力电池放电峰值功率预测[J]. 电工技术学报，2015，30(4):272-280. SUN Bingxiang，GAO Ke，JIANG Jiuchun，et al. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering[J]. Transactions of China Electrotectechnical Society，2015，30(4):272-280.
[22] WEI Z，MENG S，TSENG K，et al. An adaptive model for vanadium redox flow battery and its application for online peak power estimation[J]. Journal of Power Sources，2017，344:195-207.
[23] 曹铭，黄菊花，杨志平，等. 车用锂离子动力电池自适应状态联合估计研究[J]. 控制理论与应用，2020，37(9):1951-1962. CAO Ming，HUANG Juhua，YANG Zhiping，et al. Research on adaptive state of charge and state of power joint estimation algorithm of vehicle lithium ion power batteries[J]. Control Theory and Application，2020， 37(9):1951-1962.
[24] WANG J，CHEN Q，CAO B. Support vector machine based battery model for electric vehicles[J]. Energy Conversion and Management，2006，47(7):858-864.
[25] 朱大业，丁晓红，王神龙，等. 基于支持向量机模型的复杂非线性系统试验不确定评定方法[J]. 机械工程学报，2018，54(8):177-184. ZHU Daye，DING Xiaohong，WANG Shenlong，et al. Uncertainty evaluation method of complex nonlinear system test based on support vector machine model[J]. Jounral of Mechanical Engineering，2018，54(8):177-184.
[26] 郑方丹，姜久春，陈坤龙，等. 基于数据统计特性的GS-SVM电池峰值功率预测模型[J]. 电力自动化设备，2017，37(9):56-61. ZHENG Fangdan，JIANG Jiuchun，CHEN Kunlong，et al. Peak power prediction model for batteries based on data statistical characteristic and GS-SVM [J]. Electric Power Automation Equipment，2017，37(9):56-61.
[27] 舒星，刘永刚，申江卫，等. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J]. 机械工程学报，2021，57(14):118-128. SHU Xing，LIU Yonggang，SHEN Jiangwei，et al. Capacity prediction for lithium-ion batteries based on improved least squares support vector machine and Box-Cox transformation[J]. Jounral of Mechanical Engineering，2021，57(14):118-128..
[28] 顾德闯，杨永健. 基于GPU的多类支持向量机改进算法[J]. 吉林大学学报，2015，53(1):107-111. GU Dechuang，YANG Yongjian. Improvement of multiclass support vector machine based on graphic processor [J]. Journal of Jilin University，2015，53(1):107-111.
[29] 魏翔. 基于小波包分解和布谷鸟算法的最小二乘支持向量机风速预测模型的研究与应用[D]. 兰州:兰州大学，2015. WEI Xiang. Research and application on LSSVM wind speed forecasting model based on WPT and CS algotithm[J]. Lanzhou:Lanzhou University，2015.
[30] 张立民，刘峰，刘凯. 一种基于粗糙集理论的支持向量机分类算法[J]. 中国电子科学研究院学报，2012，7(3):241-245. ZHANG Limin，LIU Feng，LIU Kai. A support vector machine classification algorithm based on rough sets theory [J]. Journal of the Chinese Academy of Electronic Sciences，2012，7(3):241-245.
[31] YANG L，CAI Y，YANG Y，et al. Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles[J]. Applied Energy，2020，257:114006.
[32] FENG T，YANG L，ZHAO X，et al. Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction[J]. Journal of Power Sources，2015，281:192-203.
[33] JUAN R，NICOLÁS M，HENRY S. SoC Estimation for lithium-ion batteries: review and future challenges[J]. Electronics，2017，6(4):102-104.
[34] 裴磊. 基于平衡电压的电动汽车锂离子电池状态估计方法研究[D]. 哈尔滨:哈尔滨工业大学，2016. PEI Lei. State estimation based on equilibrium voltages for lithium-ion batteries in electric vehicles[D]. Harbin:Harbin Institute of Technology，2016.
[35] PLETT G. High-performance battery-pack power estimation using a dynamic cell model[J]. IEEE Transactions on Vehicular Technology，2004，53(5):1586-1593.
[36] 李园. 考虑温变-迟滞效应的动力电池状态估计方法研究[D]. 西安:长安大学，2019. LI Yuan. Research on state estimation of power batteries considering temperature-hysteresis effect[D]. Xi’an:Chang’an University，2019.
[37] 张彩萍，张承宁，李军求. 动力电池组峰值功率估计算法研究[J]. 系统仿真学报，2010，6(1):202-205. ZHANG Caiping，ZHANG Chengning，LI Junqiu. Research on peak power estimation for traction battery pack[J]. Journal of System Simulation，2010，6(1): 202-205.
[38] WEI Z，ZHAO J，RUI X，et al. Online estimation of power capacity with noise effect attenuation for lithium-ion battery[J]. IEEE Transactions on Industrial Electronics，2018，99(2):1-3.
[39] LAI Xin，HE Long，WANG Shuyu，et al. Co-estimation of state of charge and state of power for lithium-ion batteries based on fractional variable-order model - ScienceDirect[J]. Journal of Cleaner Production，2020，5:255-256.
[40] JIANG W，ZHANG N，LI P，et al. A temperature-based peak power capability estimation method for lithium-ion batteries[J]. Procedia Engineering，2017，187:249-256.
[41] ESFANDYARI M，ESFAHANIAN V，YAZDI M，et al. A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle[J]. Energy，2019，176:505-520.
[42] XIONG R，HE H，SUN F C，et al. Online estimation of peak power capability of li-ion batteries in electric vehicles by a hardware-in-loop approach[J]. Energies，2012，5(5):1455-1469.
[43] 张宵洋，张振福，毛顺永，等. 锂电池SOC与持续峰值SOP的联合估计[J]. 控制工程，2021，5(1):1-10. ZHANG Xiaoyang，ZHANG Zhenfu，MAO Shunyong， et al. Joint estimation of SOC and continuous peak SOP of lithium battery [J]. Control Engineering，2021 5(1):1-10.
[44] 王春雨，崔纳新，李长龙，等. 基于电热耦合模型和多参数约束的动力电池峰值功率预测[J]. 机械工程学报，2019，55(20):28-35. WANG Chunyu，CUI Naxin，LI Changlong，et al. State of power prediction based on electro-thermal battery model and multi-parameter constraints for lithium-ion battery[J]. Journal of Mechanical Engineering，2019，55(20):28-35.
[45] WANG Y，PAN R，LIU C，et al. Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation[J]. Journal of Power Sources，2018，374:12-23.
[46] GRANDJEAN T，LI L，ODIO M，WIDANAGE W. Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte[C]//2019 IEEE Vehicle Power And Propulsion Conference (VPPC)，2019，1-7.
[47] DOYLE M，FULLER T，NEWMAN J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Electrochem. Soc.，1993，140(6):1526-1533.
[48] DAO T，VYASARAYANI C，MCPHEE J. Simplification and order reduction of lithium-ion battery model based on porous-electrode theory[J]. Journal of Power Sources，2012，198:329-337.
[49] HAN X，OUYANG M，LU L，et al. Simplification of physics based electrochemical model for lithium ion battery on electric vehicle. Part II: pseudo two dimensional model simplification and state of charge estimation[J]. Journal of Power Sources，2015，278:814-825.
[50] XU M，WANG R，ZHAO P，et al. Fast charging optimization for lithium-ion batteries based on dynamic programming algorithm and electrochemical-thermal-capacity fade coupled model[J]. Journal of Power Sources，2019，438:227015-227026.
[51] SONG X，LU Y，WANG F，et al. A coupled electro chemo mechanical model for all-solid-state thin film Li-ion batteries:The effects of bending on battery performances[J]. Journal of Power Sources，2020，452:227803-227814.
[52] LIN Peng，JIN Peng，HONG Jichao，et al. Battery voltage and state of power prediction based on an improved novel polarization voltage model[J]. Energy Reports，2020，6:2299-2308.
[53] LI Weihan，FAN Yue，RINGBECK F，et al. Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression[J]. Applied Energy，2022，15:118114.
[54] MI C，MASRUR M，GAO D. Hybrid electric vehicles: principles and applications with practical perspectives[M]. 2011.
[55] DOYLE M，NEWMAN J，GOZDZ A，et al. Comparison of modeling predictions with experimental data from plastic lithium ion cells[J]. Journal of The Electrochemical Society，1996，143(6):1890-1903.
[56] 杨杰，王婷，杜春雨，等. 锂离子电池模型研究综述[J]. 储能科学与技术，2019，8(1):58-64. YANG Jie，WANG Ting，DU Chunyu，et al. Overview of the modeling of lithium-ion batteries[J]. Energy Stroage Science and Technology，2019，8(1):58-64.
[57] 黄凯，郭永芳，李志刚. 动力锂离子电池荷电状态估计综述[J]. 电源技术，2018，42(9):1398-1401. HUANG Kai，GUO Yongfang，LI Zhigang. Review of state of charge estimation methods for power lithium-ion battery[J]. Chinese Journal of Power Sources，2018，42(9):1398-1401.
[58] KIM J，SHIN J，JEON C，et al. High accuracy state-of-charge estimation of Li-Ion battery pack based on screening process[C]//IEEE Applied Power Electronics Conference and Expostion. Fort Worth，TX，USA:IEEE，2011:1984-1991.
[59] RUI X，SUN F，GONG X，et al. Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles[J]. Journal of Power Sources，2013，242:699-713.
[60] 姜君. 锂离子电池串并联成组优化研究[D]. 北京:北京交通大学，2013. JIANG Jun. Research on series-parallel grouping optimization of lithium-ion batteries[D]. Beijing:Beijing Jiaotong University，2013.
[61] RUI X，GONG X，MI C，et al. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources，2013，243:805-816.
[62] 郝宏远，何晋伟，孙柏阳，等. 基于串联DFIG的独立直流电网功率协调控制[J]. 电力电子技术，2019，53(12):66-70. HAO Hongyuan，HE Jinwei，SUN Baiyang，et al. Power coordinated control of independent direct current grid based on series DFIG[J]. Power Electronics，2019，53(12):66-70.
[63] 易军. 磷酸铁锂电池组能量管理系统研究与设计[D]. 成都:西南交通大学，2014. YI Jun. LiFePO₄ battery pack energy management system research and design[D]. Chengdu:Southwest Jiaotong University，2014.
[64] 丁明，陈忠，苏建徽，等. 可再生能源发电中的电池储能系统综述[J]. 电力系统自动化，2013，37(1):19-26. DING Ming，CHEN Zhong，SU Jianhui，et al. An overview of battery energy storage system for renewable energy generation[J]. Automation of Electric Power System，2013，37(1):19-26.
[65] 唐西胜. 储能在电力系统中的作用与运营模式[J]. 电力建设，2016，37(8):1-7. TANG Xisheng. Application and marketing mode of energy stroages in power system[J]. Electric Power Construction，2016，37(8):1-7.
[66] PENG S，CHEN C，SHI H，et al. State of charge estimation of battery energy storage systems based on adaptive unscented kalman filter with a noise statistics estimator[J]. IEEE Access，2017:13202-13212.
[67] 张彦会，孟祥虎，肖婷，等. 模糊PID自调整控制的锂电池均衡研究[J]. 电子技术应用，2015，41(10):123-125，132. ZHANG Yanhui，MENG Xianghu，XIAO Ting，et al. Equilibrium research on fuzzy PID and self-adjusting control for lithium battery[J]. Power Supply Technology and Its Application，2015，41(10):123-125，132.
[68] GAO W，ZHENG Y，OUYANG M，et al. Micro-short circuit diagnosis for series-connected lithium-ion battery packs using mean-difference model[J]. IEEE Transactions on Industrial Electronics，2019，66(3):2132-2142.
[69] 彭思敏，施刚，蔡旭，等. 基于等效电路法的大容量蓄电池系统建模与仿真[J]. 中国电机工程学报，2013，33(7):11-18，24. PENG Simin，SHI Gang，CAI Xu，et al. Modeling and simulation of large capacity battery systems based on the equivalent circuit method[J]. Proceeding of the CSEE，2013，33(7):11-18，24.
[70] 范刘洋，汪可友，张宝群，等. 考虑电池组不一致性的储能系统建模及仿真[J]. 电力系统自动化，2016，40(3):110-115. FAN Liuyang，WANG Keyou，ZHANG Baoqun，et al. Modeling and simulation of battery energy storage system considering intrinsic inconsistency[J]. Automation of Electric Power System，2016，40(3):110-115.
[71] 王佳元，孙泽昌，魏学哲，等. 基于一致性估计的车用动力蓄电池组SOC修正法[J]. 同济大学学报，2012，40(5):711-716. WANG Jiayuan，SUN Zechang，WEI Xuezhe，et al. An adaptive method for automotive traction battery pack SOC estimation based on in pack cell uniformity condition[J]. Journal of Tongji University，2012，40(5):711-716.
[72] DAI H，WEI X，SUN Z，et al. Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications[J]. Applied Energy，2012，95:227-237.
[73] ZHENG Y，GAO W，OUYANG M，et al. State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter[J]. Journal of Power Sources，2018，383:50-58.
[74] SUN F，XIONG R. A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles[J]. Journal of Power Sources，2015，9:27-37.
[75] DUBARRY M，VUILLAUME N，LIAW B. From single cell model to battery pack simulation for Li-ion batteries[J]. Journal of Power Sources，2009，186:500-507.
[76] ZHANG C，CHENG G，JU Q，et al. Study on battery pack consistency evolutions during electric vehicle operation with statistical method[J]. Energy Procedia，2017，105:3551-3556.
[77] PLETT L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs:Part 2:Simultaneous state and parameter estimation[J]. Journal of Power Sources，2006，12:5-16.
[78] PENG S，ZHU X，XING Y，et al. An adaptive state of charge estimation approach for lithium-ion series- connected battery system[J]. Journal of Power Sources，2018，392:48-59.
[79] 陈天雨，冯旭宁，欧阳明高，等. 基于模型的动力电池系统多尺度热安全设计[J]. 中国机械工程，2018，29(15):1840-1846，1874. CHEN Tianyu，FENG Xuning，OUYANG Minggao，et al. Model based multi-scal thermal safety design of traction battery system[J]. China Mechanical Engineering，2018，29(15):1840-1846，1874.
[80] 陈强，李香龙，李雪，等. 单体参数差异对串联电池组性能影响研究[J]. 电源技术，2013，37(11):1947-1950. CHEN Qiang，LI Xianglong，LI Xue，et al. Influence of cell parameter differences upon series battery packs[J]. Chinese Journal of Power Sources，2013，37(11):1947-1950.
[81] 黄德扬，陈自强，周诗尧，等. 极寒环境下动力锂离子电池特性[J]. 上海交通大学学报，2019，53(9):1051-1057. HUANG Deyang，CHEN Ziqiang，ZHOU Shiyao，et al. Characteristics of power lithium-ion batteries at exteme cold environment[J]. Journal of Shanghai Jiao Tong University，2019，53(9):1051-1057.
[82] CHIU K，LIN C，YEH S，et al. Cycle life analysis of series connected lithium-ion batteries with temperature difference[J]. Journal of Power Sources，2014，263:75-84.
[83] WU M，LIN C，WANG Y，et al. Numerical simulation for the discharge behaviors of batteries in series and/or parallel-connected battery pack[J]. Electrochimica Acta，2007，52(3):1349-1357.
[84] 施宝昌，沈爱弟. 并联锂离子电池组的模型化与电流分配[J]. 计算机测量与控制，2017，25(10):189-193. SHI Baochang，SHEN Aidi. Modeling and current distribution of parallel-connected lithium cells[J]. Computer Measurement & Control，2017，25(10):189-193.
[85] GONG X，XIONG R，MI C. Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells[J]. IEEE Transactions on Industry Applications，2014，51:1872-1879.
[86] 王帅，尹忠东，郑重，等. 电池单体参数不一致对电池模组性能影响研究[J]. 电测与仪表，2020，57(10):76-82，107. WANG Shuai，YIN Zhongdong，ZHENG Zhong，et al. The influence of cell parameters inconsistency on the performance of different topological battery modules[J]. Electrical Measurement & Instrumentation，2020，57(10):76-82，107.
[87] 郭健忠，张华伟，张麒麟，等. 车载动力电池成组热不一致性研究[J]. 机械设计与制造，2020(9):35-39. GUO Jianzhong，ZHANG Huawei，ZHANG Qilin，et al. Reasearch on thermal un-uniformity of vehicle traction battery pack[J]. Machinery Design & Manufacture，2020(9):35-39.
[88] 张旭. 基于系统复杂性分析的锂电池组状态估计与安全管理[D]. 合肥:中国科学技术大学，2018. ZHANG Xu. Lithium-ion battery pack states estimation and satefy management based on sytem complexity analysis[D]. Hefei:Univeristy of Science and Technology of China，2018.
[89] ZHANG C，SHARKH S. Estimation of real-time peak power capability of a traction battery pack used in an HEV[C]//Asia-Pacific Power and Energy Engineering Conference，Chengdu，2010:1-6.
[90] DONG G，WEI J，CHEN Z. Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries[J]. Journal of Power Sources，2016，328:615-626.
[91] JIN Z，ZHANG Z，ALIYEV T，et al. Estimating the power limit of a lithium battery pack by considering cell variability[J]. SAE Technical Papers，2015，32:48-59.
[92] WAAG W，FLEISCHER C，SAUER D. Adaptive on-line prediction of the available power of lithium-ion batteries[J]. Journal of Power Sources，2013，242:548-559.
[93] WANG S，STROE D，FERNANDEZ C，et al. A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model[J]. Journal of Cleaner Production，2020，242:118506-118518.
[94] JIANG B，DAI H，WEI X，et al. Online reliable peak charge/discharge power estimation of series-connected lithium-ion battery packs[J]. Energies，2017，10(3):390.
[95] ZHOU Z，KANG Y，SHANG Y，et al. Peak power prediction for series-connected LiNCM battery pack based on representative cells[J]. Journal of Cleaner Production，2019，230:1061-1073.
[96] ESFANDYARI M，YAZDI M，ESFAHANIAN V，et al. A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs[J]. Journal of Energy Storage，2019，24:100758.
[97] 彭思敏，朱学来. 无迹卡尔曼滤波法估计并联型电池系统的荷电状态[J]. 江苏工程职业技术学院学报，2015，15(4):12-14. PENG Simin，ZHU Xuelai. Application of unscented kalman filter method in state of charge estimation of parallel battery system[J]. Journal of Jiangsu College of Engineering and Technology，2015，15(4):12-14.
[98] ZHONG L，ZHANG C，HE Y，et al. A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis[J]. Applied Energy，2014，113:558-564.
[99] SHI W，HU X，JIN C，et al. Effects of imbalanced currents on large-format LiFePO₄ graphite batteries systems connected in parallel[J]. Journal of Power Sources，2016，313:198-204.
[100] WANG S，FERNANDEZ C，YU C，et al. A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm[J]. Journal of Power Sources，2020，471:228450-228462.
[101] SONG Lijun，ZHANG Keyao，LIANG Tongyi，et al. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis[J]. Journal of Energy Storage，2020，32:198-204.
[102] LI S，ZHAO P. Big data driven vehicle battery management method: A novel cyber-physical system perspective[J]. Journal of Energy Storage，2021，33:102064.
[103] WANG Y，XU R，ZHOU C，et al. Digital twin and cloud-side-end collaboration for intelligent battery management system[J]. Journal of Manufacturing Systems，2022，62:124-134.
[104] 杨瑞鑫，熊瑞，孙逢春. 锂离子动力电池外部短路测试平台开发与试验分析[J]. 电气工程学报，2021，16(1):103-118. YANG Ruixin，XIONG Rui，SUN Fengchun. Experimental platform development and characteristics analysis of external short circuit in lithium-ion batteries[J]. Journal of Electrical Engineering，2021，16(1):103-118.
[105] 杜海忠，骆滔，宋浩谊，等. 基于电压平衡的锂电池主动均衡电路及策略[J]. 电气工程学报，2021，16(3):145-151. DU Haizhong，LUO Tao，SONG Haoyi，et al. Active balancing circuit and strategy of Li-ion battery based on voltage balance[J]. Journal of Electrical Engineering，2021，16(3):145-151.