[1] 姜久春,高洋,张彩萍,等. 电动汽车锂离子动力电池健康状态在线诊断方法[J]. 机械工程学报,2019,55(20):60-72. JIANG Jiuchun,GAO Yang,ZHANG Caiping,et al. Online diagnostic method for health status of lithium-ion battery in electric vehicle[J]. Journal of Mechanical Engineering,2019,55(20):60-72. [2] 马彦,陈阳,张帆,等. 基于扩展H∞粒子滤波算法的动力电池寿命预测方法[J]. 机械工程学报,2019,55(20):36-43. MA Yan,CHEN Yang,ZHANG Fan,et al. Remaining useful life prediction of power battery based on extend H∞ particle filter algorithm[J]. Journal of Mechanical Engineering,2019,55(20):36-43. [3] ZHANG Y,XIONG R,HE H,et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology,2018,67(7):5695-5705. [4] XIONG R,LI L,TIAN J. Towards a smarter battery management system:A critical review on battery state of health monitoring methods[J]. Journal of Power Sources,2018,405:18-29. [5] TIAN J,XIONG R,YU Q. Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics,2018,66(2):1576-1584. [6] 薛楠,孙丙香,白恺,等. 基于容量增量分析的复合材料锂电池分区间循环衰退机理[J]. 电工技术学报,2017,32(13):145-152. XUE Nan,SUN Bingxiang,BAI Kai,et al. Different state of charge range cycle degradation mechanism of composite material lithium-ion batteries based on incremental capacity analysis[J]. Transactions of China Electrotechnical Society,2017,32(13):145-152. [7] ZHENG L,ZHU J,LU D D C,et al. Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries[J]. Energy,2018,150:759-769. [8] YU Q,XIONG R,YANG R,et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy,2019,255:113817. [9] CHEN C,XIONG R,SHEN W. A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation[J]. IEEE Transactions on Power Electronics,2017,33(1):332-342. [10] XIONG R,LI L,LI Z,et al. An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy,2018,219:264-275. [11] 戴海峰,姜波,魏学哲,等. 基于充电曲线特征的锂离子电池容量估计[J]. 机械工程学报,2019,55(20):52-59. DAI Haifeng,JIANG Bo,WEI Xuezhe,et al. Capacity estimation of lithium-ion batteries based on charging curve features[J]. Journal of Mechanical Engineering, 2019,55(20):52-59. [12] HU X,CHE Y,LIN X,et al. Health prognosis for electric vehicle battery packs:A data-driven approach[J]. IEEE/ASME Transactions on Mechatronics,2020,doi:10. 1109/TMECH. 2020. 2986364. [13] 李超然,肖飞,樊亚翔,等. 基于深度学习的锂离子电池SOC和SOH联合估算[J]. 中国电机工程学报,2021,41(2):681-692. LI Chaoran,XIAO Fei,FAN Yaxiang,et al. Joint estimation of the state of charge and the state of health based on deep learning for lithium-ion batteries[J]. Proceedings of the CSEE,2021,41(2):681-692. [14] SHU X,LI G,SHEN J,et al. An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation[J]. Journal of Power Sources,2020,462:228132. [15] LIU D,SONG Y,LI L,et al. On-line life cycle health assessment for lithium-ion battery in electric vehicles[J]. Journal of Cleaner Production,2018,199:1050-1065. [16] MAHDJOUBI A,ZEGNINI B,BELKHEIRI M,et al. Fixed least squares support vector machines for flashover modelling of outdoor insulators[J]. Electric Power Systems Research,2019,173:29-37. [17] ZHANG Y,XIONG R,HE H,et al. Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation[J]. IEEE Transactions on Industrial Electronics,2018,66(2):1585-1597. [18] ISHIZAKA A,SIRAJ S. Are multi-criteria decision-making tools useful? An experimental comparative study of three methods[J]. European Journal of Operational Research,2018,264(2):462-471. [19] LI X,WANG Z,ZHANG L,et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources,2019,410:106-114. [20] GOEBEL K,SAHA B,SAXENA A,et al. Prognostics in battery health management[J]. IEEE Instrumentation & Measurement Magazine,2008,11(4):33-40. [21] CHEN Z,XUE Q,XIAO R,et al. State of health estimation for lithium-ion batteries based on fusion of autoregressive moving average model and Elman neural network[J]. IEEE Access,2019,7:102662-102678. |