[1] |
ZHONG R, DU C Q. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries[J]. Energy Reports, 2023,9:2993-3021.
|
[2] |
XIAO Y Q, WEN J S, YAO L. A comprehensive review of the lithium-ion battery state of health prognosis methods combining aging mechanism analysis[J]. Journal of Energy Storage, 2023,65:107347.
|
[3] |
梁新成, 张勉, 黄国钧, 等. 基于BMS的锂离子电池建模方法综述[J]. 储能科学与技术, 2020, 9(6):1933-1939. doi: 10.19799/j.cnki.2095-4239.2020.0166 LIANG Xincheng, ZHANG Mian, HUANG Guojun, et al. Review on lithium-ion battery modeling methods based on BMS[J]. Energy Storage Science and Technology, 2020, 9(6):1933-1939. doi: 10.19799/j.cnki.2095-4239.2020.0166
|
[4] |
董海斌, 羡学磊, 马建琴, 等. 锰酸锂电池热失控特性研究[J]. 消防科学与技术, 2022, 41(1):21-25. DONG Haibin, XIAN Xuelei, MA Jianqin, et al. Research on thermal runaway characteristics of lithium manganate battery[J]. Fire Science and Technology, 2022, 41(1):21-25. doi: 10.3210/fst.41.21
|
[5] |
薄利明, 郑惠萍, 张世锋, 等. 锂电池健康状态均衡技术综述[J]. 电测与仪表, 2022,4:11-18. BO Liming, ZHENG Huiping, ZHANG Shifeng, et al. Review on health state equalization technology for lithium batteries[J]. Electrical Measurement & Instrumentation, 2022,4:11-18.
|
[6] |
TIAN H X, QIN P L, LI K, et al. A review of the state of health for lithium-ion batteries:Research status and suggestions[J]. Journal of Cleaner Production, 2020, 261(10):120813. doi: 10.1016/j.jclepro.2020.120813
|
[7] |
PASTOR-FERNANDEZ C, UDDIN K, CHOUCHELAMANE G H, et al. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems[J]. Journal of Power Sources, 2017,360:301-318.
|
[8] |
CHRISTOPH R B, MATTHEW R R, EUAN M, et al. Degradation diagnostics for lithium ion cells[J]. Journal of Power Sources, 2017,341:373-386.
|
[9] |
CUI Y Z, DU C Y, YIN G P, et al. Multi-stress factor model for cycle lifetime prediction of lithium ion batteries with shallow-depth discharge[J]. Journal of Power Sources, 2015,279:123-132.
|
[10] |
JIANG S D, SONG Z X. A review on the state of health estimation methods of lead-acid batteries[J]. Journal of Power Sources, 2022,517:1-18.
|
[11] |
梁培维, 张彦会. 基于欧姆内阻对锂电池健康状态的估算[J]. 电源技术, 2019, 43(10):1623-1704. LIANG Peiwei, ZHANG Yanhui. Estimation of lithium battery health based on ohmic internal resistance[J]. Chinese Journal of Power Sources, 2019, 43(10):1623-1704.
|
[12] |
GALEOTTI M, CINÀ L, GIAMMANCO C, et al. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy[J]. Energy, 2015,89:678-686.
|
[13] |
XIA F, WANG K G, CHEN J J. State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage[J]. Journal of Energy Storage, 2023,64:107161.
|
[14] |
LI Y, ABDEL-MONEM M, GOPALAKRISHNAN R, et al. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter[J]. Journal of Power Sources, 2018,373:40-53.
|
[15] |
ZHENG Y J, OUYANG M G, LI X J, et al. Recording frequency optimization for massive battery data storage in battery management systems[J]. Applied Energy, 2016,183:380-389.
|
[16] |
HUANG S, LIU C, SUN H, et al. State of health estimation of lithium-ion batteries based on the regional frequency[J]. Journal of Power Sources, 2022,518:230773.
|
[17] |
ZHANG Y, CAI Y X, LIU W, et al. State of health estimation of lithium-ion batteries based on the regional triangle[J]. Journal of Energy Storage, 2023,69:107918.
|
[18] |
CHE Y H, HU X S, LIN X K, et al. Health prognostics for lithium-ion batteries:Mechanisms,methods,and prospects[J]. Energy & Environmental Science, 2023,16:338-371.
|
[19] |
吴盛军, 袁晓冬, 徐青山, 等. 锂电池健康状态评估综述[J]. 电源技术, 2017, 41(12):1788-1792. WU Shengjun, YUAN Xiaodong, XU Qingshan, et al. Review on lithium-ion battery health state assessment[J]. Chinese Journal of Power Sources, 2017, 41(12):1788-1792.
|
[20] |
高仁璟, 吕治强, 赵帅, 等. 基于电化学模型的锂离子电池健康状态估算[J]. 北京理工大学学报, 2022, 42(9):791-797. GAO Renjing, LÜ Zhiqiang, ZHAO Shuai, et al. Health state estimation of Li-ion batteries based on electrochemical model[J]. Transactions of Beijing Institute of Technology, 2022, 42(9):791-797.
|
[21] |
武龙星, 庞辉, 晋佳敏, 等. 基于电化学模型的锂离子电池荷电状态估计方法综述[J]. 电工技术学报, 2022, 37(7):1703-1725. WU Longxing, PANG Hui, JIN Jiamin, et al. A review of SOC estimation methods for lithium-ion batteries based on electrochemical model[J]. Transactions of China Electrotechnical Society, 2022, 37(7):1703-1725.
|
[22] |
REN L C, ZHU G R, KANG J Q, et al. An algorithm for state of charge estimation based on a single particle model[J]. Journal of Energy Storage, 2021, 39(7):102644-102651. doi: 10.1016/j.est.2021.102644
|
[23] |
TOPAN P A, RAMADAN M N, FATHONI G, et al. State of charge (SOC) and state of health (SOH) estimation on lithium polymer battery via Kalman filter[C]// 2016 2nd International Conference on Science and Technology-Computer (ICST), October 27-28,2016,Yogyakarta,Indonesia. IEEE,2016:93-96.
|
[24] |
赵可沦, 江境宏, 邓进, 等. 基于遗忘因子递推最小二乘法的锂电池等效电路模型参数辨识方法[J]. 电子测量技术, 2022, 45(23):53-58. ZHAO Kelun, JIANG Jinghong, DENG Jin, et al. Parameter identification method of lithium battery equivalent circuit model based on forgetting factor recursive least square[J]. Electronic Measurement Technology, 2022, 45(23):53-58.
|
[25] |
KIM T, WANG Y, SAHINOGLU Z, et al. Fast UD factorization based RLS online parameter identification for model-based condition monitoring of lithium-ion batteries[C]// 2014 American Control Conference,June 04-06,2014,Portland,OR,USA. IEEE,2014:4410-4415.
|
[26] |
孙金磊, 唐传雨, 李磊, 等. 基于状态与模型参数联合估计的老化电池可充入电量估计方法[J]. 电工技术学报, 2022, 37(22):5886-5898. SUN Jinlei, TANG Chuanyu, LI Lei, et al. An estimation method of rechargeable electric quantity for aging battery based on joint estimation of state and model parameters[J]. Transactions of China Electrotechnical Society, 2022, 37(22):5886-5898.
|
[27] |
HU X S, CHE Y H, LIN X K, et al. Battery health prediction using fusion-based feature selection and machine learning[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2):382-398. doi: 10.1109/TTE.2020.3017090
|
[28] |
车云弘, 邓忠伟, 李佳承, 等. 基于数据驱动的电池系统泛化SOH估计方法[J]. 机械工程学报, 2022, 58(20):253-263. CHE Yunhong, DENG Zhongwei, LI Jiacheng, et al. Generalized data-driven SOH estimation method for battery systems[J]. Journal of Mechanical Engineering, 2022, 58(20):253-263.
|
[29] |
GRANDJEAN L L, ODIO M X, WIDANAGE W D. 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.
|
[30] |
LI J, ADEWUYI K, LOTFI N. A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health (SOH) estimation[J]. Applied Energy, 2018,212:178-190.
|
[31] |
刘征宇, 杨昆, 魏自红. 包含液相扩散方程简化的锂离子电池电化学模型[J]. 物理学报, 2019, 68(9):251-258. LIU Zhengyu, YANG Kun, WEI Zihong. Electrochemical model of lithium ion battery with simplified liquid phase diffusion equation[J]. Acta Physica Sinica, 2019, 68(9):251-258.
|
[32] |
LV C, LAI Q Z, GE T F. A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework[J]. Energy, 2017,124:975-984.
|
[33] |
郭桐, 刘涛, 方德宇, 等. 锂离子电池健康评估研究进展[J]. 电池工业, 2023, 27(1):48-54. GUO Tong, LIU Tao, FANG Deyu, et al. Research progress in health assessment of lithium-ion batteries[J]. Chinese Battery Industry, 2023,2023, 27(1):48-54.
|
[34] |
SHRIVASTAVA P, NAIDU P A, SHARMA S, et al. Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications[J]. Journal of Energy Storage, 2023,64:107159.
|
[35] |
SONG Y C, LIU D T, HOU Y D, et al. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm[J]. Chinese Journal of Aeronautics, 2018, 31(1):31-40. doi: 10.1016/j.cja.2017.11.010
|
[36] |
邓涛, 罗卫兴, 李志飞, 等. 双卡尔曼滤波法估计电动汽车电池健康状态[J]. 电池, 2018, 48(2):95-99. DENG Tao, LUO Weixing, LI Zhifei, et al. Estimation state of health of electric vehicle battery by dual Kalman filter[J]. Battery Bimonthly, 2018, 48(2):95-99.
|
[37] |
FANG L L, LI J Q, PENG B, et al. Online estimation and error analysis of both SOC and SOH of lithium-ion battery based on DEKF method[J]. Energy Procedia,2019:158:3008-3013.
|
[38] |
BI J, ZHANG T, YU H Y, et al. State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter[J]. Applied Energy, 2016,182:558-568.
|
[39] |
李鹏, 李立伟, 杨玉新. 基于IBOA-PF的锂电池健康状态预测[J]. 储能科学与技术, 2021, 10(2):705-713. doi: 10.19799/j.cnki.2095-4239.2020.0391 LI Peng, LI Liwei, YANG Yuxin. Estimation of lithium-ion battery state of health based on IBOA-PF[J]. Energy Storage Science and Technology, 2021, 10(2):705-713. doi: 10.19799/j.cnki.2095-4239.2020.0391
|
[40] |
SCHWUNK S, ARMBRUSTER N, STRAUB S, et al. Particle filter for state of charge and state of health estimation for lithium-ion phosphate batteries[J]. Journal of Power Sources, 2013,239:705-710.
|
[41] |
LIU Yuefeng, HE Yingjie, BIAN Haodong, et al. A review of lithium-ion battery state of charge estimation based on deep learning:Directions for improvement and future trends[J]. Journal of Energy Storage, 2022,52:104664.
|
[42] |
李建林, 肖珩. 锂离子电池建模现状综述[J]. 储能科学与技术, 2022, 11(2):697-703. doi: 10.19799/j.cnki.2095-4239.2021.0450 LI Jianlin, XIAO Heng. Review on modeling of lithium-ion battery[J]. Energy Storage Science and Technology, 2022, 11(2):697-703. doi: 10.19799/j.cnki.2095-4239.2021.0450
|
[43] |
徐元中, 曹翰林, 吴铁洲. 基于SA-BP神经网络算法的电池SOH预测[J]. 电源技术, 2020, 44(3):341-345. XU Yuanzhong, CAO Hanlin, WU Tiezhou. Estimation of SOH for battery based on SA-BP neural network[J]. Chinese Journal of Power Sources, 2020, 44(3):341-345.
|
[44] |
李文华, 邵方旭, 暴二平, 等. 六自由度振动老化条件下锂离子电池的衰退机理诊断与SOH预测[J]. 仪器仪表学报, 2021, 41(8):62-69. LI Wenhua, SHAO Fangxu, BAO Erping, et al. Diagnosis of degradation mechanism and SOH prediction of lithium-ion batteries under 6-DOF vibration and aging conditions[J]. Chinese Journal of Scientific Instrument, 2021, 41(8):62-69.
|
[45] |
徐彬翔, 郑林锋, 黄乙恒, 等. 基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法[J]. 电气工程学报, 2022, 17(4):11-19. XU Binxiang, ZHENG Linfeng, HUANG Yiheng, et al. Fast estimating the state of health of lithium-ion batteries based on improved least squares support vector machine[J]. Journal of Electrical Engineering, 2022, 17(4):11-19.
|
[46] |
陈璐, 于仲安, 熊莹燕. 不同温度下基于PSO-LSSVM的锂电池SOH估计与RUL预测[J]. 传感器与微系统, 2023, 42(6):141-145. CHEN Lu, YU Zhongan, XIONG Yingyan. SOH estimation and RUL prediction of Li battery based on PSO-LSSVM at different temperatures[J]. Transducer and Microsystem Technologies, 2023, 42(6):141-145.
|
[47] |
SALKIND A J, FENNIE C, SINGH P, et al. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology[J]. Journal of Power Sources, 1999,80:293-300.
|
[48] |
HE T T, XU Z P, XIE Q, et al. A method of battery capacity prediction based on fuzzy logic and neural networks[J]. Earth and Environmental Science, 2020,558:052015.
|
[49] |
樊欣欣, 陈秀国, 王建宾, 等. 基于模糊逻辑的变电站蓄电池在线健康状态评估[J]. 电子器件, 2021, 44(1):136-140. FAN Xinxin, CHEN Xiuguo, WANG Jianbin, et al. On-line health assessment of substation battery based on fuzzy logic[J]. Chinese Journal of Electron Devices, 2021, 44(1):136-140.
|
[50] |
吕佳朋, 史贤俊, 王康. 基于高斯过程回归的电池容量预测模型[J]. 电子测量技术, 2020, 43(3):43-48. LÜ Jiapeng, SHI Xianjun, WANG Kang. Battery capacity prediction model based on Gaussian process regression[J]. Electronic Measurement Technology, 2020, 43(3):43-48.
|
[51] |
陈琳, 刘博豪, 丁云辉, 等. 采用粒子群优化和高斯回归实现电池SOH估计[J]. 汽车工程, 2021, 43(10):1472-1478. doi: 10.19562/j.chinasae.qcgc.2021.10.008 CHEN Lin, LIU Bohao, DING Yunhui, et al. Estimation of battery state-of-health using particle swarm optimization with Gauss process regression[J]. Automotive Engineering, 2021, 43(10):1472-1478.
|
[52] |
张兴红, 徐翊, 巩泽浩, 等. 融合电化学阻抗与容量增量曲线特征的锂电池健康状态算法研究[J]. 重庆理工大学学报, 2023, 37(5):265-272. ZHANG Xinghong, XU Yi, GONG Zehao, et al. Algorithm research on health state of a lithium battery by integrating the features of electrochemical impedance and incremental capacity curves[J]. Journal of Chongqing University of Technology, 2023, 37(5):265-272.
|
[53] |
CHEN Liping, XIE Siqiang, ANTÓNIO M L, et al. A new SOH estimation method for Lithium-ion batteries based on model-data-fusion[J]. Energy, 2024,286:129597.
|
[54] |
HE Jiabei, TIAN Yi, WU Lifeng. A hybrid data-driven method for rapid prediction of lithium-ion battery capacity[J]. Reliability Engineering and System Safety, 2022,226:108674.
|
[55] |
MA Zeyu, YANG Ruixin, WANG Zhenpo. A novel data-model fusion state-of-health estimation approach for lithiumion batteries[J]. Applied Energy, 2019,237:836-847.
|
[56] |
HOU Y K, ZANG Z S, LIU P, et al. Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles[J]. Advances in Mechanical Engineering, 2021, 13(7):1-14.
|
[57] |
王萍, 彭香园, 程泽, 等. 基于数据驱动模型融合的锂离子电池多时间尺度状态联合估计方法[J]. 汽车工程, 2022, 44(3):362-378. WANG Ping, PENG Xiangyuan, CHENG Ze, et al. A multi-time scale joint state estimation method for lithium-ion batteries based on data-driven model fusion[J]. Automotive Engineering, 2022, 44(3):362-378.
|
[58] |
崔显, 陈自强. 基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法[J/OL]. 上海交通大学学报:1-23[2023-03-10]. https://doi.org/10.16183/j.cnki.jsjtu.2022.221.. CUI Xian, CHEN Ziqiang. et al. A highly robust state of health estimation method of lithium-ion battery based on ECM and SGPR[J/OL]. Journal of Shanghai Jiaotong University:1-23[2023-03-10]. https://doi.org/10.16183/j.cnki.jsjtu.2022.221..
|
[59] |
CHEN Dan, MENG Jinhao, HUANG Huanyang, et al. An empirical-data hybrid driven approach for remaining useful life prediction of lithium-ion batteries considering capacity diving[J]. Energy, 2022,245:123222.
|
[60] |
陈国麟, 姚行艳. 基于CNN-Transformer的锂离子电池健康状态估计[J]. 重庆工商大学学报, 2024, 41(2):66-73. CHEN Guolin, YAO Xingyan. State of health estimation of lithium-ion battery based on CNN-Transformer[J]. Journal of Chongqing Technology and Business University, 2024, 41(2):66-73.
|
[61] |
倪祥淦, 何志刚, 胡帅, 等. 基于BI-LSTM神经网络的宽采样频率电池SOH估算[J]. 车用发动机, 2022(5):44-50. NI Xianggan, HE Zhigang, HU Shuai, et al. Battery SOH estimation with wide sampling frequency based on BI-LSTM neural network[J]. Vehicle Engine, 2022(5):44-50.
|
[62] |
YU J. State of health prediction of lithium-ion batteries:Multiscale logic regression and Gaussian process regression ensemble[J]. Reliability Engineering & System Safety, 2018,174:82-95.
|
[63] |
LIN M Q, WU D G, MENG J H, et al. A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries[J]. Journal of Power Sources, 2022,518:230774.
|
[64] |
蔡艳平, 陈万, 苏延召, 等. 锂离子电池剩余寿命预测方法综述[J]. 电源技术, 2021, 45(5):678-682. CAI Yanping, CHEN Wan, SU Yanzhao, et al. Review of remaining useful life prediction for lithium ion batteries[J]. Chinese Journal of Power Sources, 2021, 45(5):678-682.
|
[65] |
WEI Zhongbao, LENG Feng, HE Zhongjie, et al. Online state of charge and state of health estimation for lithium-ion battery based on a data-model fusion method[J]. Energies, 2018, 11(7):1810-1823. doi: 10.3390/en11071810
|
[66] |
晏莉琴, 马尚德, 罗英, 等. 锂离子电池快速寿命评价技术与方法[J]. 上海航天, 2023, 40(1):123-135. YAN Liqin, MA Shangde, LUO Ying, et al. Overview of rapid lifetime evaluation technologies and methods for lithium-ion batteries[J]. Aerospace Shanghai, 2023, 40(1):123-135.
|
[67] |
HONG Shiding, QIN Chaokui, LAI Xin, et al. State-of- health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm[J]. Journal of Energy Storage, 2023,64:107179.
|
[68] |
王震坡, 王秋诗, 刘鹏, 等. 大数据驱动的动力电池健康状态估计方法综述[J]. 机械工程学报, 2023, 59(2):151-168. doi: 10.3901/JME.2023.02.151 WANG Zhenpo, WANG Qiushi, LIU Peng, et al. Review on techniques for power battery state of health estimation driven by big data methods[J]. Journal of Mechanical Engineering, 2023, 59(2):151-168. doi: 10.3901/JME.2023.02.151 |