[1] |
LI P H, ZHANG Z J, XIONG Q Y, et al. State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network [J]. Journal of power sources, 2020, 459: 228069. DOI: 10.1016/j.jpowsour.2020.228069. |
[2] |
叶楚天. 动力电池及充电基础设施技术发展对电动汽车能量补给方式的影响研究 [J]. 南方能源建设, 2017, 4(2): 69-72. DOI: 10.16516/j.gedi.issn2095-8676.2017.02.011.
YE C T. Research on the influence of power battery and charing infrastructure technology on the energy supply mode of electric vehicles [J]. Southern energy construction, 2017, 4(2): 69-72. DOI: 10.16516/j.gedi.issn2095-8676.2017.02.011. |
[3] |
ZHANG Y J, LIU Y J, WANG J, et al. State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression [J]. Energy, 2022, 239: 121986. DOI: 10.1016/j.energy.2021.121986. |
[4] |
胡轲. 大容量储能系统电池管理系统均衡技术研究 [J]. 南方能源建设, 2018, 5(1): 40-44. DOI: 10.16516/j.gedi.issn2095-8676.2018.01.006.
HU K. Research on balancing technology of battery management system of high-capacity energy storage system [J]. Southern energy construction, 2018, 5(1): 40-44. DOI: 10.16516/j.gedi.issn2095-8676.2018.01.006. |
[5] |
樊亚翔, 肖飞, 许杰, 等. 基于充电电压片段和核岭回归的锂离子电池SoH估计 [J]. 中国电机工程学报, 2021, 41(16): 5661-5669. DOI: 10.13334/j.0258-8013.pcsee.201805.
FAN Y X, XIAO F, XU J, et al. State of health estimation of lithium-ion batteries based on the partial charging voltage segment and kernel ridge regression [J]. Proceedings of the CSEE, 2021, 41(16): 5661-5669. DOI: 10.13334/j.0258-8013.pcsee.201805. |
[6] |
ZRAIBI B, OKAR C, CHAOUI H, et al. Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method [J]. IEEE transactions on vehicular technology, 2021, 70(5): 4252-4261. DOI: 10.1109/TVT.2021.3071622. |
[7] |
LI Y, LIU K L, FOLEY A M, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review [J]. Renewable and sustainable energy reviews, 2019, 113: 109254. DOI: 10.1016/j.rser.2019.109254. |
[8] |
TIAN J P, XIONG R, SHEN W X, et al. State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach [J]. Applied energy, 2021, 291(3): 116812. DOI: 10.1016/j.apenergy.2021.116812. |
[9] |
ZHANG S Z, GUO X, DOU X X, et al. A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis [J]. Journal of power sources, 2020, 479(11): 228740. DOI: 10.1016/j.jpowsour.2020.228740. |
[10] |
ZHANG Q C, LI X, ZHOU C, et al. State-of-health estimation of batteries in an energy storage system based on the actual operating parameters [J]. Journal of power sources, 2021, 506: 230162. DOI: 10.1016/j.jpowsour.2021.230162. |
[11] |
GUO P Y, CHENG Z, YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction [J]. Journal of power sources, 2019, 412: 442-450. DOI: 10.1016/j.jpowsour.2018.11.072. |
[12] |
SU L S, ZHANG J B, WANG C J, et al. Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments [J]. Applied energy, 2016, 163: 201-210. DOI: 10.1016/j.apenergy.2015.11.014. |
[13] |
XIONG R, LI L L, YU Z R, 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. DOI: 10.1016/j.apenergy.2018.03.053. |
[14] |
XU W H, WANG S L, JIANG C, et al. A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation [J]. International journal of energy research, 2021, 45(12): 14592-14602. DOI: 10.1002/er.6719. |
[15] |
LIN C P, XU J, SHI M J, et al. Constant current charging time based fast state-of-health estimation for lithium-ion batteries [J]. Energy, 2022, 247: 123556. DOI: 10.1016/j.energy.2022.123556. |
[16] |
LI X Y, YUAN C G, LI X H, et al. State of health estimation for Li-ion battery using incremental capacity analysis and Gaussian process regression [J]. Energy, 2020, 190: 116467. DOI: 10.1016/j.energy.2019.116467. |
[17] |
ZHOU Y, DONG G Z, TAN Q Q, et al. State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression [J]. Energy, 2023, 262: 125514. DOI: 10.1016/j.energy.2022.125514. |
[18] |
LI Q L, LI D Z, ZHAO K, et al. State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression [J]. Journal of energy storage, 2022, 50: 104215. DOI: 10.1016/j.est.2022.104215. |
[19] |
YANG S J, ZHANG C P, JIANG J C, et al. Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications [J]. Journal of cleaner production, 2021, 314: 128015. DOI: 10.1016/j.jclepro.2021.128015. |
[20] |
TANG T, YUAN H M. The capacity prediction of Li-ion batteries based on a new feature extraction technique and an improved extreme learning machine algorithm [J]. Journal of power sources, 2021, 514: 230572. DOI: 10.1016/j.jpowsour.2021.230572. |
[21] |
TAN X J, LIU X X, WANG H Y, et al. Intelligent online health estimation for lithium-ion batteries based on a parallel attention network combining multivariate time series [J]. Frontiers in energy research, 2022, 10: 844985. DOI: 10.3389/fenrg.2022.844985. |
[22] |
DENG Y W, YING H J, E J Q, et al. Feature parameter extraction and intelligent estimation of the state-of-health of lithium-ion batteries [J]. Energy, 2019, 176: 91-102. DOI: 10.1016/j.energy.2019.03.177. |
[23] |
WANG Z K, ZENG S K, GUO J B, et al. State of health estimation of lithium-ion batteries based on the constant voltage charging curve [J]. Energy, 2019, 167: 661-669. DOI: 10.1016/j.energy.2018.11.008. |
[24] |
连强. 综合区间数Spearman秩相关系数及其应用 [J]. 重庆工商大学学报(自然科学版), 2020, 37(6): 71-75. DOI: 10.16055/j.issn.1672-058X.2020.0006.011.
LIAN Q. The synthetic spearman rank correlation coefficient of interval numbers and its application [J]. Journal of Chongqing technology and business University (natural sciences edition), 2020, 37(6): 71-75. DOI: 10.16055/j.issn.1672-058X.2020.0006.011. |
[25] |
练继建, 李火坤, 张建伟. 基于奇异熵定阶降噪的水工结构振动模态ERA识别方法 [J]. 中国科学(E辑:技术科学), 2008, 38(9): 1398-1413.
LIAN J J, LI H K, ZHANG J W. ERA recognition method for hydraulic structure vibration modes based on singular entropy order denoising [J]. Scientia sinica (technologica), 2008, 38(9): 1398-1413. |
[26] |
KALMAN D. A singularly valuable decomposition: the SVD of a matrix [J]. The college mathematics journal, 1996, 27(1): 2-23. DOI: 10.1080/07468342.1996.11973744. |
[27] |
张一帆. 改进的动态三次指数平滑法火电厂发电量预测研究 [D]. 河北: 河北工程大学, 2020. DOI: 10.27104/d.cnki.ghbjy.2020.000298.
ZHANG Y F. Research on forecast of power generation of thermal power plant base on dynamic three exponential smoothing [D]. Hebei: Hebei University of Engineering, 2020. DOI: 10.27104/d.cnki.ghbjy.2020.000298. |
[28] |
CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [J].Eprint arxiv, 2014. DOI: 10.48550/arXiv.1412.3555. |
[29] |
XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm [J]. Systems science & control engineering an open access journal, 2020, 8(1): 22-34. DOI: 10.1080/21642583.2019.1708830. |