[1] |
赵显赫, 耿光超, 林达, 等. 基于数据驱动的锂离子电池健康状态评估综述[J]. 浙江电力, 2021, 40(7):65-73.
|
|
ZHAO Xianhe, GENG Guangchao, LIN Da, et al. Review of data-driven state of health estimation for lithium-ion battery[J]. Zhejiang Electric Power, 2021, 40(7):65-73.
|
[2] |
BELHAROUAK I, PASSERINI S. Special issue:Battery community celebration of 2019 Nobel Prize in Chemistry for the development of lithium-ion batteries[J]. Journal of Power Sources, 2020, 471:228548.
doi: 10.1016/j.jpowsour.2020.228548
|
[3] |
锂电池电解液行业标准2018年10月1日起实施[J]. 功能材料信息, 2018(4):30-31.
|
|
Lithium battery electrolyte industry standard has been implemented since October 1,2018[J]. Functional Materials Information, 2018(4):30-31.
|
[4] |
POURAGHAJAN F, THOMPSON A I, HUNTER E E, et al. The effects of cycling on ionic and electronic conductivities of li-ion battery electrodes[J]. Journal of Power Sources, 2021, 492:229636.
doi: 10.1016/j.jpowsour.2021.229636
|
[5] |
BARRÉ A, DEGUILHEM B, GROLLEAU S, et al. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications[J]. Journal of Power Sources, 2013, 241:680-689.
doi: 10.1016/j.jpowsour.2013.05.040
|
[6] |
BARCELLONA S, PIEGARI L. Effect of current on cycle aging of lithium ion batteries[J]. Journal of Energy Storage, 2020, 29:101310.
doi: 10.1016/j.est.2020.101310
|
[7] |
LI Y, LIU K, 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] |
ERDINC O, VURAL B, UZUNOGLU M. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading[C/CD]// International Conference on Clean Electrical Power. IEEE, 2009.
|
[9] |
SU B, KE X, YUAN C. Modeling the effects of state of charge and temperature on calendar capacity loss of nickel-manganese-cobalt lithium-ion batteries[J]. Journal of Energy Storage, 2022, 49:104105.
doi: 10.1016/j.est.2022.104105
|
[10] |
SU Boman, KE Xinyou, YUAN C. Modeling the effects of state of charge and temperature on calendar capacity loss of nickel-manganese-cobalt lithium-ion batteries[J]. Journal of Energy Storage, 2022, 49:104105.
doi: 10.1016/j.est.2022.104105
|
[11] |
REDONDO-IGLESIAS E, VENET P, PELISSIER S. Modelling lithium-ion battery ageing in electric vehicle applications—Calendar and cycling ageing combination effects[J]. Batteries, 2020, 6(1):14.
doi: 10.3390/batteries6010014
|
[12] |
WATANABE S, KINOSHITA M, HOSOKAWA T, et al. Capacity fade of LiAlyNi1-x-yCoxO2 cathode for lithium-ion batteries during accelerated calendar and cycle life tests (surface analysis of LiAlyNi1-x-yCoxO2 cathode after cycle tests in restricted depth of discharge ranges)[J]. Journal of Power Sources, 2014, 258:210-217.
doi: 10.1016/j.jpowsour.2014.02.018
|
[13] |
ECKER M, NIETO N, KÄBITZ S, et al. Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries[J]. Journal of Power Sources, 2014, 248:839-851.
doi: 10.1016/j.jpowsour.2013.09.143
|
[14] |
WANG J, PUREWAL J, LIU P, et al. Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide+spinel manganese oxide positives:Part 1,aging mechanisms and life estimation[J]. Journal of Power Sources, 2014, 269:937-948.
doi: 10.1016/j.jpowsour.2014.07.030
|
[15] |
SAFARI M, DELACOURT C. Aging of a commercial graphite/LiFePO4 cell[J]. Journal of the Electrochemical Society, 2011, 158(10): A1123.
doi: 10.1149/1.3614529
|
[16] |
KASSEM M, BERNARD J, REVEL R, et al. Calendar aging of a graphite/LiFePO4 cell[J]. Journal of Power Sources, 2012, 208:296-305.
doi: 10.1016/j.jpowsour.2012.02.068
|
[17] |
GOU B, XU Y, FANG S, et al. Remaining useful life prediction for lithium-ion battery using ensemble learning method[C/CD]// 2019IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019.
|
[18] |
REICHERT M, ANDRE D, RÖSMANN A, et al. Influence of relaxation time on the lifetime of commercial lithium-ion cells[J]. Journal of Power Sources, 2013, 239:45-53.
doi: 10.1016/j.jpowsour.2013.03.053
|
[19] |
GOU B, XU Y, FENG X. An ensemble learning-based data-driven method for online state-of-health estimation of lithium-ion batteries[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2):422-436.
doi: 10.1109/TTE.2020.3029295
|
[20] |
陈佳琦, 赵鹏祥, 祁宁, 等. 基于BP神经网络的油松人工林树高模型研究[J]. 西北林学院学报, 2020, 35(1):212-217,245.
|
|
CHEN Jiaqi, ZHAO Pengxiang, QI Ning, et al. Establishment of tree height model of pinus tabuli formis plantation based on BP neural network[J]. Journal of Northwest Forestry University, 2020, 35(1):212-217,245.
|
[21] |
GROLLEAU S, DELAILLE A, GUALOUS H, et al. Calendar aging of commercial graphite/LiFePO4 cell—Predicting capacity fade under time dependent storage conditions[J]. Journal of Power Sources, 2014, 255:450-458.
doi: 10.1016/j.jpowsour.2013.11.098
|
[22] |
SU L, ZHANG J, HUANG J, et al. Path dependence of lithium ion cells aging under storage conditions[J]. Journal of Power Sources, 2016, 315:35-46.
doi: 10.1016/j.jpowsour.2016.03.043
|
[23] |
HAHN S L, STORCH M, SWAMINATHAN R, et al. Quantitative validation of calendar aging models for lithium-ion batteries[J]. Journal of Power Sources, 2018, 400:402-414.
doi: 10.1016/j.jpowsour.2018.08.019
|
[24] |
KRUPP A, BECKMANN R, DIEKMANN T, et al. Calendar aging model for lithium-ion batteries considering the influence of cell characterization[J]. Journal of Energy Storage, 2022, 45:103506.
doi: 10.1016/j.est.2021.103506
|