11 |
邢子轩, 张凡, 武明虎, 等. 基于WD-GRU的锂离子电池剩余寿命预测[J]. 电源技术, 2022, 46(8): 867-871.
|
|
XING Z X, ZHANG F, WU M H, et al. Remaining life prediction of lithium ion batteries based on WD-GRU[J]. Chinese Journal of Power Sources, 2022, 46(8): 867-871.
|
12 |
陈欣, 李云伍, 梁新成, 等. 基于模态分解的Transformer-GRU联合电池健康状态估计[J]. 储能科学与技术, 2023, 12(9): 2927-2936.
|
|
CHEN X, LI Y W, LIANG X C, et al. Battery health state estimation of combined Transformer-GRU based on modal decomposition[J]. Energy Storage Science and Technology, 2023, 12(9): 2927-2936.
|
13 |
CHEN D Q, HONG W C, ZHOU X Z. Transformer network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Access, 2022, 10: 19621-19628.
|
14 |
ZENG A L, CHEN M X, ZHANG L, et al. Are transformers effective for time series forecasting? [EB/OL]. 2022: arXiv: 2205.13504. http://arxiv.org/abs/2205.13504
|
15 |
SHI S Q, GAO J, LIU Y, et al. Multi-scale computation methods: Their applications in lithium-ion battery research and development[J]. Chinese Physics B, 2016, 25(1): 018212.
|
16 |
ZHOU H Y, ZHANG S H, PENG J Q, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting[J]. ArXiv e-Prints, 2020: arXiv: .
|
17 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[EB/OL]. 2017: arXiv: 1706.03762. http://arxiv.org/abs/1706.03762
|
18 |
余宇峰, 朱跃龙, 万定生, 等. 基于滑动窗口预测的水文时间序列异常检测[J]. 计算机应用, 2014, 34(8): 2217-2220, 2226.
|
|
YU Y F, ZHU Y L, WAN D S, et al. Time series outlier detection based on sliding window prediction[J]. Journal of Computer Applications, 2014, 34(8): 2217-2220, 2226.
|
19 |
WILLIARD N, HE W, OSTERMAN M, et al. Comparative analysis of features for determining state of health in lithium-ion batteries[J]. International Journal of Prognostics and Health Management, 2020, 4(1): doi: 10.36001/IJPHM.2013.V4I1.1437.
|
20 |
SAHA B, GOEBEL K. Battery data set[R]. NASA Ames Prognostics Data Analysis, 2007.
|
1 |
LIU X Q, ZHU S L, LIANG Y Q, et al. 3D N-doped mesoporous carbon/SnO2 with polypyrrole coating layer as high-performance anode material for Li-ion batteries[J]. Journal of Alloys and Compounds, 2022, 892: 162083.
|
2 |
刘巧云, 祁秀秀, 郝卫强. 锂电池用正极材料钴酸锂改性研究进展[J]. 电源技术, 2022, 46(12): 1357-1359.
|
|
LIU Q Y, QI X X, HAO W Q. Research progress on modification of lithium cobalt oxide as cathode material for lithium battery[J]. Chinese Journal of Power Sources, 2022, 46(12): 1357-1359.
|
3 |
杨欢, 乔志军. 纳米SnO2锂电负极材料的研究进展[J]. 云南化工, 2020, 47(3): 5-6.
|
|
YANG H, QIAO Z J. Research progress of nano SnO2 lithium battery anode materials[J]. Yunnan Chemical Technology, 2020, 47(3): 5-6.
|
4 |
张雨, 吕瑞华, 聂丽宇, 等. 浅谈基于BP神经网络对实验室锂电池循环性能预测研究[J]. 江西化工, 2020(3): 93-95.
|
|
ZHANG Y, LYU R H, NIE L Y, et al. Study on prediction of lithium battery cycle performance based on BP neural network[J]. Jiangxi Chemical Industry, 2020(3): 93-95.
|
5 |
黄奂奇. 基于电化学热耦合模型的锂离子电池老化状态估计[D]. 哈尔滨: 哈尔滨工业大学, 2021.
|
|
HUANG H Q. Aging state estimation of lithium ion battery based on electrochemical thermal coupling model[D].Harbin: Harbin Institute of Technology, 2021.
|
6 |
黄泽波. 基于电化学模型估算锂电池SOC的方法研究[J]. 电源世界, 2016(7): 25-27.
|
|
HUANG Z B. Lithium battery SOC estimation method study based on electrochemical model[J]. The World of Power Supply, 2016(7): 25-27.
|
7 |
刘月峰, 赵光权, 彭喜元. 多核相关向量机优化模型的锂电池剩余寿命预测方法[J]. 电子学报, 2019, 47(6): 1285-1292.
|
|
LIU Y F, ZHAO G Q, PENG X Y. A lithium-ion battery remaining using life prediction method based on multi-kernel relevance vector machine optimized model[J]. Acta Electronica Sinica, 2019, 47(6): 1285-1292.
|
8 |
王超, 范兴明, 张鑫, 等. 基于IRVM的锂电池荷电状态评估方法与仿真验证[J]. 电子技术应用, 2018, 44(12): 127-130, 134.
|
|
WANG C, FAN X M, ZHANG X, et al. An evaluation method of Li-ion batteries state of charge based on IRVM and verified by simulation[J]. Application of Electronic Technique, 2018, 44(12): 127-130, 134.
|
9 |
吴章玉, 朱成杰, 王鸣雁. 基于RNN的锂电池健康预测[J]. 绿色科技, 2021, 23(18): 201-203.
|
|
WU Z Y, ZHU C J, WANG M Y. Health prediction of lithium battery based on RNN[J]. Journal of Green Science and Technology, 2021, 23(18): 201-203.
|
10 |
GONG Y D, ZHANG X Y, GAO D Z, et al. State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm[J]. Journal of Energy Storage, 2022, 53(9): 1-13.
|