1 |
董渊昌, 庞晓琼, 贾建芳, 等. 基于SVD-SAE-GPR的锂离子电池RUL预测[J]. 储能科学与技术, 2023, 12(4): 1257-1267.
|
|
DONG Y C, PANG X Q, JIA J F, et al. Remaining useful life prediction of lithium-ion batteries based on SVD-SAE-GPR[J]. Energy Storage Science and Technology, 2023, 12(4): 1257-1267.
|
2 |
张若可, 郭永芳, 余湘媛, 等. 基于数据驱动的锂离子电池RUL预测综述[J]. 电源学报, 2021, 20(4): 1-15.
|
|
ZHANG R K, GUO Y F, YU X Y, et al. A review of RUL prediction for lithium-ion battery based on data-driven[J]. Journal of Power Supply, 2021, 20(4): 1-15.
|
3 |
VICHARD L, RAVEY A, VENET P, et al. A method to estimate battery SOH indicators based on vehicle operating data only[J]. Energy, 2021, 225: 120235.
|
4 |
XU J, MEI X S, WANG X, et al. A relative state of health estimation method based on wavelet analysis for lithium-ion battery cells[J]. IEEE Transactions on Industrial Electronics, 2021, 68(8): 6973-6981.
|
5 |
LIU C, WANG Y J, CHEN Z H. Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system[J]. Energy, 2019, 166: 796-806.
|
6 |
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(8): 791-797.
|
|
GAO R J, LÜ Z Q, ZHAO S, et al. Health state estimation of Li-ion batteries based on electrochemical model[J]. Transactions of Beijing Institute of Technology, 2022, 42(8): 791-797.
|
7 |
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 L X, PANG H, JIN J M, 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.
|
8 |
颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948.
|
|
YAN X W, DENG H R, GUO Q, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3937-3948.
|
9 |
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.
|
10 |
王凡, 史永胜, 刘博亲, 等. 基于注意力改进BiGRU的锂离子电池健康状态估计[J]. 储能科学与技术, 2021, 10(6): 2326-2333.
|
|
WANG F, SHI Y S, LIU B Q, et al. Health state estimation of lithium-ion batteries based on attention augmented BiGRU[J]. Energy Storage Science and Technology, 2021, 10(6): 2326-2333.
|
11 |
孙广明, 贾新羽, 陈良亮. 基于K近邻回归的锂离子电池健康状态估计[J]. 电源技术, 2022, 46(8): 872-875.
|
|
SUN G M, JIA X Y, CHEN L L. State of health estimation of lithium-ion battery based on K nearest neighbour regression and IC curve[J]. Chinese Journal of Power Sources, 2022, 46(8): 872-875.
|
12 |
肖浩逸, 何晓霞, 梁佳佳, 等. 一种基于模态分解和机器学习的锂电池寿命预测方法[J]. 储能科学与技术, 2022, 11(12): 3999-4009.
|
|
XIAO H Y, HE X X, LIANG J J, et al. A lithium battery life-prediction method based on mode decomposition and machine learning[J]. Energy Storage Science and Technology, 2022, 11(12): 3999-4009.
|
13 |
丁阳征, 贾建芳. 改进PSO优化ELM预测锂离子电池剩余寿命[J]. 电子测量与仪器学报, 2019, 33(2): 72-79.
|
|
DING Y Z, JIA J F. Improved PSO optimized extreme learning machine predicts remaining useful life of lithium-ion battery[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(2): 72-79.
|
14 |
LI P H, ZHANG Z J, GROSU R, et al. An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111843.
|
15 |
王朋凯, 张新燕, 张光昊. 基于ResNet-Bi-LSTM-Attention的锂离子电池剩余使用寿命预测[J]. 储能科学与技术, 2023, 12(4): 1215-1222.
|
|
WANG P K, ZHANG X Y, ZHANG G H. Remaining useful life prediction of lithium-ion batteries based on ResNet-Bi-LSTM-Attention model[J]. Energy Storage Science and Technology, 2023, 12(4): 1215-1222.
|
16 |
熊庆,邸振国,汲胜昌.锂离子电池健康状态估计及寿命预测研究进展综述[J].高电压技术,2023,49(2):1-14.
|
|
XIONG Q, DI Z G, JI S C. Review on health state estimation and life prediction of lithium-ion batteries[J]. High Voltage Engineering,2023,49(2):1-14.
|
17 |
WANG Y X, MARKERT R, XIANG J W, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems and Signal Processing, 2015, 60/61: 243-251.
|
18 |
叶鑫, 王海瑞, 李远博, 等. 基于VMD和优化的LSTM锂离子电池寿命预测方法[J]. 电子测量技术, 2022, 45(23): 153-158.
|
|
YE X, WANG H R, LI Y B, et al. Remaining useful life prediction method of lithium-ion battery based on variational mode decomposition and optimized LSTM[J]. Electronic Measurement Technology, 2022, 45(23): 153-158.
|
19 |
丁建立, 张琪琪, 王静, 等. 基于Transformer-VAE的ADS-B异常检测方法[J]. 系统工程与电子技术, 2023, 45(3): 1-13.
|
|
DING J L, ZHANG Q Q, WANG J,et al. ADS-B anomaly detection method based on Transformer-VAE[J]. Systems Engineering and Electronics, 2023, 45(3): 1-13.
|
20 |
祁柏林, 赵娅倩, 魏建勋, 等. 基于ResGCN-GRU的大气污染风险源识别[J]. 计算机系统应用, 2023, 32(6): 301-307.
|
|
QI B L, ZHAO Y Q, WEI J X, et al. Identification of air pollution risk sources based on ResGCN-GRU[J]. Computer Systems and Applications, 2023, 32(6): 301-307.
|
21 |
郝可青, 吕志刚, 邸若海, 等. 基于鲸鱼算法优化长短时记忆神经网络的锂电池剩余寿命预测[J]. 科学技术与工程, 2022, 22(29): 12900-12908.
|
|
HAO K Q, LÜ Z G, DI R H, et al. Remaining useful life prediction of lithium battery based on long short-term memory optimized by whale optimization algorithm[J]. Science Technology and Engineering, 2022, 22(29): 12900-12908.
|
22 |
张军. VMD在信号分解中的K值确定方法[J]. 兰州文理学院学报(自然科学版), 2022, 36(4): 75-79.
|
|
ZHANG J. The K value determination method of VMD in signal decomposition[J]. Journal of Lanzhou University of Arts and Science (Natural Sciences), 2022, 36(4): 75-79.
|
23 |
丁恒, 黄凯, 田海建. 基于VMD和ISSA-ELM的锂离子电池剩余使用寿命预测[J]. 电源学报, 2022, 21(6): 1-11.
|
|
DING H, HUANG K, TIAN H J. Prediction of remaining service life of lithium-ion battery based on VMD and ISSA-EL[J]. Journal of Power Supply, 2022, 21(6): 1-11.
|
24 |
戴彦文, 于艾清. 基于健康特征参数的CNN-LSTM&GRU组合锂电池SOH估计[J]. 储能科学与技术, 2022, 11(5): 1641-1649.
|
|
DAI Y W, YU A Q. Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation[J]. Energy Storage Science and Technology, 2022, 11(5): 1641-1649.
|