1 |
梁新成, 张勉, 黄国钧. 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.
|
|
LIANG X C, ZHANG M, HUANG G J. Review on lithium-ion battery modeling methods based on BMS[J]. Energy Storage Science and Technology, 2020, 9(6): 1933-1939.
|
2 |
OMARIBA Z B, ZHANG L J, SUN D B. Review on health management system for lithium-ion batteries of electric vehicles[J]. Electronics, 2018, 7: 72.
|
3 |
袁烨, 张永, 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状[J]. 自动化学报, 2020, 46(10): 2013-2030.
|
|
YUAN Y, ZHANG Y, DING H. Research on key technology of industrial artificial intelligence and its application in predictive maintenance[J]. Acta Automatica Sinica, 2020, 46(10): 2013-2030.
|
4 |
ZHANG W J. A review of the electrochemical performance of alloy anodes for lithium-ion batteries[J]. Journal of Power Sources, 2011, 196(1): 13-24.
|
5 |
康鑫, 时玮, 陈洪涛. 基于锂离子电池简化电化学模型的参数辨识[J]. 储能科学与技术, 2020, 9(3): 969-978.
|
|
KANG X, SHI W, CHEN H T. Parameter identification based on simplified electrochemical model of lithium ion battery[J]. Energy Storage Science and Technology, 2020, 9(3): 969-978.
|
6 |
朱奕楠, 吕桃林, 赵芝芸, 等. 基于并行卡尔曼滤波器的锂离子电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2352-2362.
|
|
ZHU Y N, LÜ T L, ZHAO Z Y, et al. State of charge estimation of lithium ion battery based on parallel Kalman filter[J]. Energy Storage Science and Technology, 2021, 10(6): 2352-2362.
|
7 |
任璞, 王顺利, 何明芳, 等. 基于内阻增加和容量衰减双重标定的锂电池健康状态评估[J]. 储能科学与技术, 2021, 10(2): 738-743.
|
|
REN P, WANG S L, HE M F, et al. State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading[J]. Energy Storage Science and Technology, 2021, 10(2): 738-743.
|
8 |
CHANG Y, FANG H J, ZHANG Y. A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery[J]. Applied Energy, 2017, 206: 1564-1578.
|
9 |
WEI J W, DONG G Z, CHEN Z H. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5634-5643.
|
10 |
陈翌, 白云飞, 何瑛. 数据驱动的锂电池健康状态估算方法比较[J]. 储能科学与技术, 2019, 8(6): 1204-1210.
|
|
CHEN Y, BAI Y F, HE Y. Comparison of data-driven lithium battery state of health estimation methods[J]. Energy Storage Science and Technology, 2019, 8(6): 1204-1210.
|
11 |
SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4(5): 383-391.
|
12 |
PATIL M A, TAGADE P, HARIHARAN K S, et al. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation[J]. Applied Energy, 2015, 159: 285-297.
|
13 |
CHANG Y, FANG H J. A hybrid prognostic method for system degradation based on particle filter and relevance vector machine[J]. Reliability Engineering & System Safety, 2019, 186: 51-63.
|
14 |
李练兵, 李思佳, 李洁, 等. 基于差分电压和Elman神经网络的锂离子电池RUL预测方法[J]. 储能科学与技术, 2021, 10(6): 2373-2384.
|
|
LI L B, LI S J, LI J, et al. RUL prediction of lithium-ion battery based on differential voltage and Elman neural network[J]. Energy Storage Science and Technology, 2021, 10(6): 2373-2384.
|
15 |
陈峥, 李磊磊, 舒星, 等. 基于特征处理与径向基神经网络的锂电池剩余容量估算方法[J]. 储能科学与技术, 2021, 10(1): 261-270.
|
|
CHEN Z, LI L L, SHU X, et al. Efficient remaining capacity estimation method for LIB based on feature processing and the RBF neural network[J]. Energy Storage Science and Technology, 2021, 10(1): 261-270.
|
16 |
CHEN L, ZHANG Y, ZHENG Y, et al. Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation[J]. Neurocomputing, 2020, 414: 245-254.
|
17 |
XUE Z W, ZHANG Y, CHENG C, et al. Remaining useful life prediction of lithium-ion batteries with adaptive unscented Kalman filter and optimized support vector regression[J]. Neurocomputing, 2020, 376: 95-102.
|
18 |
SATEESH BABU G, ZHAO P L, LI X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//Database Systems for Advanced Applications, 2016.
|
19 |
MA G J, ZHANG Y, CHENG C, et al. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network[J]. Applied Energy, 2019, 253: 113626.
|
20 |
易灵芝, 张宗光, 范朝冬, 等. 基于EEMD-GSGRU的锂电池寿命预测[J]. 储能科学与技术, 2020, 9(5): 1566-1573.
|
|
YI L Z, ZHANG Z G, FAN C D, et al. Life prediction of lithium battery based on EEMD-GSGRU[J]. Energy Storage Science and Technology, 2020, 9(5): 1566-1573.
|
21 |
QIN T C, ZENG S K, GUO J B, et al. A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena[J]. Energies, 2016, 9(11): 896.
|
22 |
DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
|
23 |
BOYD S. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine Learning, 2010, 3(1): 1-122.
|
24 |
CAO L Y. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D: Nonlinear Phenomena, 1997, 110(1/2): 43-50.
|
25 |
SHEN Z P, ZHANG Y M, LU J W, et al. SeriesNet: A generative time series forecasting model[C]//2018 International Joint Conference on Neural Networks (IJCNN). July 8-13, 2018, Rio de Janeiro, Brazil. IEEE, 2018: 1-8.
|
26 |
LEA C, FLYNN M D, VIDAL R, et al. Temporal convolutional networks for action segmentation and detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 1003-1012.
|
27 |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 770-778.
|
28 |
CHANG S Y, ZHANG Y, HAN W, et al. Dilated recurrent neural networks[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York, USA: Curran Associates Inc., 2017: 76-86.
|
29 |
LAI G K, CHANG W C, YANG Y M, et al. Modeling long- and short-term temporal patterns with deep neural networks[C]//SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 95-104.
|
30 |
SAHA B, GOEBEL K. Battery data set[R]. NASA Ames Prognostics Data Repository, 2007.
|
31 |
LIU D T, ZHOU J B, LIAO H T, et al. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(6): 915-928.
|
32 |
FAN J M, FAN J P, LIU F, et al. A novel machine learning method based approach for Li-ion battery prognostic and health management[J]. IEEE Access, 2019, 7: 160043-160061.
|