Abstract
The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To overcome these problems, this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network. First, this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features, which overcomes the problems of redundant model information and low computational efficiency. This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life. Lastly, the attention mechanism is used to give greater weight to features that have a greater impact on the target value, which enhances the learning effect of the model on the long input sequence. To verify the efficacy of the proposed model, this paper uses NASA’s lithium-ion battery cycle life data set.
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References
Jingliang Zhang and Jay Lee, Journal of Power Sources 196, 6007 (2011).
Wei He, Nicholas Williard, Michael Osterman and Michael Pecht, Journal of Power Sources 196, 10314 (2011).
Xiaosong Hu, Changfu Zou, Caiping Zhang and Yang Li, IEEE Power Energy Magazine 15, 20 (2017).
Liu Datong, Zhou Jianbao and Guo Limeng, Journal of Instrumentation 36, 1 (2015).
Xin Xu and Nan Chen, Reliability Engineering & System Safety 159, 47 (2017).
Xiaosong Hu, Dongpu Cao and Bo Egardt, IEEE/ASME Transactions on Mechatronics 23, 161 (2018).
Meru A. Patil, Piyush Tagade, Krishnan S. Hariharan, Subramanya M. Kolake, Taewon Song, Taejung Yeo and Seokgwang Doo, Applied Energy 159, 285 (2015).
WANG D, MIAO Q and PECHT M, Journal of Power Sources 239, 253 (2013).
LIU JIE and SAXENA, An Adaptive Recurrent Nm-ion Batteries, Annual Conference of the Prognostics and Health Management Society, 1 (2010).
Rezvani Mohammad, AbuAil Mohamed, Lee Seungchul and Jay Lee, A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM), Sae Technical Paper, 2011-01-2247.
WU J, ZHANG C and CHEN Z, Applied Energy 173, 134 (2016).
REN Lei, Sun Yaqiang, Cui Jin and Zhang Lin, Journal of Manufacturing Systems 48, 71 (2018).
Wu Yuting, Mei Yuan, Dong Shaopeng, Lin Li and Liu Yingqi, Neurocomputing 275, 167 (2018).
ZOU H, HASTIE T and TIBSHIRANI R, Journal of Computational and Graphical Statistics 15, 265 (2006).
Md Shamim Reza and Jinwen Ma, ICA and PCA Integrated Feature Extraction for Classification, IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China, 453 (2016).
BURDA Y, GROSSE R and SALAKHUTDINOV R, Computer Science 4, 112 (2016).
Chen Geheng, Information Technology 2, 149 (2018).
CHOI H, CHO K and BENGIO Y, Neurocomputing 284, 171 (2018).
Jiang Yuanyuan, Liu Zhu, Luo Hui and Wang Hui, Journal of Electronic Measurement and Instrument 30, 179 (2016).
Wang Haixia and Li Kaiyong, Computer Measurement and Control 27, 271 (2019).
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This work has been supported by the National Natural Science Foundation of China (No.61871350), the Zhejiang Science and Technology Plan Project (No.2019C011123), and the Zhejiang Province Basic Public Welfare Research Project (No.LGG19F030011).
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Wang, Xb., Wu, Ft. & Yao, Mh. A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism. Optoelectron. Lett. 16, 410–417 (2020). https://doi.org/10.1007/s11801-020-9214-y
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DOI: https://doi.org/10.1007/s11801-020-9214-y