首页|Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction
Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction
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NSTL
Elsevier
? 2022 Elsevier LtdOne of the main concerns of battery management systems is predicting the degradation of lithium-ion batteries, which remaining useful life prediction is an essential tool for prognostic and health management of batteries. In this study, we develop a novel prognostic architecture that is based on a least-squares generative adversarial network with the gated recurrent unit as the generator and multi-layer perceptron as the discriminator and use it to predict the Lithium-ion batteries’ remaining useful life. The proposed method aims to learn the probability distribution of future values in an adversarial training fashion. This generative adversarial network gives more penalties to large errors and addresses the vanishing gradient problem during training. As a result, the predicted values will get closer to the actual data. Furthermore, to obtain high prediction accuracy, time-domain features are evaluated using statistical formulas. The most important features are then selected using the random forest algorithm and fed to the network as a multivariate input set. The performances of the proposed method are tested using a battery degradation dataset from the data repository of Prognostics Center of Excellence at NASA. Furthermore, experimental data from lithium-ion cells at different current rates are conducted for evaluation and verification. The obtained outcomes demonstrate that the designed model achieves the low prediction error of 2.63% and maximum absolute error of 0.02.
Gated recurrent unitGenerative adversarial networkLithium-ion batteryPrognosticRemaining useful life
Ardeshiri R.R.、Liu M.、Razavi-Far R.、Li T.、Wang X.、Ma C.
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Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education and the Department of Electrical Engineering Shanghai Jiao Tong University
Department of Electrical and Computer Engineering University of Windsor Windsor
National & Local Joint Engineering Research Center for Reliability Technology of Energy Internet Intelligent Terminal Core Chip Beijing Smart-Chip Microelectronics Technology Co Ltd.
China Automotive Technology and Research Center Co. Ltd. Tianjin
University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University
NSTL
Elsevier
