Life prediction of lithium-ion batteries based on stacked denoising autoencoders

Fan Xu, Fangfang Yang, Zicheng Fei, Zhelin Huang and Kwok-Leung Tsui

Reliability Engineering and System Safety, 2021, vol. 208, issue C

Abstract: Accurate life prediction of lithium-ion batteries is important to help assess battery quality in advance, improve long-term battery planning, and subsequently guarantee the safety and reliability of battery operations. In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features. In general, the SDAE contains autoencoders and uses a deep network architecture to learn the complex nonlinear input-output relationship in a layer-by-layer fashion. Many features enabling the life prediction of lithium-ion batteries are extracted from discharge temperature and voltage curves. As redundancies in these features may result in poor prediction accuracy, a clustering by fast search (CFS) method is adopted to filter and select essential features. The CFS selects effective features by aggregating the types of battery features into clusters. All selected features are then fed into the SDAE to predict battery life cycle. Key hyperparameters are investigated, such as the number of iterations, the learning rate, and the denoising rate of the SDAE network. Experimental results show that the proposed selected-features-based deep learning method can provide more accurate and efficient battery life predictions with less fluctuation than the method without feature selection.

Keywords: Lithium-ion battery; Life prediction; Stacked denoising autoencoder; Clustering by fast search; Deep learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308838

DOI: 10.1016/j.ress.2020.107396

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