An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery. (February 2022)

• Record Type: Journal Article
• Authors: Gong, Qingrui; Wang, Ping; Cheng, Ze
• Is Part Of: Journal of energy storage. Volume 46(2022)
• Publication Date: 2022-02
• Subjects: Lithium-ion batteries -- State of health -- Charging curves -- Encoder-decoder model -- Deep learning -- Simple recurrent unit; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.103804
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: The estimation model of SOH is established by deep learning method. Extracting feature information by using CNN module and ULSAM module. Encoding feature information by using SRU module. Introducing attention mechanism to decoder to decode long encoding sequence. Abstract: Accurate estimation of state of health (SOH) of lithium-ion batteries is an important guarantee for the safe and stable operation of these batteries, which is a key technology in battery management system (BMS). The charging curves of lithium-ion batteries with different aging degrees are also different. Based on this fact, this paper proposes an encoder-decoder model based on deep learning to establish the mapping relationship between battery charging curves and the value of SOH. The model consists of encoder and decoder. The encoder is a hybrid neural network composed of two-dimensional convolution module, ultra-lightweight subspace attention mechanism (ULSAM) module and simple recurrent unit (SRU) module, which can effectively encode the sampling data of the charging curves and generate the encoding sequence. The decoder is mainly composed of back propagation (BP) neural network, which is responsible for decoding the encoding sequence and output an estimate of the SOH. For long encoding sequence, a decoder with attention mechanism is proposed to improve the estimation accuracy of the model. Experimental results show that the proposed model has good adaptability to different types of batteries, can adapt to various sampling modes of charging curves, and has high estimation accuracy.