An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery

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.

Automated summary

This paper proposed an encoder-decoder model based on deep learning to estimate the state of health of lithium-ion batteries. Experimental results show that the model has good adaptability and high accuracy.