Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism

Shuo Sun, Junzhong Sun, Zongliang Wang, Zhiyong Zhou and Wei Cai
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Shuo Sun: Navy Submarine Academy, Qingdao 266042, China
Junzhong Sun: Navy Submarine Academy, Qingdao 266042, China
Zongliang Wang: Navy Submarine Academy, Qingdao 266042, China
Zhiyong Zhou: Navy Submarine Academy, Qingdao 266042, China
Wei Cai: Navy Submarine Academy, Qingdao 266042, China

Energies, 2022, vol. 15, issue 12, 1-17

Abstract: During the use and management of lead–acid batteries, it is very important to carry out prediction and study of the state of the health (SOH) of the battery. To this end, this paper proposes a SOH prediction method for lead–acid batteries based on the CNN-BiLSTM-Attention model. The model utilizes the convolutional neural network (CNN) to carry out feature extraction and data dimension reduction in the input factors of model, and then these factors are used as the input of the bidirectional long short-term memory network (BiLSTM). The BiLSTM is used to learn the temporal correlation information in the local features of input time series bidirectionally. The attention mechanism is introduced to assign more attention to key features in the input sequence with more significant influence on the output result by assigning weights to important features, and finally, multi-step prediction of the battery SOH is realized. Compared with the prediction results of battery SOH using other neural network methods, the method proposed in this study can provide higher prediction accuracy and achieve accurate multi-step prediction of battery SOH. Measured results show that most of the multi-step prediction errors of the proposed method are controlled within 3%.

Keywords: state of health (SOH); convolutional neural network (CNN); bidirectional long short-term memory network (BiLSTM); attention mechanism (Attention); multi-step prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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