Construction and Estimation of Battery State of Health Using a De-LSTM Model Based on Real Driving Data

Haitao Min, Yukun Yan, Weiyi Sun (), Yuanbin Yu, Rui Jiang and Fanyu Meng
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Haitao Min: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Yukun Yan: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Weiyi Sun: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Yuanbin Yu: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Rui Jiang: China FAW Corporation Limited, Changchun 130013, China
Fanyu Meng: China FAW Corporation Limited, Changchun 130013, China

Energies, 2023, vol. 16, issue 24, 1-23

Abstract: Electric vehicles (EVs) have considerable potential in promoting energy efficiency and carbon neutrality. State of health (SOH) estimations for battery systems can be effective for avoiding accidents involving EVs. However, existing methods have rarely been developed using real driving data. The complex working environments of EVs and their limited data acquisition capability increase the challenges for estimating SOH. In this study, a novel battery SOH definition for EVs was established by analyzing and extracting six potential SOH indicators from driving data. The definition proposed using the entropy weight method (EWM) described the degradation trend for different EV batteries. Combined with a denoising autoencoder, a novel long short-term memory neural network model was established for SOH prediction. It can learn robust features using noisy input data without being affected by different environments or driver behaviors. The network achieved a maximum mean absolute percentage error (MAPE) of 0.8827% and root mean square error (RMSE) of 0.9802%. The results have shown that the proposed method has a higher level of accuracy and is more robust than existing methods in the field.

Keywords: electric vehicles; state of health; lithium-ion battery; De-LSTM; entropy weight method (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: 2023
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