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

Author

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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)

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.

Suggested Citation

  • Haitao Min & Yukun Yan & Weiyi Sun & Yuanbin Yu & Rui Jiang & Fanyu Meng, 2023. "Construction and Estimation of Battery State of Health Using a De-LSTM Model Based on Real Driving Data," Energies, MDPI, vol. 16(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8088-:d:1301275
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    References listed on IDEAS

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