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End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data

Author

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  • Xiong, Xin
  • Wang, Yujie
  • Jiang, Cong
  • Zhang, Xingchen
  • Xiang, Haoxiang
  • Chen, Zonghai

Abstract

Accurately monitoring the State of Health (SOH) of lithium-ion batteries is one of the key technologies critical for the safe and reliable operation of Electric Vehicles (EVs). However, the random charging–discharging behavior of EV users results in differentiated, incomplete, and limited health information in single charging–discharging data, leading to ongoing challenges in SOH estimation. Firstly, an efficient data preprocessing algorithm is designed to automatically handle data slicing, cleaning, alignment, and recombination. Secondly, recognizing the relatively slow change in SOH, multi-neighboring incomplete charging segments are aligned and used as inputs to the SOH estimation model, and then the average capacity of these neighboring charging data is computed as SOH labels. Furthermore, based on the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) structure, an end-to-end SOH estimation model is constructed. This model initially utilizes a multi-channel CNN to fuse health information from multi-neighboring incomplete charging segments and then employs LSTM for SOH prediction. Finally, on a dataset composed of 20 EVs, the proposed SOH estimation method is rigorously validated using K-fold cross-validation. The results demonstrated that the Mean Absolute Error (MAE) is within 2.13%, and the Root Mean Square Error (RMSE) is below 2.74%, highlighting the model’s high estimation accuracy.

Suggested Citation

  • Xiong, Xin & Wang, Yujie & Jiang, Cong & Zhang, Xingchen & Xiang, Haoxiang & Chen, Zonghai, 2024. "End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002664
    DOI: 10.1016/j.energy.2024.130495
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    References listed on IDEAS

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    1. Xu, Zhicun & Xie, Naiming & Diao, Huakang, 2023. "Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model," Energy, Elsevier, vol. 283(C).
    2. Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
    3. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    4. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    5. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    6. Tian, Jiaqiang & Liu, Xinghua & Li, Siqi & Wei, Zhongbao & Zhang, Xu & Xiao, Gaoxi & Wang, Peng, 2023. "Lithium-ion battery health estimation with real-world data for electric vehicles," Energy, Elsevier, vol. 270(C).
    7. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    8. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    9. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    10. Deng, Zhongwei & Xu, Le & Liu, Hongao & Hu, Xiaosong & Duan, Zhixuan & Xu, Yu, 2023. "Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles," Applied Energy, Elsevier, vol. 339(C).
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