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Data-physics-driven estimation of battery state of charge and capacity

Author

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  • Tang, Aihua
  • Huang, Yukun
  • Xu, Yuchen
  • Hu, Yuanzhi
  • Yan, Fuwu
  • Tan, Yong
  • Jin, Xin
  • Yu, Quanqing

Abstract

High-power density lithium-ion batteries have been utilized in both energy storage and high rate charging and discharging applications. Accurate state estimation is fundamental to enhancing battery life and safety. Therefore, a data-physics-driven estimation of the state of charge and capacity for lithium-titanate batteries was conducted using Gaussian distribution fusion. Firstly, a fractional order model was selected as the physical analytical model for lithium-titanate batteries. Secondly, a data-driven model that combines convolutional neural networks and long short-term memory networks was employed to predict the battery state of charge. Thirdly, the physical analytical model and data-driven model were fused to estimate the state of charge by employing the principle of Gaussian distribution fusion. Finally, both the state of charge and actual capacity of the battery were jointly estimated. The results showcase the capability of the proposed method to accurately estimate the state of charge and capacity, ensuring an accuracy level within 1%. Furthermore, this approach exhibits a 20% improvement in accuracy compared to traditional methods.

Suggested Citation

  • Tang, Aihua & Huang, Yukun & Xu, Yuchen & Hu, Yuanzhi & Yan, Fuwu & Tan, Yong & Jin, Xin & Yu, Quanqing, 2024. "Data-physics-driven estimation of battery state of charge and capacity," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224005486
    DOI: 10.1016/j.energy.2024.130776
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    1. Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
    2. Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    3. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    4. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    5. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    6. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    8. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    9. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
    10. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    11. Yu, Quanqing & Nie, Yuwei & Peng, Simin & Miao, Yifan & Zhai, Chengzhi & Zhang, Runfeng & Han, Jinsong & Zhao, Shuo & Pecht, Michael, 2023. "Evaluation of the safety standards system of power batteries for electric vehicles in China," Applied Energy, Elsevier, vol. 349(C).
    12. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    13. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    14. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    15. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
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    4. Qingbin Wang & Hangang Yan & Yuxi Wang & Yun Yang & Xiaoguang Liu & Zhuoqi Zhu & Gancai Huang & Zheng Huang, 2025. "Probabilistic State of Health Prediction for Lithium-Ion Batteries Based on Incremental Capacity and Differential Voltage Curves," Energies, MDPI, vol. 18(20), pages 1-16, October.
    5. Zhang, Jiahao & Chen, Jiadui & Liu, Dan & He, Ling & Yang, Kai & Du, Feilong & Ye, Wen & Zhang, Xiaoxiang, 2025. "Multi-state joint prediction algorithm for lithium battery packs based on data-driven and physical models," Energy, Elsevier, vol. 322(C).
    6. Tang, Aihua & Xu, Yuchen & Hu, Yuanzhi & Tian, Jinpeng & Nie, Yuwei & Yan, Fuwu & Tan, Yong & Yu, Quanqing, 2024. "Battery state of health estimation under dynamic operations with physics-driven deep learning," Applied Energy, Elsevier, vol. 370(C).
    7. Tao, Junjie & Wang, Shunli & Cao, Wen & Cui, Yixiu & Fernandez, Carlos & Guerrero, Josep M., 2024. "Innovative multiscale fusion – Antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 312(C).
    8. Jin, Zhaorui & Fu, Shiyi & Fan, Hongtao & Tao, Yulin & Dong, Yachao & Wang, Yu & Sun, Yaojie, 2025. "Edge-cloud collaborative method for state of charge estimation of lithium-ion batteries by combining Kalman filter and deep learning," Energy, Elsevier, vol. 332(C).
    9. Zheng, Hanbo & Wang, Yanan & Du, Qi & Wang, Shusheng & Li, Zhe & Zhu, Binxin & Qin, Tuanfa, 2026. "An advanced moving horizon framework for state of charge estimation in lithium-ion battery," Renewable Energy, Elsevier, vol. 256(PD).
    10. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    11. Zhao, Xuyang & He, Hongwen & Wei, Zhongbao & Huang, Ruchen & Yue, Hongwei & Guo, Xuncheng, 2025. "Cross-scale modeling-driven multi-state estimation framework for lithium-ion batteries with integrated distributed thermal sensing," Energy, Elsevier, vol. 335(C).
    12. Huang, Yan-feng & Wu, Tao & Fei, Yue & Chen, Xing-ni & Xu, Bin, 2025. "Exploration of electrode structure optimization based on a heterogeneous electrode model: Analysis of polarization effect under the regulation of particle morphology," Energy, Elsevier, vol. 322(C).
    13. Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).

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