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Improved harmonic loss – History gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations

Author

Listed:
  • Wang, Shunli
  • Wei, Jie
  • Zhang, Liya
  • Li, Huan
  • Fernandez, Carlos
  • Blaabjerg, Frede

Abstract

Accurate estimation of the state of charge (SOC) and state of energy (SOE) of a battery is critical for battery system management to improve the reliability and safety of battery operation. Since both SOC and SOE are cumulative quantities of time, historical factors greatly influence them. Therefore, this paper aims to propose a research method based on harmonic loss-history gated cell cycling (HL-HGUR) to obtain accurate estimation results. The algorithm is based on the gated recurrent unit (GRU) and takes into account the influence of historical factors, while the loss function is improved by combining the basic forgetting curve to improve the estimation accuracy of SOC and SOE. To further validate the estimation performance of the algorithm, this study uses the Beijing Bus Stress Test Environment (BBDST) and the Dynamic Stress Test Environment (DST) to confirm the efficacy of the method. Under these two conditions, the HL-HGUR algorithm achieves a maximum MAE of 1.29 % and a maximum RMSE of 1.49 % for SOC, and a maximum MAE of 1.09 % and a maximum RMSE of 1.25 % for SOE, which are all better than the traditional LSTM, GRU, and HGRU algorithms. Therefore, the HL-HGUR estimation method can realize the accurate estimation of SOC and SOE, and effectively improve the estimation efficiency of large-scale energy storage lithium-ion batteries.

Suggested Citation

  • Wang, Shunli & Wei, Jie & Zhang, Liya & Li, Huan & Fernandez, Carlos & Blaabjerg, Frede, 2025. "Improved harmonic loss – History gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations," Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225048674
    DOI: 10.1016/j.energy.2025.139225
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    References listed on IDEAS

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    1. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
    2. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    3. Aijun Tian & Weidong Xue & Chen Zhou & Yongquan Zhang & Haiying Dong, 2024. "Mechanism and Data-Driven Fusion SOC Estimation," Energies, MDPI, vol. 17(19), pages 1-16, October.
    4. Zhang, Ying & Gu, Pingwei & Duan, Bin & Zhang, Chenghui, 2024. "A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries," Energy, Elsevier, vol. 301(C).
    5. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).
    6. Chai, Xuqing & Li, Shihao & Liang, Fengwei, 2024. "A novel battery SOC estimation method based on random search optimized LSTM neural network," Energy, Elsevier, vol. 306(C).
    7. Ruan, Guanqiang & Liu, Zixi & Cheng, Jinrun & Hu, Xing & Chen, Song & Liu, Shiwen & Guo, Yong & Yang, Kuo, 2024. "A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects," Energy, Elsevier, vol. 304(C).
    8. Chen, Yuan & Duan, Wenxian & Huang, Xiaohe & Wang, Shunli, 2024. "Multi-output fusion SOC and SOE estimation algorithm based on deep network migration," Energy, Elsevier, vol. 308(C).
    9. Wang, Jianfeng & Zuo, Zhiwen & Wei, Yili & Jia, Yongkai & Chen, Bowei & Li, Yuhan & Yang, Na, 2024. "State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF," Applied Energy, Elsevier, vol. 368(C).
    10. T. N. V. Krishna & Seelam V. S. V. Prabhu Deva Kumar & Sunkara Srinivasa Rao & Liuchen Chang, 2024. "Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles," Energies, MDPI, vol. 17(14), pages 1-18, July.
    11. Rahil Parag Sheth & Narendra Singh Ranawat & Ayon Chakraborty & Rajesh Prasad Mishra & Manoj Khandelwal, 2023. "The Lithium-Ion Battery Recycling Process from a Circular Economy Perspective—A Review and Future Directions," Energies, MDPI, vol. 16(7), pages 1-16, April.
    12. Mou, Jianhui & Zhou, Wenqi & Yu, Chengcheng & Fu, Qiang & Wang, Bo & Wang, Yangwei & Li, Junjie, 2025. "A data-driven SOE estimation framework for lithium-ion batteries under drive cycle conditions over wide temperature range," Energy, Elsevier, vol. 318(C).
    13. Li, Hao & Fu, Lijun & Long, Xinlin & Liu, Lang & Zeng, Ziqing, 2024. "A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention," Energy, Elsevier, vol. 294(C).
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