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A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries

Author

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  • Ge, Dongdong
  • Jin, Guiyang
  • Wang, Jianqiang
  • Zhang, Zhendong

Abstract

Lithium-ion batteries (Libs) have become the best power source for electric vehicles due to their high energy density and long cycle life. The state estimation is directly related to the safe and efficient use of Libs. In our previous research, a Bat Algorithm-Extreme Learning Machine (BA-ELM) model was constructed and promising results were achieved for state of health (SOH) estimation. To increase the engineering application value of BA-ELM for SOH/state of charge (SOC) estimation, optimization mechanism of improving the global exploration and local exploitation capabilities of the model was studied. First, three improved methods including a conversion mechanism of Artificial Bee Colony algorithm employing bees and scout bees, a mechanism for adaptive adjustment of the search frequency of bats according to the Euclidean distance difference, and an adaptive inertia weight coefficient method were proposed to construct a novel Improved Bat Algorithm (IBA)-ELM model. Second, a battery life degradation test platform was built to obtain the degradation dataset for Libs. The variation rules of nine battery variables and cycles were revealed. Four high-quality SOH-related features were extracted. Thus, the SOH estimation performance of six models was evaluated. Third, a Libs charge–discharge dataset under driving conditions was collected and processed. Four models were trained based on 72,048 samples from four conditions. Further, SOC estimation tests were conducted on 35,071 samples from completely different conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of SOH estimation by IBA-ELM decreased by 21.93 % and 23.79 %, respectively, compared to those by BA-ELM. The RMSE and MAE of SOC estimation decreased by more than 10 %. Error, radar, and violin plots demonstrate that the IBA-ELM could accurately estimate SOH/SOC in larger scale datasets, simultaneously exhibiting better generalization performance.

Suggested Citation

  • Ge, Dongdong & Jin, Guiyang & Wang, Jianqiang & Zhang, Zhendong, 2024. "A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224021698
    DOI: 10.1016/j.energy.2024.132395
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    References listed on IDEAS

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    5. Xia, Baozhou & Ye, Min & Wei, Meng & Wang, Qiao & Lian, Gaoqi & Li, Yan, 2025. "SOH estimation of lithium-ion batteries with local health indicators in multi-stage fast charging protocols," Energy, Elsevier, vol. 334(C).
    6. He, Rui & Peng, Tian & Zhang, Xinyu & Chen, Zhigang & Yao, Junhao & Nazir, Muhammad Shahzad & Zhang, Chu, 2026. "A novel hybrid model for state of health prediction in lithium batteries based on non-stationary transformers optimized by tree-structured Parzen estimator considering health factors," Applied Energy, Elsevier, vol. 402(PC).
    7. Tian, Aina & He, Luyao & Ding, Tao & Dong, Kailang & Wang, Yuqin & Jiang, Jiuchun, 2025. "A generic physics-informed neural network framework for lithium-ion batteries state of health estimation," Energy, Elsevier, vol. 332(C).
    8. Wang, Yaxuan & Guo, Shilong & Cui, Yue & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    9. Gao, Kai & Li, Qi & Hu, Lin & Huang, Jing & Li, Heng & Wu, Yue, 2026. "Physical informed neural network for SOH estimation of lithium-ion battery with electrochemical mechanism," Energy, Elsevier, vol. 342(C).

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