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Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

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  • Lin, Mingqiang
  • Yan, Chenhao
  • Meng, Jinhao
  • Wang, Wei
  • Wu, Ji

Abstract

Accurate state of health (SOH) estimation is a key issue for lithium-ion batteries management and control. In this paper, a novel SOH estimation method is proposed based on the fusion of the simulated annealing algorithm and support vector regression (SVR). Firstly, considering the electrochemical and thermodynamic characteristics of the battery aging process, we extract the health factors by analyzing and sampling the differential thermal capacity (DTC) curves which are based on temperature, voltage, and current. Then, an SVR model is constructed to estimate the SOH. The mean-variance obtained from cross-validation is used as the evaluation function, and hyperparameters of the SVR are optimized using the simulated annealing algorithm. Finally, we conduct two sets of experiments on the Oxford dataset for verification. Experimental results not only show the outperformance of the DTC curves for describing the battery aging but also illustrate that our proposed prediction model exhibits higher accuracy and less error of SOH estimation under the premise of ensuring real-time performance than the other two comparative models.

Suggested Citation

  • Lin, Mingqiang & Yan, Chenhao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2022. "Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007320
    DOI: 10.1016/j.energy.2022.123829
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    References listed on IDEAS

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    1. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
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    Cited by:

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    4. 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).
    5. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
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    10. Wen, Jie & Jia, Chenyu & Xia, Guangshu, 2025. "State of health prediction of lithium-ion batteries for driving conditions based on full parameter domain sparrow search algorithm and dual-module bidirectional gated recurrent unit," Energy, Elsevier, vol. 335(C).
    11. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    12. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    13. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    14. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
    15. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Sonthalia, Ankit & Femilda Josephin, J.S. & Varuvel, Edwin Geo & Chinnathambi, Arunachalam & Subramanian, Thiyagarajan & Kiani, Farzad, 2025. "A deep learning multi-feature based fusion model for predicting the state of health of lithium-ion batteries," Energy, Elsevier, vol. 317(C).
    17. Deyang Yin & Xiao Zhu & Wanjie Zhang & Jianfeng Zheng, 2024. "Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression," Energies, MDPI, vol. 17(22), pages 1-14, November.
    18. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    19. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Li, Jian & Zhang, Hongguang & Yang, Fubin & Zhang, Jian & Li, Qi, 2025. "State of health estimation of lithium-ion batteries based on feature optimization and data-driven models," Energy, Elsevier, vol. 316(C).
    20. Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
    21. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
    22. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).
    23. Zhang, Wencan & Li, Xingyao & Liu, Guote & Ouyang, Nan & Yuan, Jiangfeng & Xie, Yi & Wu, Weixiong, 2024. "Optimization design of a hybrid thermal runaway propagation mitigation system for power battery module using high-dimensional surrogate models," Renewable Energy, Elsevier, vol. 225(C).
    24. Ting Zhu & Wenbo Wang & Yu Cao & Xia Liu & Zhongyuan Lai & Hui Lan, 2025. "An Innovative Framework for Forecasting the State of Health of Lithium-Ion Batteries Based on an Improved Signal Decomposition Method," Sustainability, MDPI, vol. 17(11), pages 1-25, May.

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