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Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance

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  • Khosravi, Nima
  • Oubelaid, Adel

Abstract

Accurate parameter identification of lithium-ion batteries (LIBs) is critical for the performance and safety of electric vehicles (EVs). This study introduces a hybrid approach to efficiently identify and forecast LIB parameters under dynamic operating conditions. The hybrid method combines the hierarchical deep learning neural network (HDLNN) with the walrus optimization (WO) technique. While HDLNN predicts the battery (BAT) components and greatly reduces the error between estimated and measured voltage, the WO method is used to determine optimal settings. The suggested method improves BAT modeling accuracy and ensures optimal performance under different load scenarios. Three complementary algorithms, the genetic algorithm (GA), the black hole algorithm (BHA), and harmony search (HS) have been used for comparison in order to confirm the efficacy of the WO approach. Results indicate the superiority of the WO method, achieving a faster computation time of 0.17 s and reducing voltage error by 4.65 mV, outperforming the alternative algorithms. The results demonstrate how the WO-HDLNN hybrid technique can reliably and steadily detect BAT characteristics in LIB applications for both EVs and hybrid EVs.

Suggested Citation

  • Khosravi, Nima & Oubelaid, Adel, 2025. "Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007893
    DOI: 10.1016/j.energy.2025.135147
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    1. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2025. "Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization," Applied Energy, Elsevier, vol. 379(C).
    2. Xia, Bing & Zhao, Xin & de Callafon, Raymond & Garnier, Hugues & Nguyen, Truong & Mi, Chris, 2016. "Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods," Applied Energy, Elsevier, vol. 179(C), pages 426-436.
    3. Li, Yuanmao & Liu, Guixiong & Deng, Wei & Li, Zuyu, 2024. "Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods," Applied Energy, Elsevier, vol. 367(C).
    4. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    5. Khosravi, Nima & Dowlatabadi, Masrour & Sabzevari, Kiomars, 2025. "A hierarchical deep learning approach to optimizing voltage and frequency control in networked microgrid systems," Applied Energy, Elsevier, vol. 377(PA).
    6. Khosravi, Nima & Baghbanzadeh, Rasoul & Oubelaid, Adel & Tostado-Véliz, Marcos & Bajaj, Mohit & Hekss, Zineb & Echalih, Salwa & Belkhier, Youcef & Houran, Mohamad Abou & Aboras, Kareem M., 2023. "A novel control approach to improve the stability of hybrid AC/DC microgrids," Applied Energy, Elsevier, vol. 344(C).
    7. 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).
    8. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    9. Chen, Xiaokai & Lei, Hao & Xiong, Rui & Shen, Weixiang & Yang, Ruixin, 2019. "A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    10. Hu, Minghui & Li, Yunxiao & Li, Shuxian & Fu, Chunyun & Qin, Datong & Li, Zonghua, 2018. "Lithium-ion battery modeling and parameter identification based on fractional theory," Energy, Elsevier, vol. 165(PB), pages 153-163.
    11. 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).
    12. Zheng, Kun & Li, Yijing & Yang, Zhipeng & Zhou, Feifan & Yang, Kun & Song, Zhengxiang & Meng, Jinhao, 2025. "Adversarial training defense strategy for lithium-ion batteries state of health estimation with deep learning," Energy, Elsevier, vol. 317(C).
    13. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
    14. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    15. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    16. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    17. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    18. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    19. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    20. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    21. Kim, Minho & Chun, Huiyong & Kim, Jungsoo & Kim, Kwangrae & Yu, Jungwook & Kim, Taegyun & Han, Soohee, 2019. "Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search," Applied Energy, Elsevier, vol. 254(C).
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