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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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