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<p>This paper proposes an Improved Sparrow Search Algorithm (ISSA) for accurate parameter identification of lithium-ion batteries. ISSA enhances the original algorithm through Tent chaotic initialization, a Sine-Cosine-based discoverer update, and a Lévy flight strategy for joiners, improving global search and local escape capabilities. Validation using a second-order RC model under HPPC and DST conditions at 0&#xa0;°C, 25&#xa0;°C, and 45&#xa0;°C shows ISSA outperforms GA, PSO, and SSA in convergence accuracy and robustness. The method is successfully extended to battery pack simulation with manufacturing variations, demonstrating practical scalability. An ablation study confirms the contribution of each improvement component. ISSA provides a precise and reliable solution for battery modeling, with strong potential for advanced battery management systems.</p>

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Parameter Identification of Lithium-ion Batteries Based on Improved Sparrow Search Algorithm

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Zhongxing Li,
  • Qiao Zhang

摘要

This paper proposes an Improved Sparrow Search Algorithm (ISSA) for accurate parameter identification of lithium-ion batteries. ISSA enhances the original algorithm through Tent chaotic initialization, a Sine-Cosine-based discoverer update, and a Lévy flight strategy for joiners, improving global search and local escape capabilities. Validation using a second-order RC model under HPPC and DST conditions at 0 °C, 25 °C, and 45 °C shows ISSA outperforms GA, PSO, and SSA in convergence accuracy and robustness. The method is successfully extended to battery pack simulation with manufacturing variations, demonstrating practical scalability. An ablation study confirms the contribution of each improvement component. ISSA provides a precise and reliable solution for battery modeling, with strong potential for advanced battery management systems.