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This study investigates the performance of a single-cell cylindrical Lithium Cobalt Oxide (LCO) battery, focusing on multi-objective genetic algorithm (GA) optimization to enhance its operational characteristics for electric vehicle (EV) applications. Analyzed under a 1C rate over a duration of 0–2100s, the battery features a Lithium Cobalt Oxide positive electrode, graphite negative electrode, and a lithium hexafluorophosphate separator, yielding an unoptimized mass of 0.0487 kg and a specific energy of 177.8 Wh/kg. The optimization process targeted critical parameters, including electrode (anode and cathode) thicknesses and porosities, leading to a reduction in battery density from 2000 to 1900 kg/m3 and an increase in specific energy to 192 Wh/kg. Additionally, the optimized configuration, improved thermal management, lowering operational temperature from 68 to 34.5 °C. These results underscore the efficacy of multi-objective GA optimization in enhancing battery performance by balancing energy density and thermal stability, making it well-suited for electric vehicle applications.

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Enhancing Lithium-Ion Battery Performance for Electric Vehicles: Optimization of Electrochemical Parameters Using Genetic Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • P. Mangaiyarkarasi,
  • J. Sneha,
  • R. Jayaganthan

摘要

This study investigates the performance of a single-cell cylindrical Lithium Cobalt Oxide (LCO) battery, focusing on multi-objective genetic algorithm (GA) optimization to enhance its operational characteristics for electric vehicle (EV) applications. Analyzed under a 1C rate over a duration of 0–2100s, the battery features a Lithium Cobalt Oxide positive electrode, graphite negative electrode, and a lithium hexafluorophosphate separator, yielding an unoptimized mass of 0.0487 kg and a specific energy of 177.8 Wh/kg. The optimization process targeted critical parameters, including electrode (anode and cathode) thicknesses and porosities, leading to a reduction in battery density from 2000 to 1900 kg/m3 and an increase in specific energy to 192 Wh/kg. Additionally, the optimized configuration, improved thermal management, lowering operational temperature from 68 to 34.5 °C. These results underscore the efficacy of multi-objective GA optimization in enhancing battery performance by balancing energy density and thermal stability, making it well-suited for electric vehicle applications.