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Lithium-ion batteries are increasingly demonstrating their superior advantages and are considered a potential replacement for traditional energy sources in automobile engines. However, numerous factors affect the lifespan of lithium-ion batteries, such as temperature, rated voltage, capacity, and charge/discharge cycles. Traditional estimation methods are primarily used to estimate the battery state, such as remaining capacity, state of charge, or level of degradation. However, traditional estimation methods often lead to errors when dealing with complex models and fail to address noise states effectively. In this paper, the authors propose an estimation method for lithium-ion batteries using the Extended Kalman Filter, which overcomes the drawbacks of traditional estimation methods.

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Estimation of the State of Lithium-Ion Battery for Electric Vehicles Using Extended Kalman Filter

  • 会议论文

  • 发表日期：

    2026年

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  • Pham Quoc Thai,
  • Tran Thuan Hoang,
  • Duong Van Hoa,
  • Nong Trong Tu,
  • Du Van Ngan,
  • Ngo Tan Thong,
  • Phan Van Binh,
  • Huynh Duc Tri,
  • Le Lit

摘要

Lithium-ion batteries are increasingly demonstrating their superior advantages and are considered a potential replacement for traditional energy sources in automobile engines. However, numerous factors affect the lifespan of lithium-ion batteries, such as temperature, rated voltage, capacity, and charge/discharge cycles. Traditional estimation methods are primarily used to estimate the battery state, such as remaining capacity, state of charge, or level of degradation. However, traditional estimation methods often lead to errors when dealing with complex models and fail to address noise states effectively. In this paper, the authors propose an estimation method for lithium-ion batteries using the Extended Kalman Filter, which overcomes the drawbacks of traditional estimation methods.