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<p>Estimating the state-of-charge (SOC) of lithium-ion batteries faces three main challenges at present: ensuring accuracy, achieving smooth output, and maintaining low computational complexity. To tackle these issues, this study introduces a hybrid algorithm observer. This approach combines the proportional-integral (PI) principle with the Kalman filter, utilizing a state-of-charge dynamics model and a current dynamics model. The SOC dynamics model, described by a differential equation, is developed to improve estimation accuracy. Meanwhile, the current dynamics model supports the design of a PI observer, which offers a low-complexity solution for SOC estimation. To address the issue of white noise in measurement signals, a one-dimensional Kalman filter is applied. This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer. In addition, the system incorporates parameter observation to estimate key battery parameters. The hybrid observer was tested in a real vehicle to validate its effectiveness. Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.</p>

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An algorithm-hybrid observer combining proportional-integral with Kalman filter for state-of-charge estimation of lithium-ion battery

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Guangwei Yin,
  • Hua Wang,
  • Lin He,
  • Xiaofei Liu,
  • Guoqiang Wang,
  • Jichao Liu

摘要

Estimating the state-of-charge (SOC) of lithium-ion batteries faces three main challenges at present: ensuring accuracy, achieving smooth output, and maintaining low computational complexity. To tackle these issues, this study introduces a hybrid algorithm observer. This approach combines the proportional-integral (PI) principle with the Kalman filter, utilizing a state-of-charge dynamics model and a current dynamics model. The SOC dynamics model, described by a differential equation, is developed to improve estimation accuracy. Meanwhile, the current dynamics model supports the design of a PI observer, which offers a low-complexity solution for SOC estimation. To address the issue of white noise in measurement signals, a one-dimensional Kalman filter is applied. This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer. In addition, the system incorporates parameter observation to estimate key battery parameters. The hybrid observer was tested in a real vehicle to validate its effectiveness. Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.