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In response to the inaccuracy of traditional state of health (SOH) assessment methods for lead-acid batteries, the attenuation behavior of lead-acid batteries at different rates is investigated and a SOH estimation method is proposed using the standard deviation of charging time as input for a random forest model. The results indicate that lead-acid batteries exhibit a relatively linear decay during the aging processes at 100% charge discharge depth cycles at rates of 0.05 C and 0.1 C, respectively. The time standard deviation σV-T during the constant current charging stage and the time standard deviation σI-T during the constant voltage charging stage respectively show good linear positive correlations with the battery SOH, with a goodness of fit R2 above 0.92. A random forest model based on multiple decision trees is emplored to evaluate the battery SOH value using σV-T and σI-T as inputs and SOH value as output. The RMSE value of the SOH evaluation model is less than 2.2% while the MAE value is less than 1.5%, indicating that the model has high evaluation accuracy. The results of this study have significant reference value for the online SOH evaluation of lead-acid batteries.

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SOH Estimation of Lead-Acid Batteries Based on Standard Deviation of Charging Time as Health Factors

  • 会议论文

  • 发表日期：

    2025年

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  • Yongxiang Cai,
  • Wei Liu,
  • Yang Wang,
  • Xiankui Wen,
  • Xin Chen,
  • Qi Zhang,
  • Qiangqiang Liao

摘要

In response to the inaccuracy of traditional state of health (SOH) assessment methods for lead-acid batteries, the attenuation behavior of lead-acid batteries at different rates is investigated and a SOH estimation method is proposed using the standard deviation of charging time as input for a random forest model. The results indicate that lead-acid batteries exhibit a relatively linear decay during the aging processes at 100% charge discharge depth cycles at rates of 0.05 C and 0.1 C, respectively. The time standard deviation σV-T during the constant current charging stage and the time standard deviation σI-T during the constant voltage charging stage respectively show good linear positive correlations with the battery SOH, with a goodness of fit R2 above 0.92. A random forest model based on multiple decision trees is emplored to evaluate the battery SOH value using σV-T and σI-T as inputs and SOH value as output. The RMSE value of the SOH evaluation model is less than 2.2% while the MAE value is less than 1.5%, indicating that the model has high evaluation accuracy. The results of this study have significant reference value for the online SOH evaluation of lead-acid batteries.