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<p>The state estimation of a battery management system (BMS) is a critical part. The most important part is to precisely estimate the state of charge (SOC) and state of health (SOH). The study object is first chosen to be a resistance capacitance (RC) equivalent circuit model (ECM) of second order. Next, the chosen battery model’s offline variables are identified, and the identification technique is confirmed. Aiming at the problem of high-precision joint estimation of SOC and SOH for power batteries, the UKF + EKF joint estimation algorithm was established. To increase the SOC’s estimate accuracy even more, the UKF + EKF method served as the foundation for the multi-innovation adaptive uninformed Kalman filter (MIAUKF) algorithm. The MIAUKF + EKF algorithm’s joint SOC and SOH estimate is achieved. The experimental findings demonstrate that the MIAUKF + EKF has a greater reliability than the UKF + EKF method, and it also has a better estimation effect on SOH. To further validate the performance of the MIAUKF + EKF joint estimation approach in real environment, the Typhoon HIL602+ hardware-in-loop equipment is used to design a bench test platform for batteries. The findings indicate that even under the condition of colored noise in voltage and current, and the suggested algorithm’s SOC and SOH estimate accuracy, is still rather excellent.</p>

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Research on the joint estimation method of charge state and health state of power lithium battery

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Zhifu Wang,
  • Shunshun Zhang,
  • Wei Luo,
  • Zhongyi Yang,
  • Yifang Gao

摘要

The state estimation of a battery management system (BMS) is a critical part. The most important part is to precisely estimate the state of charge (SOC) and state of health (SOH). The study object is first chosen to be a resistance capacitance (RC) equivalent circuit model (ECM) of second order. Next, the chosen battery model’s offline variables are identified, and the identification technique is confirmed. Aiming at the problem of high-precision joint estimation of SOC and SOH for power batteries, the UKF + EKF joint estimation algorithm was established. To increase the SOC’s estimate accuracy even more, the UKF + EKF method served as the foundation for the multi-innovation adaptive uninformed Kalman filter (MIAUKF) algorithm. The MIAUKF + EKF algorithm’s joint SOC and SOH estimate is achieved. The experimental findings demonstrate that the MIAUKF + EKF has a greater reliability than the UKF + EKF method, and it also has a better estimation effect on SOH. To further validate the performance of the MIAUKF + EKF joint estimation approach in real environment, the Typhoon HIL602+ hardware-in-loop equipment is used to design a bench test platform for batteries. The findings indicate that even under the condition of colored noise in voltage and current, and the suggested algorithm’s SOC and SOH estimate accuracy, is still rather excellent.