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<p>This work presents a robust and straightforward approach for the joint estimation of the state of charge (SoC) and state of health (SoH) in lithium-ion batteries, aimed at enhancing the performance of battery management systems. A modified second-order RC equivalent circuit model is employed to capture the dynamic behavior of the battery, with specific modifications enabling simultaneous estimation of SoC and SoH. The model parameters are identified using the variable forgetting factor recursive least squares algorithm. Since SoH is closely linked to battery aging, the parameters are modeled as functions of the number of charge–discharge cycles. To address measurement noise in current and terminal voltage, and assuming them as non-Gaussian, a centered error entropy unscented Kalman filter is proposed, providing accurate estimates of SoC and SoH with the assumption that cycle information is known. The performance of the proposed method is evaluated using the root mean square error and its average value over a publicly available dataset. Finally, the method is validated through implementation on a hardware test platform.</p>

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A robust and novel approach to joint SoC and SoH estimation for Li-ion batteries with experimental validation

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Avanesh Kumar,
  • Supriyo Samanta,
  • Rahul Radhakrishnan,
  • Hiren Kumar G. Patel

摘要

This work presents a robust and straightforward approach for the joint estimation of the state of charge (SoC) and state of health (SoH) in lithium-ion batteries, aimed at enhancing the performance of battery management systems. A modified second-order RC equivalent circuit model is employed to capture the dynamic behavior of the battery, with specific modifications enabling simultaneous estimation of SoC and SoH. The model parameters are identified using the variable forgetting factor recursive least squares algorithm. Since SoH is closely linked to battery aging, the parameters are modeled as functions of the number of charge–discharge cycles. To address measurement noise in current and terminal voltage, and assuming them as non-Gaussian, a centered error entropy unscented Kalman filter is proposed, providing accurate estimates of SoC and SoH with the assumption that cycle information is known. The performance of the proposed method is evaluated using the root mean square error and its average value over a publicly available dataset. Finally, the method is validated through implementation on a hardware test platform.