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<p>Accurate state of charge (SOC) estimation helps to maintain the battery running in a reliable and efficient way. The moving horizon estimation (MHE) algorithm is an efficient way to estimate SOC in the battery system with physical constraints and noises, but its huge computational burden hinders its online application. To reduce the burden, this paper proposes an explicit MHE method to offload the computationally complex optimization solution in the MHE. First, the complex and constrained optimization problem is solved offline by multi-parametric quadratic programming (mp-QP). Then, the explicit solution of optimal estimation is derived by piecewise affine (PWA) function, which is suitable for online estimation. The proposed explicit MHE method significantly reduces the online computational resources and enables real-time optimal SOC estimation. Finally, experiments and simulations validate the effectiveness of the explicit MHE method in ensuring estimation accuracy and saving computational cost. Results show the proposed method can achieve a max absolute SOC estimation error of 1.78% and reduce the computational time to 0.85 ms.</p>

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Explicit Moving Horizon Estimator Based Online State of Charge Estimation of Lithium-Ion Battery

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Zhaoxia Peng,
  • Yifan Xiong,
  • Chenyang Pan,
  • Zhenwei Yang,
  • Bofan Wu,
  • Shichun Yang,
  • Guoguang Wen,
  • Dongpu Cao

摘要

Accurate state of charge (SOC) estimation helps to maintain the battery running in a reliable and efficient way. The moving horizon estimation (MHE) algorithm is an efficient way to estimate SOC in the battery system with physical constraints and noises, but its huge computational burden hinders its online application. To reduce the burden, this paper proposes an explicit MHE method to offload the computationally complex optimization solution in the MHE. First, the complex and constrained optimization problem is solved offline by multi-parametric quadratic programming (mp-QP). Then, the explicit solution of optimal estimation is derived by piecewise affine (PWA) function, which is suitable for online estimation. The proposed explicit MHE method significantly reduces the online computational resources and enables real-time optimal SOC estimation. Finally, experiments and simulations validate the effectiveness of the explicit MHE method in ensuring estimation accuracy and saving computational cost. Results show the proposed method can achieve a max absolute SOC estimation error of 1.78% and reduce the computational time to 0.85 ms.