Accurate estimation of state of charge is essential to ensure reliable and efficient management of lithium-ion batteries in electric vehicles. The working procedure of lithium-ion batteries is very sophisticated. Thus, developing novel methods for SOC estimation in nonlinear non-Gaussian battery system is inevitable. In this article, a novel adaptive cubature H-infinity filter (ACHF) is proposed. It combines the favorable characteristics of H-infinity (HF) and cubature Kalman filter (CKF). In the iterative process of CKF, the singular value decomposition is used to guarantee non-negative definiteness of the error covariance matrix. The statistical properties of process and measurement noise are timely modified by the Sage–Husa estimator. The performance of the developed method is evaluated by the urban dynamometer driving schedule test. Through the comparison with traditional CKF, the experimental results show that the proposed ACHF method can achieve precise SOC. Moreover, the robustness verification results illustrate that the proposed algorithm is robust to the SOC initial errors.

准确估计充电状态对于确保对电动汽车中的锂离子电池进行可靠有效的管理至关重要。锂离子电池的工作程序非常复杂。因此，在非线性非高斯电池系统中开发用于 SOC 估计的新方法是不可避免的。在本文中，提出了一种新颖的自适应体积 H 无穷大滤波器 (ACHF)。它结合了 H 无穷大 (HF) 和体积卡尔曼滤波器 (CKF) 的有利特性。在CKF的迭代过程中，利用奇异值分解来保证误差协方差矩阵的非负确定性。Sage-Husa 估计器会及时修改过程和测量噪声的统计特性。所开发方法的性能通过城市测功机驾驶计划测试进行评估。通过与传统CKF的对比，实验结果表明提出的ACHF方法可以实现精确的SOC。此外，鲁棒性验证结果表明所提出的算法对SOC初始误差具有鲁棒性。