Battery SOC estimation based on BASOA-IEKF fusion algorithm
1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013， China； 2. School of Water Energy and Environment, Cranfield University, Cranfield, MK43 0AL, UK
Abstract：Standard extended Kalman filter (EKF) is currently and commonly used algorithm for estimating the state of charge (SOC) of batteries. However, the accuracy of battery SOC estimation is compromised due to the linearization errors and the dependence on noise matrices in the system. To address the issue, a fusion filtering algorithm based on boundary adaptive seeker optimization algorithm and iterated extended Kalman filter（BASOA-IEKF） was proposed to improve the SOC estimation accuracy by iteratively updating the state estimates and intelligently optimizing the system noise matrix. The static and dynamic simulation results show that under static conditions, the maximum estimation error of SOC is 3.74%. Under hybrid pulse power characterization（HPPC） conditions, the estimation error is less than 3.00%, while under urban dynamometer driving schedule（UDDS） conditions, it is less than 2.50%. Compared to the single IEKF algorithm, the BASOA-IEKF algorithm achieves higher accuracy in SOC estimation with smaller fluctuations in the SOC error curve after convergence and demonstrates better stability and global robustness.
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