Abstract
State of charge (SOC) is one of the most significant parameters in the battery management system (BMS). Accurate estimate of the SOC can prevent the battery overcharge and over-discharge, which can effectively increase the life of the Lithium-ion battery and improve the safety of electric vehicle. In this paper, an improved second-order equivalent model is established. The improved model distinguishes the direction of charge and discharge for the resistance and capacitance parameters. To improve the estimation accuracy of SOC, this paper proposes an improved Adaptive Extended Kalman Filter by introducing an iterative method into the AEKF algorithm. The improved algorithm mainly uses the measured voltage data to adjust the covariance matrix P multiple times in one calculation step to reduce the error in the linearization process. The dynamic stress test (DST) and urban dynamometer driving schedule (UDDS) are applied to verify the validity and accuracy of the improved algorithm. The experimental results show that the algorithm proposed in this paper has faster convergence and more accurate compared with AEKF algorithms.
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Jin, Y., Su, C. & Luo, S. Improved Algorithm Based on AEKF for State of Charge Estimation of Lithium-ion Battery. Int.J Automot. Technol. 23, 1003–1011 (2022). https://doi.org/10.1007/s12239-022-0087-x
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DOI: https://doi.org/10.1007/s12239-022-0087-x