Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries

• Published in: IEEE transactions on power electronics 2025-02, Vol.40 (2), p.3033-3042
• Main Authors: Yi, Baozhao, Du, Xinhao, Zhang, Jiawei, Wu, Xiaogang, Hu, Qiuhao, Jiang, Weiran, Hu, Xiaosong, Song, Ziyou
• Format: Article
• Language: English
• Subjects: Accuracy; Batteries; Battery charge measurement; Bias-compensated algorithm; Electronic mail; Estimation; Kalman filters; lithium iron phosphate batteries; Machine learning algorithms; State estimation; State of charge; Voltage measurement; voltage measurement bias
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2024.3492714

Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for safe and reliable operation of batteries. Voltage measurement bias strongly affects state estimation accuracy, especially in Lithium Iron Phosphate (LFP) batteries, owing to the flat open-circuit voltage (OCV) curves. This work introduces a bias-compensated algorithm to reliably estimate SOC and SOH of LFP batteries under the influence of voltage measurement biases. Specifically, SOC and SOH are estimated using the Dual Extended Kalman Filter in the SOC range with the high slope of the OCV-SOC curve, where the effects of voltage bias are weak. Besides, the voltage biases estimated in the low-slope SOC regions are compensated in the following joint estimation of SOC and SOH to enhance the state estimation accuracy. Experimental results indicate that the proposed algorithm significantly outperforms the traditional method, which does not consider voltage biases under different temperatures and aging conditions. In addition, the bias-compensated algorithm can achieve low estimation errors of less than 1.5% for SOC and 2% for SOH, even with a 30 mV voltage bias. Finally, even if the voltage measurement bias changes during operation, the proposed algorithm remains robust and maintains the estimated errors of states at 2%.