Batteries 2023, 9(8), 392;, Manuscript ID: batteries-2465060; 26 July 2023, DOI: 10.3390/batteries9080392

State-of-Charge Estimation of Lithium-Ion Batteries Based on Dual-Coefficient Tracking Improved Square-Root Unscented Kalman Filter

Simin Peng1, Ao Zhang1, Dandan Liu1, Mengzeng Cheng2, Jiarong Kan1, and Michael G. Pecht3,*
1 School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
2 Economic Research Institute of State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110015, China
3 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20740, USA
* Author to whom correspondence should be addressed.

For more information about this article and related research, please contact Michael G. Pecht.


Accurate state of charge (SOC) estimation is helpful for battery management systems to extend batteries’ lifespan and ensure the safety of batteries. However, due to the pseudo-positive definiteness of the covariance matrix and noise statistics error accumulation, the SOC estimation of lithium-ion batteries is usually inaccurate or even divergent using Kalman filters, such as the unscented Kalman filter (UKF) and the square-root unscented Kalman filter (SRUKF). To resolve this problem, an SOC estimation method based on the dual-coefficient tracking improved square-root unscented Kalman filter for lithium-ion batteries is developed. The method is composed of an improved square-root unscented Kalman filter (ISRUKF) and a dual-coefficient tracker. To avoid the divergence of SOC estimation due to the covariance matrix with pseudo-positive definiteness, an ISRUKF based on the QR decomposition covariance square-root matrix is presented. Moreover, the dual-coefficient tracker is designed to track and correct the state noise error of the battery, which can reduce the SOC estimation error caused by the accumulation of the battery model error using the ISRUKF. The accuracy and robustness of the SOC estimation method using the developed method are validated by the comparison with the UKF and SRUKF. The developed algorithm shows the highest SOC estimation accuracy with the SOC error within 1.5%.

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