Coestimation of SOC and Three-Dimensional SOT for Lithium-Ion Batteries Based on Distributed Spatial-Temporal Online Correction

• Published in: IEEE transactions on industrial electronics (1982) Vol. 70; no. 6; pp. 5937 - 5948
• Main Authors: Xie, Yi, Li, Wei, Hu, Xiaosong, Tran, Manh-Kien, Panchal, Satyam, Fowler, Michael, Zhang, Yangjun, Liu, Kailong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Batteries; Battery management system; battery modeling; Convergence; Electric charge; Energy storage; Estimation; industrial energy storage system; Kalman filters; Lithium-ion batteries; lithium-ion battery; Rechargeable batteries; Resistance; Restoration; state estimation; State of charge; Temperature distribution; Thermocouples; Thermodynamic properties; Voltage
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2022.3199905

Energy storage system based on batteries is a key to achieve a green industrial economy and the online estimation of its status is critical for the battery management system. Therefore, this article proposed a distributed spatial-temporal online correction algorithm for the state of charge (SOC) three-dimensional (3-D) state of temperature (SOT) coestimation of battery. First, the internal resistance is identified, and SOC is estimated based on the adaptive Kalman filter. Then, to improve the fidelity of electrical status estimation under the dynamic operation condition, the SOC estimation is coupled with an online restoration algorithm of distributed temperature. An improved fractal growth process is used to achieve the self-organization and convergence during the restoration of 3-D temperature distribution. Finally, to validate the fidelity of online coestimation algorithm for electrical and thermal parameters, dynamic current profiles are used. The coestimation method raises the fidelity of SOC estimation by 1.5% at most, compared with the SOC estimation algorithm without the SOT estimation. It also keeps the mean relative error of SOT estimation within 8%. Additionally, the robustness of the spatial-temporal online correction method with dual adaptive Kalman filters is validated. The result shows that the coestimation algorithm still has a good convergence performance with disturbance added.