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

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.

Automated summary

This article proposes a distributed spatial-temporal online correction algorithm for the coestimation of the state of charge (SOC) and state of temperature (SOT) of batteries, which is crucial for a battery management system in achieving a green industrial economy. The algorithm identifies the internal resistance and estimates SOC using an adaptive Kalman filter. It then couples SOC estimation with an online restoration algorithm for distributed temperature, using an improved fractal growth process. The proposed coestimation algorithm improves SOC estimation fidelity by up to 1.5% and maintains the mean relative error of SOT estimation within 8%.