Novel temperature-effective modeling and state of charge estimation based on sigma-point Kalman filter for lithium titanate oxide battery

Battery modeling and state of charge (SoC) estimation are critical functions in the effective battery management system (BMS) operation. Temperature directly affects the performance and changes the model accuracy of a battery. Most studies have focused on estimating the internal temperature of the battery from the surface temperature of the battery with the help of sensors. However, due to the high number of cells in battery packs, the increase in sensor costs and the number of parameters have been ignored. Therefore, this article presents a new framework for the temperature effect using the electrical circuit model. The terminal voltage of the battery includes the effect under different operating conditions. This effect was associated with internal resistance in the battery model. The developed temperature-effective battery model was tested at different temperatures and operating currents. The model was validated with a maximum average root mean square error of 0.05% from the test results. The SoC of the LTO battery was estimated with the sigma-point Kalman (SPK) filter incorporating the developed model. The maximum average root mean square error in the estimation results is 0.11%. It is suitable for practical applications due to its low cost, simplicity, and reliability.