A Parameter Extraction Method for the Li-Ion Batteries With Wide-Range Temperature Compensation

Abstract

An accurate battery model is essential for precise estimation of battery performance indicators, such as state of charge (SoC), state of health (SoH), etc. It helps in determining the optimum energy management and therefore, optimum utilization of the battery capacity. Some of the battery model parameters may change with the variation of environmental conditions, such as operating temperature, and therefore, accurate temperature compensated model estimation is necessary to minimize the error in battery model and SoC estimation. This article presents a novel parameters extraction method for the Thevenin equivalent circuit model of Li-ion batteries considering their resiliency on temperature effect. The proposed approach represents each of the RC parallel circuits of the Thevenin-based equivalent circuit battery model as a first-order linear time-invariant (LTI) system. The resistance and capacitance values for each of the RC circuits in the model have been identified as the parameters of a standard LTI system using the system identification theory and represented them as the temperature-compensated model parameters. The proposed model can be applied to various applications, such as SoC, SoH estimation, and battery energy management. In order to demonstrate the suitability of the proposed modeling approach, the model is augmented with the conventional extended Kalman filter to enable accurate SoC estimation under varying operating temperatures. The accuracy and effectiveness of the proposed modeling approach and its suitability to enhance SoC estimation under a wide operating temperature range ( $-$ 5 to 45 $^\circ$ C) have been validated through laboratory experiments in the LabVIEW platform.