Kalman Filter Based Vehicle SOC Performance Analysis for Different Battery Types

The Battery Management System (BMS) is critical to the battery’s efficient and to deliver a safe performance. The monitoring of battery parameters is one of the tasks performed by the BMS. In BMS, the battery state of charge is a critical parameter which indicates the amount of charge it contains. In this context, battery SOC estimate is playing and important roles in the battery management system as the SOC has a direct impact on BMS decision-making and control. For this reason, Kalman filter correction approach is used in this design, to analyze the impact of charge and discharge rates, temperature, and charge or discharge cycles on SOC estimates. The Kalman filter correction technique is presented based on those technology, with its application in the pure electric car battery management system. The findings has reveals that the Kalman filter correction algorithm efficiently corrects the Ah method error, increases estimation accuracy, and offers a more accurate SOC estimation technique for battery management systems.