FPGA Implementation of Battery State-of-Charge Estimation Using Extended Kalman Filter and Dynamic Sampling

The rapid increase in the adoption of electric vehicles (EVs) has highlighted issues related to the safety and efficiency of lithium-ion batteries. This study implemented a hardware module to effectively estimate the state of charge (SOC), which is a core element of the battery management system (BMS), using an extended Kalman filter (EKF)-based approach. A method to reduce the power consumption during hardware design through adjustments to the sampling period according to the SOC range was proposed. The root mean square error was obtained as below 0.75, with only 2455 samples out of the 700,000 measurements, achieving a reduction of 99.65%. Following the evaluation of the accuracy of the software model, the results were compared through hardware implementation. Consequently, the performance was verified via synthesis using a DE2-115 FPGA board from Terasic in Taiwan.