Enhancing Low-Pass Filter Energy Management with Adaptive State of Charge Limiter for Hybrid Energy Storage in Electric Vehicles

Abstract

Electric vehicles (EVs) have become a vital solution for environmental transportation; however, challenges related to battery life and power density persist. In pursuit of enhanced EV performance and cost-effectiveness, researchers advocate for Hybrid Energy Storage Systems (HESS), integrating various Energy Storage Systems (ESS). An efficient Energy Management Strategy (EMS) is crucial for optimal power distribution within the HESS. This study introduces a real-time, simple, and practical EMS using a low-pass filter (LPF). However, the LPF lacks State of Charge (SoC) control, necessitating the addition of a SoC Limiter. The static SoC Limiter, while effective, faces challenges in predicting peak loads, leading to suboptimal power-sharing performance. To address this limitation, LPF with Adaptive SoC Limiter (LPF-ASL) is proposed. The LPF-ASL accommodates the peak load by saving some portion of supercapacitor (SC) power for peak load. In an unpredictable initial SC SoC test, LPF-ASL achieves substantial reductions in maximum battery current compared to LPF and Fuzzy Logic Control (FLC) by up to 21.30% and 21.14%, respectively. This underscores the effectiveness of LPF-ASL in optimizing battery life and enhancing power distribution within HESS-equipped EVs.