ABSTRACT Electric vehicles can significantly improve their energy efficiency through regenerative braking, which recovers kinetic energy during braking for storage in batteries. Supercapacitors are particularly suitable for this purpose due to their high power density and long cycle life. However, to prevent thermal stress and degradation during rapid energy exchange in regenerative braking, the charging current must be carefully managed. The proposed method controls the highest charging current using the supercapacitor temperature as an input parameter. The Levy Enhanced Red Panda Optimization (LE‐RPO) algorithm is employed to optimally tune the FOPID controller parameters and adjust the supercapacitor reference current. In order to improve the utilization of energy, this commit aims to analyze the supercapacitor's maximal charge capacity. The results were compared between a method with fixed peak current and ESR and one that was modified by temperature, peak current, and ESR. Modeling by varying the initial ambient temperatures was also examined. Lastly, the effect of the methods on the life span of the supercapacitor module is studied. According, the result shows that the proposed LE‐RPO method outperforms the conventional models like GWO, SOA and RPO with an improved efficiency of 79.233%. It highlights the superior stability and control system performance of the proposed LE‐RPO method. Likewise, the proposed LE‐RPO algorithm achieves a settling time of 7.714 s, which demonstrates faster stabilization compared to other methods such as Fuzzy, PSO, RTO, LSTM‐PSO, GA‐PSO, and IGWO‐CSO. Moreover, the stability performance of the proposed LE‐RPO‐based FOPID controller was evaluated under four conditions, such as no fault and SLG (single line to ground) faults on Lines 1–1, 1–3, and 1–5. The results show the settling time remains almost unchanged (7.7403–7.7543 s), indicating the system quickly reaches steady state with minimal disturbance.
Sekhar et al. (Sun,) studied this question.