Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems With Multi-Objective Optimization Criterion

Electric vehicles (EVs) experience rapid battery degradation due to high peak power during acceleration and deceleration, followed by subsequent charging and discharging cycles during urban drive. To meet the high-power demands and mitigate degradation, EVs are equipped with larger-sized battery energy storage systems (ESS) results in increasing their cost and reducing their overall efficiency. Battery and supercapacitor (SC) powered hybrid ESS (HESS), offers an appealing solution to overcome the limitations of standalone battery ESS (BESS). Real-time power sharing among the sources in HESS to achieve satisfactory mileage and battery cycle life is a significant challenge when optimizing power management and dimensioning of HESS. However, to overcome these problems, an integrated optimization approach is proposed using the non-dominated sorting genetic algorithm III (NSGA-III) and fuzzy logic-based control (FLC) strategy. In the process of deriving the optimal configuration for HESS, the battery capacity is identified based on the required minimum range. Moreover, the optimal arrangement of the SC module is derived by minimizing battery capacity loss, HESS mass, and overall financial cost over vehicle lifetime. In comparison to a high-power (HP) standalone BESS, the optimized HESS governed by the proposed EM technique can prolong the battery's cycle life by 72.8% and 76.38%, as well as remarkable reductions in ESS life cycle cost-to-range ratio of up to 37.5% and 42.14% when following the standard US06 and Urban Dynamometer Driving Schedule (UDDS) routes, respectively. Involvement of SC resulted in a substantial 34.3% reduction in the mass of the HESS when compared to the HP standalone BESS. This study further demonstrates that an appropriately tuned fuzzy-logic EM method, which can be seamlessly integrated into a vehicle in realtime, exhibits superior performance in comparison to the basic rule-based approach