Comparative Analysis of Multi-Objective and Multi-Verse Optimization for Energy Management in Electric Vehicles

Hybrid Energy Storage Systems (HESS), combining batteries, super capacitors, and flywheel energy storage systems (FESS), enhance electric vehicle (EV) performance by improving energy efficiency and extending battery lifespan. This study presents a comparative analysis of Pareto-based Multi-Objective Optimization (PMO) and Multi-Verse Optimization (MVO) for energy management across high-speed, city, and mixed driving conditions. Modelling and simulations were carried out using MATLAB/Simulink, with optimization algorithms implemented through custom MATLAB scripts. MVO demonstrates superior performance in both energy allocation and battery stress reduction. For high-speed driving, it achieves an average energy consumption of 54.19 kWh/100 km, while city and mixed cycles record 15.36 kWh/100 km and 9.88 kWh/100 km, respectively. Compared to PMO, MVO reduces battery energy usage by 15.3% and energy losses by 18.98%, while increasing FESS utilization by over 40%. In city driving, FESS contribution rises from 4.00 kWh to 12.18 kWh, reducing battery dependency significantly. Regenerative braking efficiency remains constant at 76.92% across all profiles, indicating stable energy recovery. The findings highlight that PMO and traditional methods tend to underutilize FESS, especially in stop-and-go traffic. MVO effectively redistributes energy by dynamically engaging FESS and super capacitors, minimizing battery cycling and thermal load. This contributes to improved system efficiency, better urban driving performance, and lower carbon impact. These results establish MVO as a highly effective and computationally efficient approach for real-time EV energy management and fuel-equivalent savings.