Optimal Energy Management Strategy for Electric Vehicles Based on Fuzzy Logic Controller Integrated with PSO-NM Optimization Approach

Pairing a high-power source like a supercapacitor (SC) with a lithium-ion battery can enhance the efficiency, durability, and performance of pure electric vehicles. However, designing a real-time supervisory controller that delivers excellent power split performance poses a significant challenge in battery-supercapacitor-powered EVs. This research proposed a global fuzzy logic-based energy management strategy (GFEMS) integrated with a hybrid Particle Swarm-Nelder Mead (PSO-NM) optimization algorithm, offering an optimal solution by optimizing power flow in HESS. The proposed strategy aims to optimize the HESS power flow by reducing battery current RMS and minimizing the state of charge (SOC) differences in SC from the start to the end of a trip. The optimization algorithm helps fine-tune the proposed fuzzy logic-based controller’s rule weights to meet the above objectives. Following the completion of the offline optimization process, the control strategy can be applied in real-time and applicable to any drive cycle and vehicle type. Simulation results demonstrate that the proposed GFEMS provides a superior overall control performance and minimum SC’s SOC variations for both highway and congested city driving scenarios. In contrast to a battery-only ESS, the proposed GFEMS showcases its competence by diminishing the peak battery current by a significant 70.34% for the US06 drive cycle.