Energy-Efficient MPPT Control in PEMFC Powered Electric Vehicles using Fuzzy Logic and MOPSO-Based Optimization

With the increasing demand for propulsion systems with high cleanliness and efficiency, Proton Exchange Membrane Fuel Cells (PEMFCs) have become one of the most promising energy sources for EVs. However, the PEMFC has nonlinear and dynamic properties that require robust and adaptive controls in order to maintain continuous operation at or near the maximum power point (MPP) under a changing load and environment. In this paper, a new MPPT scheme, which combines FLC and MOPSO, is proposed to further improve the energy conversion efficiency and dynamic capability of the exemplary PEMFC based EV systems. The proposed FLC is utilized, with the rule-based flexibility of FLC to handle nonlinear PEMFC dynamics, combined with MOPSO in order to optimize the membership functions and rule base of the fuzzy inference system. Optimizing goals are power maximization, fuel-cost-minimization and tracking error reduction, so as to achieve a compromise between efficiency, stability, and response. A complete simulation model of the PEMFC-EV system is established in MATLAB/Simulink by taking into account practical drive cycles, thermal effects, and load fluctuations. Simulation results indicate that the FLC-MOPSO hybrid optimized controller provides better MPPT performance as compared with classical P&O and FLC alone methods. In particular, the proposed approach achieves the convergence to the MPP with faster rate, dynamic extraction ability of power, and fuel efficiency improvements. Moreover, the proposed controller guarantees its robust characteristic with respect to parameter variations and external perturbations, and it operates successfully over a wide operating envelope. This research constitutes a step towards intelligent control strategies for fuel cell electric vehicles and demonstrates the promise of using soft computing and metaheuristic optimization in future green mobility. The proposed approach provides a viable and energy-efficient solution for online MPPT control in future PEMFC based EVs