Hybrid fuzzy-MPC based multi-objective control strategy for fast charging of electric vehicles with advanced battery thermal management and renewable grid support.

The growing adoption of electric vehicles (EVs) and the increasing integration of variable renewable energy sources (RES), such as solar and wind, present significant challenges for grid stability, battery health, and energy management during fast charging. This study proposes a novel hybrid control strategy that combines offline-tuned Fuzzy Logic Control (FLC) with Model Predictive Control (MPC) to enhance the efficiency and reliability of EV fast charging in grid-connected, RES-driven environments. Unlike conventional approaches that rely solely on MPC or FLC, the proposed method leverages FLC's adaptive decision-making to optimize key MPC parameters offline, thereby improving real-time responsiveness and control robustness. Once tuned, the MPC controller predicts grid states, renewable power fluctuations, and EV charging dynamics to optimize charging speed while maintaining thermal safety, reducing battery degradation, and minimizing grid stress. Simulation and experimental results under various operating conditions-including fluctuating renewable inputs, multiple EV charging demands, and dynamic grid behaviour, demonstrate that the proposed hybrid controller significantly outperforms traditional methods in terms of charging efficiency, power quality, battery protection, and renewable energy utilization. The findings also highlight the potential for extending this control framework to future vehicle-to-grid (V2G) applications, contributing to more resilient and sustainable smart grid operations.