Integrated Optimization of Ride Comfort and Battery Degradation in Pure Electric Vehicles with Active Suspension

Pure electric vehicles are increasingly important for reducing transportation emissions, improving energy efficiency, and supporting sustainable mobility. However, their performance depends strongly on battery health, energy consumption, and vehicle dynamic behavior. This study proposes a genetic-algorithm-optimized fuzzy active suspension controller for a pure electric vehicle by simultaneously considering ride comfort, suspension travel, battery state of charge, and battery degradation. An integrated EV–active suspension simulation framework is developed by combining the electric powertrain, battery aging model, and full-car active suspension model. The fuzzy controller membership functions are optimized using a genetic algorithm and evaluated under UDDS, NEDC, and WLTP Class ۳ driving cycles. The objective function combines weighted ride comfort, front and rear suspension travel, final SOC, and battery capacity loss. The results show that the optimized controller improves ride comfort by ۱۵.۳۲% in NEDC, ۲.۱۸% in UDDS, and ۲.۳۶% in WLTP Class ۳. Battery aging is also reduced by ۳.۵۱%, ۵.۱۷%, and ۴.۵۸% under the same cycles, respectively. Overall, the proposed GA-based fuzzy controller provides an effective compromise between passenger comfort, suspension performance, actuator energy demand, and battery health preservation.