designing an energy management control system in hybrid vehicles using an optimized fuzzy method

Improving fuel efficiency and enhancing the dynamic performance of hybrid electric vehicles are critical challenges in modern powertrain control design. this paper proposes a novel optimized fuzzy logic-based energy management strategy specifically developed for a class b hev. the main objective is to reduce fuel consumption and emissions while ensuring effective power distribution among key drivetrain components. the study introduces a two-stage methodology: first, an optimal sizing of the powertrain components—internal combustion engine, electric motor, and battery—is achieved using a genetic algorithm, ensuring the most efficient configuration for vehicle performance. second, three different energy management strategies are implemented and compared: a conventional rule-based control, a standard fuzzy logic controller, and the proposed optimized fuzzy controller. simulation results demonstrate that the optimized fuzzy strategy significantly improves fuel economy and emission performance compared to the other methods. specifically, it achieves up to 20% better fuel efficiency than the rule-based controller while maintaining smooth power transitions. the study also highlights the impact of component sizing on control effectiveness, reinforcing the advantage of co-optimization of both sizing and control logic. the findings suggest that integrating intelligent optimization techniques such as ga with fuzzy control logic provides a superior approach to energy management in hevs. this makes the proposed method a promising solution for next-generation hybrid vehicle applications aiming for both environmental sustainability and high performance.