Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle

This study presents a comprehensive multi-objective optimization approach for a dual HESS-based electric vehicle (EV) powertrain using the interactive adaptive-weight genetic algorithm (i-AWGA) method. The dual HESS EV concept aims to show the benefits of combining independent traction systems powered by their respective energy sources. Therefore, the main purpose of this optimization is to simultaneously maximize driving autonomy and battery lifespan and minimize HESS size, considering design variables from components such as batteries, electric motors, differential, and ultracapacitors. At the same time, three independent fuzzy logic controllers - which perform the power management control between the hybrid energy storage systems - are likewise optimized, tuning their parameters according to the applied constraints. The best trade-off solution, equipped with a 332.34 kg dual HESS mass, achieved a driving range of 285.56 km and a front battery life cycle of 36585 h. As compared to a similar EV powered by a single HESS and optimized under the same driving conditions, the dual HESS EV improved the ratio between the driving range and energy storage system's overall mass by 3%, reaching a driving range 19.57% longer, and increasing the battery life by up to 22.88%.

Automated summary

This study presents a comprehensive multi-objective optimization approach for a dual HESS-based EV powertrain. By maximizing driving range and battery lifespan, minimizing HESS size, and optimizing power management controller parameters, the best solution achieves an increase in driving range and battery life.