Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle

Abstract

The four-wheel independent-drive electric vehicle (FWID-EV) stands out among the electrically-propelled vehicles due to its higher efficiency. This EV configuration faces an energy management challenge, needing a controller to define the best power split among the motors, decreasing the energy consumption while keeping the EV performance. Aiming to overcome this challenge, this paper presents a fuzzy logic control (FLC) strategy to perform the power split among the electric motors to improve vehicle energy efficiency and dynamic performance under real driving conditions. The implemented controller is developed hierarchically, ensuring the speed follows a driving cycle and acting as an electronic differential to correct the vehicle trajectory along the path. Multi-objective optimization is presented using a particle swarm algorithm (MOPSO) to minimize the mass of the electrical components (motors and battery) and extend the driving range throu gh the maximization of the battery state of charge (SoC). Moreover, the optimization process also aims to enhance vehicle handling by minimizing the driver steering input during the cycle. The best trade-off solution was able to reach a 124.2 km drive range with a 235.8 kg battery (387.8 V and 91.2 Ah), presenting improved handling performance with 78.5% decrease in the driver steering action.