Multi-objective optimal torque allocation strategy for hub motor
electric vehicles considering road conditions
Abstract
Improved hybrid genetic particle swarm optimization (IGPSO)
algorithm-based optimal torque allocation technique is suggested to
enhance the overall performance of hub motor electric vehicle (HMEV)
under various road conditions. The strategy adopts a hierarchical
control structure, using PID to track the speed signal in the upper
controller to obtain the demand longitudinal torque, designing the
objective functions of handling stability, energy saving, and comfort in
the lower controller, and designing a fuzzy controller to determine the
weight coefficients of different optimization objective functions of the
strategy, and proposing the IGPSO algorithm to solve the final
optimization problem to obtain the optimal torque distribution results.
The results of the New European Driving Cycle (NEDC) condition with
different road adhesion coefficients show that the IGPSO strategy can
significantly improve the maneuvering stability, energy economy, and
comfort of the HMEV compared to the Even Distributed (ED) strategy.