MPC-based longitudinal control strategy considering energy consumption for a dual-motor electric vehicle

To improve the energy economy and speed tracking qualities of an unmanned electric vehicle (EV) having a dual-motor powertrain, this paper proposes a model predictive control (MPC) based longitudinal control strategy considering energy consumption. Firstly, an enhanced vehicle longitudinal dynamic model considering powertrain response performance is built as predictive model to guarantee the high precision and robustness of speed prediction. Secondly, pedal command is solved by an online activity set method aiming at minimizing speed tracking errors to realize fast and reliable real-time solving. Finally, an efficient energy management strategy (EMS) is developed to optimize the demand torque distribution and gear shifting. Acquiring these two quantities with an offline global optimization method, the strategy addresses frequent gear shifting problems by online adjusting gear shifting lines. The real-time performance of the proposed strategy is validated in a HIL test. Results show that the proposed MPC-based strategy improves the speed tracking accuracy by 58.93% and expands the high efficiency range of powertrain by 40.93%. The equivalent electric consumption of the EV is reduced by 9.29%. This study provides a foundation for the practical application of longitudinal control algorithms on EVs in the future.