Multiobjective optimization of longitudinal dynamics and energy management for HEVs based on nash bargaining game

Appropriate coordination among multiple power components is essential to improve energy efficiency, traffic safety and driving comfort simultaneously for hybrid electric vehicles. Previous methods for these multiobjective co-optimization issues, on the other hand, may result in misleading optimization when the vehicle is driven in complicated and varied scenarios. To overcome these limitations, this paper proposes a novel multiobjective optimization controller based on the Nash bargaining game in which the longitudinal dynamic control and energy management strategy are treated as two independent players. The Nash equilibrium is selected as the threatpoint and obtained through a linear quadratic game approach. The Nash bargaining solution (NBS) is then computed based on the alternating direction method of multipliers (ADMM). Simulation results demonstrate that the proposed controller can outperform the hierarchical optimization controller with average 5.6% fuel efficiency improvement and the centralized controller in the aspects of maintaining the optimality and robustness of the control performance.