This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.Keywords: plug-in hybrid electric vehicle (PHEV); battery health; energy management strategy; stochastic dynamic programming (SDP); particle swarm optimization (PSO)

To further improve the transient and steady-state performance of automotive electronic throttle position tracking, in this paper an adaptive prescribed performance servo control strategy is designed and applied to a real electronic throttle control system. In view of the possible high gain of the prescribed performance controller in practice, the actuator constraint is also considered in the controller design. The designed servo controller can ensure the transient and steady-state responses of tracking error are limited in the range prescribed by the performance function, and converge with the prescribed convergence rate and have no overshoot. The incorporated adaptive updating law can enhance the robustness of the transient and steady-state performance against uncertainty from the product tolerance, the operating conditions, and the aging of components. Both Matlab/Simulink simulation and dSPACE-based hardware-in-the-loop experimental verification show the effectiveness and applicability of the proposed control strategy.

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