This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.
Energy management strategies are of the vital importance in fully playing the potential of plug-in hybrid electric vehicles. This paper proposes an improved adaptive equivalent minimization strategy for a parallel plug-in hybrid electric vehicle. In this method, performance of adaptive equivalent minimization strategy is prompted through incorporating information of future driving condition into equivalent factor adjustment. Two main works have been done. First, a novel equivalent factor adjustment method is proposed in the improved adaptive equivalent minimization strategy. Based on the predicted information of future driving condition, equivalent factor is tuned in each running loop of equivalent minimization strategy. Second, information of future driving condition is predicted by harnessing floating car data. Benefit from future driving condition forecasting, the improved adaptive equivalent minimization strategy holds better capability in accommodating road condition switching and preliminary self-regulation for hilly road. Simulation results show that, compared with the convention adaptive equivalent minimization strategy, the improved adaptive equivalent minimization strategy can obtain better fuel economy.
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