Adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicle
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Abstract:
In order to improve the fuel economy of plug-in hybrid vehicles (PHEV), the optimal trajectories of state of charge (SOC) under different typical driving cycles and different driving distances were simulated and analyzed by using dynamic programming (DP) algorithm. On the basis of equivalent fuel consumption minimum strategy (ECMS), PI control was used to update the energy fuel equivalent factor in real time, so as to ensure that the actual SOC trajectory could roughly follow the theoretical reference trajectory, and then an adaptive equivalent consumption minimization strategy (AECMS) which can be controlled in real time was proposed. To verify the effectiveness of the proposed control strategy, different typical working conditions and driving mileages were used to simulate and compare the control performances of ECMS, DP and AECMS. The results show that the control effect of AECMS was close to that of DP and could be controlled in real time. AECMS reduced fuel consumption by 3.50%~8.71% in CD mode and 1.11%~2.46% in CS mode compared with ECMS.
