Predictive Equivalent Consumption Minimization Strategy With Segmented Traffic Information

Abstract

This paper proposes a predictive equivalent consumption minimization strategy (P-ECMS) for a plug-in hybrid electric vehicle (PHEV), assuming the availability of two levels of traffic information. The two levels of traffic information include 1) segmented traffic information available from mobile mapping applications, and 2) detailed velocity information, possibly obtained by short-term speed forecasting. Battery state-of-charge (SOC) reference waypoints are obtained by a simplified speed profile constructed from that segmented traffic information. The proposed P-ECMS adjusts its co-state or equivalence factor based on the difference between the future SOC obtained from short-horizon prediction and a future reference SOC. The benefits of the proposed P-ECMS are evaluated through vehicle simulations on a specified trip, compared against an adaptive ECMS (A-ECMS) with the same control parameters minus prediction. Simulation results on the trip demonstrate that the proposed P-ECMS can achieve a 9.7% reduction in the corresponding fuel consumption on average, compared to the A-ECMS across different combinations of initial co-state and control gain. Moreover, its performance is robust to variations in the initial co-state, the co-state update rate, and the prediction horizon, as well as to inaccuracies in the velocity prediction. Compared to the A-ECMS, the standard deviation in fuel consumption with the P-ECMS can be reduced by 96% from 84.1 to 3.4 grams.