A New Real-Time Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles

Abstract

Based on the equivalent consumption minimization strategy (ECMS), a novel real-time energy management (EM) strategy for parallel hybrid electric vehicles (HEVs) is introduced. Given the full trajectory of the driver demanded power, the ECMS optimal equivalent factor $\lambda ^{*}$ can be determined. For causal EM strategies, the entire drivecycle is not known in advance. Thus, adaptive ECMS (A-ECMS) was introduced, which sets the time-varying equivalent factor $\lambda$ as an estimate of $\lambda ^{*}$ . The proposed EM strategy is an A-ECMS. This EM strategy is designed to catch energy-saving opportunities (CESOs) during the trip, and thus, it is named ECMS-CESO. Since ECMS-CESO eliminates the calculations used for predicting the vehicle velocity and performing horizon optimization, it is easy to implement and fast for real-time applications. Simulation results show that ECMS-CESO yields fuel economy (FE) close to the maximum FE. Compared with an A-ECMS, the proposed strategy improves FE by 7%.