Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution.
On Implementing Optimal Energy Management for EREV Using Distance Constrained Adaptive Real-Time Dynamic Programming
