Comparison of Optimal Supervisory Control Strategies for a Series Plug-In Hybrid Electric Vehicle Powertrain

Abstract

This paper uses dynamic programming to compare the optimal fuel and electricity costs associated with two supervisory control strategies from the plug-in hybrid electric vehicle (PHEV) literature. One strategy blends fuel and electricity for propulsion throughout the useful range of battery state of charge (SOC), while the second strategy switches from all-electric to blended operation at a predefined SOC threshold. Both strategies are optimized for a series PHEV powertrain using deterministic dynamic programming (DDP) to ensure a fair comparison. The DDP algorithm is implemented in a novel manner using a backward-looking powertrain model instead of forward-looking models used in previous research. The paper’s primary conclusion is that there is no significant difference in the performance of the two control strategies for the series PHEV considered. This result contrasts sharply with previous results for parallel and power-split PHEVs, and is examined for different relative fuel and electricity prices and trip lengths.