Energy Management Strategy for Plug-in-Hybrid Electric Vehicles Based on Predictive PMP

This article presents an energy management strategy for parallel plug-in-hybrid electric vehicles based on Pontryagin's minimum principle (PMP) that is suitable for series-production vehicles. The PMP-strategy uses journey prediction data obtained from a series-production navigation unit. This introduces two important practical aspects that are addressed in detail in this work. First, the journey prediction provides clustered information such as estimated average speeds from the navigation map. This has a strong effect on the applicability of PMP due to so-called singularities that systematically appear whenever changes in the engine ON/OFF command occur. Second, the prediction of the journey is necessarily uncertain. This implies that an open-loop PMP-solution is not sufficient and feedback has to be introduced. Two methods are discussed: one based on repeated optimization and the other based on an adaptive PMP approach. In this article, we furthermore present a novel solution method to deal with singular controls, which is easy enough to be relevant in automotive practice and is computationally efficient. The method achieves a performance very close to a benchmark solution, which is obtained from an offline application of PMP based on the information about the actual journey. An experimental study shows that there is considerable potential for energy consumption savings of 5.29% compared with a baseline control strategy.

Automated summary

This article introduces an energy management strategy for parallel plug-in-hybrid electric vehicles based on Pontryagin's minimum principle, utilizing journey prediction data obtained from a series-production navigation unit. The novel solution method proposed in this work effectively deals with singular controls and achieves considerable energy consumption savings of 5.29% compared to a benchmark control strategy.