Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO₂ emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO₂ emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.

This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.