Adaptive ECMS for trip level energy management of HEVs considering vehicle and route parameter variations

Abstract

Minimizing fuel consumption is critical to justify the additional investment in motors and batteries for Hybrid Electric Vehicles (HEVs). This requires a trip-level energy management (TEM) strategy that accounts for dynamic vehicle parameters, such as mass, rolling resistance, and powertrain efficiency, alongside future drive cycles influenced by traffic, vehicle loading, and driver behaviour. Conventional TEM approaches, assuming nominal parameters, compromise fuel economy and charge sustainability. This paper presents a hierarchical and computationally efficient TEM technique integrating real-time vehicle parameter estimation with personalized drive cycle prediction. The method utilizes dynamic vehicle parameter models, interactive multiple models, and multi-scale drive cycle analysis to capture individual driver behaviour and traffic evolution. Validation on standard and ViSSIM-generated drive cycles, along with Driver-in-the-Loop simulations, shows a 4 %–6 % fuel economy improvement compared to conventional TEM. Onboard implementation feasibility is demonstrated through Hardware-in-the-Loop testing on an industrial embedded platform.