Methodology for Modeling and Energy Management Control of a Parallel Hybrid Powertrain on Recurrent Routes Using Energetic Macroscopic Representation
This paper presents the methodology to design and optimize a predictive Rule-Based Energy Management Strategy (RB EMS) for real-time control applications, intended to minimize the fuel consumption on a recurrent route, e.g. the daily work-home commute.
The optimization process takes into consideration the desired trip profile, selected by the driver on the vehicle onboard GPS and linked to a traffic management system. A basic RB EMS, emulating the vehicle performance and energy consumption is first set using on-road measurement data logging. As a second step, the dynamic programming optimization routine is applied to the vehicle model, assuming a repeated NEDC cycle as the scheduled route. Obtained results of the powertrain components behavior under optimal control are evaluated and used to update the operating energy management rules of the basic controller. Finally, an optimized RB-controller is proposed by coupling between the dynamic programming and the basic RB-controller, followed by an evaluation of the energy consumption and powertrain efficiency resulting from the three investigated control strategies of a mass-production parallel hybrid vehicle (Peugeot 308). The powertrain is modeled using the energetic macroscopic representation.