Trip-based optimization methodology for a rule-based energy management strategy using a global optimization routine

Abstract

The fuel savings of plug-in hybrid electric vehicles strongly rely on the energy management strategy deployed onboard. For the current mass-produced plug-in hybrid electric vehicles, notably the Toyota Prius, the energy management strategy is a rule-based type, which is configured to optimize instantly the fuel consumption without taking into consideration the upcoming driving patterns of the given route schedule. Hence, it operates the vehicle first in the electric mode over a predefined all-electric range and then in the charge-sustaining mode. The energy consumption results are seen to be far from optimal when compared with global optimization strategies with prior knowledge of the scheduled route, such as dynamic programming. Hence, this study presents the methodology to optimize the rule-based energy management strategy for real-time implementation in the Prius plug-in hybrid electric vehicle, using dynamic programming as the global optimization routine. The optimization process takes into account the desired trip profile selected by the driver on the vehicle’s onboard Global Positioning System and linked to a traffic management system. A basic rule-based energy management strategy, which emulates the vehicle performance and the energy consumption, has been set first using on-road measurement data logging. As a second step, the dynamic programming optimization routine was applied to the model, assuming a repeated New European Driving Cycle as the scheduled route. The results obtained for the behaviours of the powertrain components under optimal control are evaluated and used to update the operating energy management rules of the basic controller. Finally, an optimized rule-based controller is proposed by coupling between the dynamic programming and the basic rule-based controller, followed by an evaluation of the energy consumption and the powertrain efficiency of the three investigated control strategies.