Real-time Multi-Layer Predictive Energy Management for a Plug-in Hybrid Vehicle based on Horizon and Navigation Data 2024-01-2773

Plug-In Hybrid Vehicles (PHEV) have been of significant importance recently to comply with future CO₂ and pollutant emissions limit. However, performance of these vehicles is closely related to the energy management strategy (EMS) used to ensure minimum fuel consumption and maximize electric driving range. While conventional EMS concepts are developed to operate in wide range of scenarios, this approach could potentially compromise the fuel consumption benefit due to the omission of route and traffic information. With the advancements in the availability of real-time traffic, navigation and driving route information, the EMS can be further optimized to extract the complete potential of a PHEV. In this context, this paper presents application of predictive energy management (PEM) functionalities combined with information such as live traffic data to reduce the fuel consumption for a P1/P3 configuration PHEV vehicle. The proposed PEM uses on-board navigation and E-horizon data based on Advanced Driver Assistance Systems Interface Specifications (ADASIS). A multi-layer optimization approach is implemented across different prediction horizons. In the long horizon, Dynamic Programming (DP) calculates the optimal battery SoC (State of Charge) trajectory for the entire driving route. The search domain and discretization step of DP are optimized to ensure real-time capability. Based on this target SoC, Pontryagin’s Minimum Principle (PMP) is used in the short to medium horizon to calculate an Equivalence Factor (EF) that defines the optimal distribution between fuel and electrical energy. For the low-level EMS, Equivalent Consumption Minimum strategy (ECMS) is used that computes the torque split, gear ratio and engine on/off decision based on the EF from PMP. The fuel consumption savings for the developed PEM functions are investigated in comparison to conventional rule-based (RB) EMS for different real-world use-cases.