Simplified-road-condition-based global optimization and calibration strategy for PHEV energy management

As a key technology for plug-in hybrid electric vehicles (PHEVs), energy management strategy significantly impacts their fuel economy and emissions. Existing strategies often fail to fully exploit PHEV energy-saving potential in engineering applications due to inherent limitations. This study proposes a simplified driving condition prediction method using satellite navigation, vehicle-to-everything (V2X) technology, and intelligent transportation systems, constructing substitute driving conditions from low/medium/high-speed typical profiles based on the principle that similar operating points in the characteristic maps of internal combustion engines and drive motors exhibit comparable efficiency. A novel energy management strategy based on global optimization and calibration using substitute driving profiles is developed. Taking low/medium/high-speed mileage proportions, total distance, and state of charge (SOC) as initial conditions, particle swarm optimization (PSO) is employed to offline-optimize rule-based control thresholds (power allocation factors, engine operating boundaries, etc.), generating optimal calibration maps embedded in a PHEV controller. Online validation is conducted via a driving case. Key research includes building an Isight-Cruise-Simulink co-simulation platform for vehicle-level optimization. Case studies show that under a depleted SOC (30 %), the proposed strategy achieves a 5.7 % improvement in comprehensive fuel economy compared to rule-based strategies, with results slightly lower than full global optimization. These findings validate the feasibility and effectiveness of the simplified driving condition method, offering a potential solution to the challenge of existing strategies struggling to adapt rapidly to real-time driving condition changes in PHEVs.