Research on Energy Consumption Optimization Control Strategy for Hybrid Electric Passenger Vehicles

The energy management strategy of hybrid electric vehicles (HEVs) is critical to achieving overall vehicle efficiency. Current global optimization methods involve significant computational complexity. Conventional or adaptive equivalent consumption minimization strategy (ECMS) also presents challenges in robustness and calibration efficiency, as it typically depends on real-time driving condition recognition and online parameter adjustment. To address these issues, this study proposes an engineering optimization framework based on a system efficiency lookup table approach. This method uses offline iterative simulation to determine a globally representative equivalent factor. It pre-computes optimal system efficiency, drive modes, and energy allocation across the entire operating range. These results are stored in a lookup table that the vehicle control unit (VCU) can access in real time. This approach eliminates the uncertainties associated with online optimization. It also provides calibration engineers with a quantitative tool to directly balance competing objectives, such as fuel economy and NVH. The optimized strategy increases the proportion of electric and parallel mode operation. This concentrates engine operating points within the high-efficiency zone and significantly reduces losses from multi-stage energy conversion. Simulations, dynamometer tests, and real-world road tests confirm the strategy’s effectiveness and repeatability. The approach achieves a fuel consumption reduction of approximately 0.2 L/100 km, with all test coefficients of variation (CV) remaining below 5%. The framework demonstrates good extensibility and can be adapted to different architectures, such as plug-in hybrid electric vehicles (PHEVs) and fuel cell vehicles (FCVs). It offers a universal and efficient engineering solution for optimizing energy consumption in hybrid powertrain systems.