Parameter-optimization-based ECMS for plug-in hybrid electric buses considering driving cycle recognition

An appropriate energy management strategy is crucial to achieving energy-saving and emission-reduction goals for plug-in hybrid electric buses (PHEB). This study proposes an adaptive equivalent consumption minimization strategy (A-ECMS) considering the driving cycle recognition based on the P2 configuration to achieve dynamic real-time adjustment of the equivalent factor (EF). First, local driving conditions are collected and preprocessed using an intelligent transportation system (ITS). Second, features are extracted from the speed window using the sliding window method, and an algorithm combining Particle Swarm Optimization and a Support Vector Machine (PSO-SVM) is used for driving cycle data classification prediction and relevant parameter optimization. Moreover, three key parameters-EF, the penalty factor, and the transmission ratio of the main reducer-are optimized using the Grey Wolf Optimizer (GWO) to reduce operating costs, and model simulations are conducted for verification. The comparison between the simulation results and the hardware-in-the-loop (HIL) test results shows that the proposed strategy can achieve excellent fuel economy, which is consistent with the expected results.