Adaptive ECMS Based on EF Optimization by Model Predictive Control for Plug-In Hybrid Electric Buses

The plug-in hybrid electric bus (PHEB) is a relatively common new energy bus for urban use. In this article, to achieve the purpose of adaptive real-time optimization for fuel economy, a novel energy management strategy (EMS) is proposed to predict the battery state of charge (SOC) and optimize the equivalent factor combined with an adaptive equivalent fuel consumption minimization strategy (A-ECMS). For the accuracy of prediction, $K$ -means and $K$ -nearest neighbors algorithm (KNN) are used to distinguish and classify paths. On this basis, the model predictive control algorithm is used to predict the vehicle battery SOC, with the selection of the equivalent factor optimized according to the battery reference SOC trajectory. The proposed strategy has been verified not only under various working conditions of the simulation software but also on the hardware-in-the-loop (HIL) platform. The experimental results show that the proposed strategy can better predict the change of SOC under mixed working conditions, and the predicted trajectory is close to the reference SOC trajectory. The simulation results and the HIL results both show that the proposed strategy performed well for fuel economy optimization.