A Novel Model Predictive Control Based Co-Optimization Strategy for Velocity Planning and Energy Management of Intelligent PHEVs
Yuanjian Zhang, Zheng Chen, Guang Li, Yonggang Liu, Yanjun Huang
Abstract
Facilitated by the advanced abilities in environment sensing and integrated communication, intelligent plug-in hybrid electric vehicles (IPHEVs) enable massive autonomy in decision-making. The evolution towards intelligence imposes stringent demand on optimal control in IPHEVs, of which the velocity planning and energy management is strongly coupled. To address this issue, a novel model predictive control (MPC) based control strategy is designed for IPHEV with the dramatically improved ability in real time implementation. The inexact Kantorovich sequential quadratic programming (iKSQP) method is exploited to efficiently solve the cooperative control problem within the receding horizon. The reference velocity involving the detailed description on future driving and careful consideration of safe distance to forward vehicles is obtained via the microcosmic traffic flow analysis (MTFA). The simulation results manifest the superior performance of the raised optimal control strategy in cooperatively planning optimal velocity and managing power allocation in real time.