Trip-Oriented Model Predictive Energy Management Strategy for Plug-in Hybrid Electric Vehicles

Zhenzhen Lei,Zheng Chen,Daqi Chen,Yonggang Liu,Junjun Liu,Dongye Sun

doi:10.1109/access.2019.2933015

Abstract

Energy management strategies play a critical role in performance optimization of plug-in hybrid electric vehicles (PHEVs). In order to attain effective energy distribution of PHEVs, a predictive energy management strategy is proposed in this study based on real-time traffic information. First, an exponentially varied model for the velocity prediction is established, of which the tunable decay coefficient is regulated by the supported vector machine (SVM). In this manner, the prediction precision is improved. Then, by properly simplifying the powertrain model, the state of charge (SOC) reference trajectory is generated based on the fast dynamic programming (DP) with fast calculation speed and consideration of the traffic information. Moreover, the typical DP algorithm is leveraged to solve the nonlinear rolling optimization problem for minimizing the operating cost in a receding horizon. Simulation results demonstrate that the proposed algorithm can reach 92.83% operating savings, compared with that of the traditional DP; and save 6.18% cost compared with the MPC algorithm only with reference of the trip duration.

Highlights

Nowadays, transportation electrification paves a potential way to mitigate air pollution and decrease greenhouse gas (GHG) emission [1], [2]
By analyzing planning algorithms of the reference state of charge (SOC) trajectory in existing literatures, we summarize that they are mainly divided into two categories
Inspired by the discussions mentioned above, we conclude that a predictive control framework with full incorporation of traffic information should be able to achieve superior energy management of plug-in hybrid electric vehicles (PHEVs) with real-time application

Summary

INTRODUCTION

Transportation electrification paves a potential way to mitigate air pollution and decrease greenhouse gas (GHG) emission [1], [2]. Its effect cannot be competitive with that solved by DP, since the latter one is obviously not a linear or piecewise linear function with respect to driving distance or duration due to the time-varied velocity and power demand To overcome this drawback, determination of the SOC reference should fully consider historical and current transportation information. Inspired by the discussions mentioned above, we conclude that a predictive control framework with full incorporation of traffic information should be able to achieve superior energy management of PHEVs with real-time application This becomes the main motivation of our research. An MPC control framework is constructed including the fast SOC reference that is solved by DP with real-time updated traffic information, and the future speed prediction implemented by an exponentially varied algorithm. Where I is the current of battery, Voc(SOC) and Rbatt (SOC) denote the open circuit voltage and internal resistance with respect to SOC, and Qbatt means the battery capacity

PREDICTIVE ENERGY MANAGEMENT STRATEGY

TRIP MODELING

SIMULATION AND DISCUSSIONS

PERFORMANCE COMPARISON OF MPC WITH AND WITHOUT TRAFFIC INFORMATION

Findings

CONCLUSION