Hybrid Model Predictive Control for Hybrid Electric Vehicle Energy Management Using an Efficient Mixed-Integer Formulation

Abstract

This paper proposes to use hybrid model predictive control (HMPC) for energy management in hybrid electric vehicle (HEV) using an efficient formulation. HEV has two sources of energy - electric motor and internal combustion engine (ICE) - allowing it an additional degree of freedom to optimize the ratio between the use of two energy sources. HEV energy management is crucial to exploit its potential to reduce fuel consumption and emissions. However, it is challenging to achieve the optimal solution along with the fast dynamics of HEVs. In this paper, instead of using a nonlinear, computationally expensive dynamic model of HEV, a piecewise afne (PWA) model is used to depict its behavior, implemented with multiple linear regression. The validation of the developed model is taken with standard drive cycles. Based on the PWA model, an optimal control problem of HMPC was formulated as mixed integer linear programming (MILP). Conventionally, mixed logical dynamical (MLD) system has been used in the process control field, including early studies of HEV control. This paper applies Big-M formulation which is efficient in its problem size and tight inequality relation for integer variables. The performance of the HMPC controller was examined in a simulated environment based on MATLAB/Simulink HEVP2 application. As a result, HMPC shows superior control performance than the equivalent consumption minimization strategy (ECMS).