A Stochastic Optimal Control Method Using Statistical Information and Binary Variables with an Application to Online Optimal Mode Management for Plug-in Hybrid Vehicles

Abstract

In this paper, a novel stochastic optimal control method based on a stochastic model predictive control frame-work is proposed. The proposed method is formulated as mixed integer linear programming using statistical information and binary variables, which allows us to obtain the deterministic optimization problem from the stochastic optimization problem. Moreover, it does not need to assume a class of stochastic process such as white noise. This paper also shows that the present method can be applied to real stochastic systems that have only low computation specifications, through an example problem on online optimal mode management for a Plug-in Hybrid Vehicle. The usefulness of the method is demonstrated via a detailed numerical simulator named ADVISOR, and the results show that the amount of the fuel consumption is reduced and computation time is small enough for the problem.