Scenario Model Predictive Control for Data-Based Energy Management in Plug-In Hybrid Electric Vehicles

One of the major limitations of optimization-based strategies for allocating the power flow in hybrid powertrains is that they rely on predictions of future power demand. These predictions are inherently uncertain as they are dependent on complex human behaviors that are challenging to model accurately. This article proposes a data-based scenario model predictive control (MPC) framework, where the inputs are determined at each control update by optimizing the power allocation over multiple previous examples of a route being driven. The proposed energy management optimization is convex, and results from scenario optimization are used to bound the confidence that the one-step-ahead optimization will be feasible with given probability. It is shown through numerical simulation that scenario MPC obtains the same reduction in fuel consumption as nominal MPC with full preview of future driver behavior and that the scenario MPC optimization can be solved efficiently using a tailored optimization algorithm.