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

TL;DR

This paper introduces a data-driven scenario model predictive control approach for hybrid vehicle energy management, effectively handling uncertain driver behavior predictions to optimize power allocation and reduce fuel consumption.

Contribution

It presents a convex, scenario-based MPC framework that bounds feasibility probability, improving energy management under uncertain future demands in hybrid vehicles.

Findings

Achieves fuel reduction comparable to full preview MPC.

Uses scenario optimization to handle prediction uncertainty.

Optimization can be solved efficiently with a tailored algorithm.

Abstract

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 behaviours that are challenging to model accurately. This paper proposes a data-based scenario model predictive control 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 model predictive control (MPC) obtains the same reduction in fuel consumption as nominal MPC with full preview of future driver behaviour, and that the scenario MPC optimization can be solved efficiently using a tailored optimization algorithm.