A Modelling Framework for Energy-Management and Eco-Driving Problems using Convex Relaxations

TL;DR

This paper introduces a convex optimization framework for eco-driving and vehicle energy management, enabling efficient and globally optimal solutions by relaxing non-convex problems under certain conditions.

Contribution

The paper develops a unified convex modeling approach for eco-driving and energy management problems, demonstrating their solvability via convex relaxation with guarantees of global optimality.

Findings

Convex relaxation yields globally optimal solutions under mild conditions.

The framework applies to various eco-driving and energy management problems.

Numerical example confirms the effectiveness of the approach.

Abstract

This paper presents a convex optimization framework for eco-driving and vehicle energy management problems. We will first show that several types of eco-driving and vehicle energy management problems can be modelled using the same notions of energy storage buffers and energy storage converters that are connected to a power network. It will be shown that these problems can be formulated as optimization problems with linear cost functions and linear dynamics, and nonlinear constraints representing the power converters. We will show that under some mild conditions, the (non-convex) optimization problem has the same (globally) optimal solution as a convex relaxation. This means that the problems can be solved efficiently and that the solution is guaranteed to be globally optimal. Finally, a numerical example of the eco-driving problem is used to illustrate this claim.