Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed Integer Nonlinear Programming

TL;DR

This paper presents a novel continuous relaxation method for solving complex mixed integer nonlinear programs to optimize electric vehicle speed and gearshift, significantly improving energy efficiency with feasible real-time computation.

Contribution

It introduces a new relaxation technique for MINLPs in vehicle control, enabling efficient real-time optimization of speed and gearshift for energy savings.

Findings

Co-optimization yields higher energy efficiency than sequential optimization.

The relaxation technique reduces computational cost to real-time levels.

Simulation confirms the effectiveness of the proposed approach.

Abstract

In this paper, we propose an approach to coordinated receding-horizon control of vehicle speed and transmission gearshift for automated battery electric vehicles (BEVs) to achieve improved energy efficiency. The introduction of multi-speed transmissions in BEVs creates an opportunity to manipulate the operating point of electric motors under given vehicle speed and acceleration command, thus providing the potential to further improve the energy efficiency. However, co-optimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can be computationally very challenging. In this paper, we propose a novel continuous relaxation technique to treat such MINLPs that makes it possible to compute solutions with conventional nonlinear programming solvers. After analyzing its theoretical properties, we use it to solve the optimization problem involved in coordinated receding-horizon control of BEV speed and gearshift. Through simulation studies, we show that co-optimizing vehicle speed and transmission gearshift can achieve considerably greater energy efficiency than optimizing them sequentially, and the proposed relaxation technique can reduce the online computational cost to a level that is comparable to the time available for real-time implementation.