Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

TL;DR

This paper presents a real-time eco-driving control method for electric trucks that reduces energy consumption and extends battery life by optimizing speed trajectories considering topography and traffic, using a novel model and control framework.

Contribution

It introduces a new state-space model and an ADMM-based optimization approach within a model predictive control framework for energy-efficient truck driving.

Findings

Energy consumption reduced by up to 5.05%

Battery life extended by up to 35.35%

Effective real-time control under topography and traffic uncertainties

Abstract

In this paper, we develop a model to plan energy-efficient speed trajectories of electric trucks in real-time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. We then formulate an energy minimization problem and solve it by an alternating direction method of multipliers (ADMM) method that exploits the structure of the problem. A model predictive control framework is then employed to deal with topographic and traffic uncertainties in real-time. An empirical study is conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The experimental results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by as high as 35.35% compared to benchmarking solutions.