Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

Bianca Caiazzo,Angelo Coppola,Alberto Petrillo,Stefania Santini

doi:10.3390/en14165122

Abstract

This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

Highlights

Several works have highlighted the main benefits of electric vehicles (EVs) as follows: (i) greenhouse gas emission reduction compared to traditional internal combustion engine vehicles (ICEVs); (ii) noise reduction, which represents a benefit for individual users, non-users and more generally, for the whole urban environment since it is considered as a new pollutant agent; (iii) improved performances with respect to ICEVs in terms of accelerations and energy efficiency due to instant torque and comfortable driving; (iv) economic saving both from an individual and a social point of view; (v) a positive coordination with renewable energy sources (RESs) within an electric grid by adjusting the load variation while offsetting the negative impact of RESs on the grid due to their intermittent nature
Leveraging the multi-agent systems (MASs) framework, a set of connected vehicles can be modelled as a directed graph G N = {V N, E N }, with V N = 1, 2, . . . , N the set of vehicles belonging to the network, while E N = V N × V N is the edges set used to mimic direct and active communication links
We introduce the neighbouring set Ni for the i-th EV as the set Ni = { j| aij = 1}: this means that vehicle i can receive information from any j ∈ Ni

Summary

Introduction

Electrification is an effective approach towards low-carbon future transportation [1]. Since the transportation sector accounts for approximately 25% of global energy consumption and 26% of energy-related carbon dioxide emissions, the wide spread of electric vehicles (EVs) has been proven to be the most suitable environmentalfriendly choice with respect to conventional vehicles, achieving crucial acceptance in today’s market [1,2]. Several works have highlighted the main benefits of EVs as follows (see [3,4] and references therein): (i) greenhouse gas emission reduction compared to traditional internal combustion engine vehicles (ICEVs); (ii) noise reduction, which represents a benefit for individual users, non-users (e.g., cyclists or pedestrians) and more generally, for the whole urban environment since it is considered as a new pollutant agent;

Objectives

Methods

Findings

Conclusion