Comparative Energy Assessment of Automated Electric Vehicles Equipped With Acc Systems

This paper aims at conducting a comprehensive energy assessment of Automated Electric Vehicles equipped with two different type of ACC systems. By systematically comparing the energy performance of these two control strategies, insights about their respective efficiencies are provided under various driving conditions. The study involves rigorous testing and analysis, focusing on the energy consumption patterns of EVs operating under PI and MPC control schemes. The employed evaluation methodology integrates stochastic simulation with the PAWN sensitivity technique, in order to capture and quantify the impact of different driving variables on the energy output. The results of this research are expected to inform the development of more energy-efficient ACC systems, thereby enhancing the overall efficiency of EVs.

I. Introduction

The global push towards sustainable transportation has significantly increased the employment of Electric Vehicles (EVs). Due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels, EVs are heralded as a key component of a cleaner future. However, energy efficiency still remains a crucial challenge, particularly in the context of Automated/Autonomous Driving Systems (ADSs) [1]. Accordingly, the assessment of ADSs footprint is a crucial task in order to improve their operational sustainability and their design [2]. As a prime example, let us take the Adaptive Cruise Control (ACC) system into consideration, which is designed to enhance driving comfort and safety by automatically adjusting the vehicle's speed to maintain a safe distance from the vehicle ahead. Numerous studies have investigated the performance of ACC-equipped vehicles, with field experiments investigating the consumption of vehicles following an ACC-equipped one [3], [4]. The results indicate that following an ACC-equipped vehicle results in higher consumption compared to following a human-driven one. The same outcome was found in [5], which confirmed that current ACC implementations may increase energy consumption and safety risks with higher penetration rates in fleets. Usually, the performance of AEVs are assessed under nominal conditions [6], i.e. setting both vehicle and consumption model parameters at nominal values or omitting them entirely from the ADS model. However, the variation of driving parameters should not be overlooked, as they could significantly affect vehicle performance and test outcomes. Moreover, this approach is not exhaustive in capturing the complexities associated with AEVs. Finally, most of the time, only one possible control scheme for the considered ADS is considered at once.