Real-Time Eco-Driving for Connected Electric Vehicles

Caroline Ngo (1) , Edwin Solano-Araque (1) , Missie Aguado-Rojas (1) , Antonio Sciarretta (1) , Bicheng Chen (2) , Mohamed El Baghdadi (3)

1 IFPEN - IFP Energies nouvelles
2 RWTH - Rheinisch-Westfälische Technische Hochschule Aachen University
3 VUB - Vrije Universiteit Brussel

This paper presents a real-time eco-driving algorithm for connected electric vehicles. The proposed solution generates a safe eco-speed profile, avoiding collision with the preceding vehicle and respecting the speed limits. An Eco-driving Optimal Control Problem (ED-OCP) is formulated minimizing the energy consumption of an electric vehicle while enforcing state (position, speed, acceleration) constraints. Analytical solutions of the state-constrained ED-OCP are implemented according to a model predictive control scheme. The proposed solution is evaluated using a connected simulation platform developed during the H2020 EU project CEVOLVER, under several driving scenarii, showing a significant energy consumption reduction.

Mots clés: optimal control; eco-driving; connected electric vehicle optimal control; connected electric vehicle

Domaines: Sciences de l'ingénieur [physics]; Energie électrique; Electronique

Caroline Ngo, Edwin Solano-Araque, Missie Aguado-Rojas, Antonio Sciarretta, Bicheng Chen, et al.. Real-Time Eco-Driving for Connected Electric Vehicles. IFAC-PapersOnLine, 2021, 54 (10), pp.126-131. ⟨10.1016/j.ifacol.2021.10.152⟩. ⟨hal-03498077⟩