Energy-Optimal Trajectory Planning for Electric Vehicles using Model Predictive Control
Paper in proceeding, 2024

This paper proposes a space-sampled Economic Model Predictive Control (EMPC) approach to jointly minimize total energy consumption of an electric vehicle (EV) and track both longitudinal velocity and path curvature reference trajectories. We consider a single-track vehicle model constrained to the range of accelerations ± 3 m/s2, and energy consumption is modelled explicitly including power losses of electric machines. Simulations with the high-fidelity simulator IPG CarMaker show the trade-off between energy consumption and reference tracking. Namely, results show how longitudinal velocity and acceleration control significantly impact energy consumption, whereas deviating from the path centerline mainly allows better velocity tracking.

Author

Alexandre Rocha

Student at Chalmers

Anand Ganesan

Chalmers, Electrical Engineering, Systems and control

Volvo

Derong Yang

Volvo

Nikolce Murgovski

Chalmers, Electrical Engineering, Systems and control

2024 European Control Conference, ECC 2024

1346-1351
9783907144107 (ISBN)

2024 European Control Conference, ECC 2024
Stockholm, Sweden,

Subject Categories

Vehicle Engineering

Energy Systems

Robotics

Control Engineering

DOI

10.23919/ECC64448.2024.10590794

More information

Latest update

8/13/2024