Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Practical Implementation and Experimental Validation of an Optimal Control based Eco-Driving System

Vinith Kumar Lakshmanan, Olivier Lemaire, Antonio Sciarretta

TL;DR

This work develops and validates a predictive eco-driving system for electric vehicles based on Pontryagin's Minimum Principle, implemented online via Shrinking Horizon Model Predictive Control to handle time-varying traffic and lead-vehicle constraints. The practical ED solution is deployed on a Renault Zoe with a Visual driver Advisory System (VAS) that displays an energy-optimal target speed to the driver, while real-time inputs come from GPS localization, perception, and cloud-based route planning. Real-world experiments over a 2.3 km urban route show ED reduces energy consumption by about 4.6% on average compared to a conventional driver, with smoother velocity profiles and anticipative braking behavior; an eco-driving score quantifies proximity to energy-optimal traces. The work demonstrates the feasibility of integrating PMP-based optimization with driver advisory interfaces for real-time energy savings in urban driving, and outlines avenues for improvement in sensing frequency, lead-vehicle dynamics estimation, and map accuracy toward automated deployment.

Abstract

The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates the energy gains of an ED system for an electric vehicle, obtained from a predictive optimal controller, in a real-world traffic scenario. To this end, a Visual driver Advisory System (VAS) in the form of a personal tablet is used to advise the driver to follow a target eco-speed via a screen. Two Renault Zoe electric cars, one equipped with the different modules for ED and one without, are used to perform field tests on a route between Rueil-Malmaison and Bougival in France. Overall, the ED consumed, on average, 4.6~$\%$ less energy than the non-eco-driven car with a maximum of 2~$\%$ change in average speed.

Practical Implementation and Experimental Validation of an Optimal Control based Eco-Driving System

TL;DR

This work develops and validates a predictive eco-driving system for electric vehicles based on Pontryagin's Minimum Principle, implemented online via Shrinking Horizon Model Predictive Control to handle time-varying traffic and lead-vehicle constraints. The practical ED solution is deployed on a Renault Zoe with a Visual driver Advisory System (VAS) that displays an energy-optimal target speed to the driver, while real-time inputs come from GPS localization, perception, and cloud-based route planning. Real-world experiments over a 2.3 km urban route show ED reduces energy consumption by about 4.6% on average compared to a conventional driver, with smoother velocity profiles and anticipative braking behavior; an eco-driving score quantifies proximity to energy-optimal traces. The work demonstrates the feasibility of integrating PMP-based optimization with driver advisory interfaces for real-time energy savings in urban driving, and outlines avenues for improvement in sensing frequency, lead-vehicle dynamics estimation, and map accuracy toward automated deployment.

Abstract

The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates the energy gains of an ED system for an electric vehicle, obtained from a predictive optimal controller, in a real-world traffic scenario. To this end, a Visual driver Advisory System (VAS) in the form of a personal tablet is used to advise the driver to follow a target eco-speed via a screen. Two Renault Zoe electric cars, one equipped with the different modules for ED and one without, are used to perform field tests on a route between Rueil-Malmaison and Bougival in France. Overall, the ED consumed, on average, 4.6~ less energy than the non-eco-driven car with a maximum of 2~ change in average speed.
Paper Structure (7 sections, 12 equations, 7 figures)

This paper contains 7 sections, 12 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Eco-Driving with Pontryagin's Minimum Principle
  3. Optimal Control Problem Formulation
  4. Implementation: Model Predictive Control
  5. Practical Implementation of Eco-Driving
  6. Experiments and Results
  7. Conclusion

Figures (7)

  • Figure 1: The tablet mounted on the dashboard displays the ED target speed.
  • Figure 2: Schematic of the eco-driving advisory system.
  • Figure 3: The HMI Eco-Driving.
  • Figure 4: Speed (a) and energy (b) profiles of the ED and HD for a single trip.
  • Figure 5: Gain in energy consumption vs change in average speed for the nine trips conducted.
  • ...and 2 more figures