Work

Experimental validation of predictive optimal control based eco-driving

Year: 2026

Type: article

Abstract: The main goal of Eco-Driving (ED) is to maximize energy efficiency. The paper’s main novelty is a real-world evaluation of an optimal control based ED advisory system for electric vehicles, derived using Pontryagin’s Minimum Principle (PMP). A Visual Advisory System (VAS), such as a digital tablet, advises the driver to follow a target eco-speed in real-time. Tests were performed with two Renault Zoe vehicles in real world traffic conditions, one equipped with the ED system and one standard, over three routes around Rueil-Malmaison, France. The ED vehicle achieved average energy savings of 4.6 % on the regional route, 13.5 % on the urban route, and approximately 4.0 % on the highway. An analysis, using Dynamic programming (DP) as benchmark, is performed to quantify the impacts of modeling assumptions and driver behavior. Segment-level analysis shows that model simplifications in the PMP formulation account for up to a 12.6 % increase in energy consumption compared to DP, while the driver tracking error contributes an additional 0.5 % in unconstrained scenarios. In contrast, in constrained driving situations involving a preceding vehicle, driver tracking error alone leads to an energy increase of 11.3 % with respect to the advised speed profile, highlighting the influence of traffic on speed tracking capability.

Source: Control Engineering Practice

DOI: 10.1016/j.conengprac.2026.106932

Authors: Vinith Kumar Lakshmanan, Antonio Sciarretta

Institution: IFP Énergies nouvelles

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,010