Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time

Enze Liu, Zhiyuan Lin, Haibo Chen, Dongyao Jia, Ye Liu, Junhua Guo, Tiezhu Li and Tangjian Wei

Energy, 2025, vol. 324, issue C

Abstract: Optimising driving velocity profiles is crucial for reducing vehicle fuel consumption and NOx emissions without altering core vehicle components. While many studies have addressed eco-driving, most have focused solely on minimising fuel consumption or have treated NOx emissions separately, resulting in distinct, non-integrated speed profiles, and have often neglected the influence of real-time traffic. To overcome these limitations, this paper introduces a novel Multiobjective Speed Profile Optimisation (MO-SPO) framework for eco-driving that simultaneously minimises fuel consumption, NOx emissions, and travel time while accounting for surrounding traffic. Two solution approaches are developed and compared: a two-phase Model Predictive Control (MPC) method and a newly proposed Deep Reinforcement Learning (DRL) method that directly integrates multiple objectives and real-time traffic constraints into the speed control policy.

Keywords: Eco-driving speed profile optimisation; Fuel consumption; NOx emission; Multiobjective optimisation; Model predictive control; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s0360544225014355

DOI: 10.1016/j.energy.2025.135793

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