A fine-grained predictive optimization framework for dynamic eco-routing of electric vehicles
Published in Computers & Industrial Engineering, 2026
As electric vehicle (EV) adoption grows globally, optimizing energy consumption during travel is essential for extending range and improving urban sustainability. This paper presents a novel eco-routing framework, Predictive Optimization for Eco-Routing of Electric Vehicles (POE-EV), which is designed with a two-step approach: first, a deep learning model is developed to predict fine-grained energy consumption across road segments, accounting for complex spatiotemporal driving conditions. Next, Dijkstra’s algorithm generates high-quality initial routes based on these predictions. These routes are refined through a Genetic Algorithm (GA), optimizing the balance between minimizing travel time and reducing energy consumption. This sequential process of prediction followed by optimization provides key advantages, with Dijkstra’s algorithm offering efficient path generation and GA exploring multiple solutions for …
Recommended citation: Q Liu, Y Li, M Liang*, R Gao, J Zhang, Y Liu. (2026). A fine-grained predictive optimization framework for dynamic eco-routing of electric vehicles. Computers & Industrial Engineering, 111662. https://www.sciencedirect.com/science/article/pii/S0360835225008083