Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

Desreveaux, Anatole; Bouscayrol, Alain; Trigui, Rochdi; Castex, Elodie; Klein, John · 2019 · Crossref

DOI: 10.1109/tvt.2019.2949215

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Summary

This study investigates how velocity profiles, specifically maximal velocity and acceleration, influence the energy consumption of electric vehicles (EVs). The research is motivated by the need to accurately assess EV driving range, a critical factor for adoption, and to evaluate the potential of speed limitations as a strategy for reducing energy use. Standard standardized driving cycles (e.g., NEDC, WLTP) fail to reflect real-world variability, while traditional backward simulation models ignore vehicle physical limitations, leading to inaccurate energy estimates. To address these gaps, the authors developed a forward simulation approach coupling a detailed traction model of a commercial Renault Zoe with a specific driving cycle generator. The vehicle model, organized using Energetic Macroscopic Representation (EMR), accounts for battery dynamics, electric drive efficiency maps, and mechanical transmission losses. Crucially, the system includes a feedback loop: if the vehicle’s torque or speed limitations prevent it from following the generated velocity reference, the generator recalculates the profile to ensure the vehicle reaches its final destination. This method was validated against real-world data from urban, suburban, and highway trips, achieving simulation errors of only 3–5% in energy consumption, significantly outperforming backward approaches which showed errors up to 5% in simple cases and failed to account for torque limitations. The study analyzed the impact of varying maximal velocity and acceleration by ±10% and ±20% on a mixed 19 km commuting trip. Results demonstrate that maximal velocity has a strong, proportional impact on energy consumption. A 20% reduction in maximal velocity decreased energy consumption by approximately 19%, while a 20% increase raised it by 21%. Conversely, variations in maximal acceleration had a negligible effect; a 20% reduction in acceleration limits altered energy consumption by less than 1%. Across different trip types, the impact of velocity was most pronounced in highway scenarios (24% change for ±20% velocity) and least in urban settings (14–16% change), attributed to the quadratic relationship between aerodynamic drag and speed. The findings conclude that velocity limitations are a highly effective lever for managing EV energy consumption and range, whereas acceleration constraints are largely irrelevant for energy efficiency in typical driving conditions. The proposed forward simulation method provides a robust tool for evaluating real-world energy consumption by accurately modeling vehicle limitations and dynamic feedback, offering superior accuracy compared to classical backward models. This approach can be extended to study other constraints, such as road slopes or vehicle mass, aiding in the optimization of EV performance and infrastructure planning.

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• speed choice