How to Reduce Range Anxiety? The Impact of Digital Elevation Model Quality on Energy Estimates for Electric Vehicles

Graser, Anita; Asamer, Johannes; Dragaschnig, Melitta · 2015 · OpenAlex-citations

DOI: 10.1553/giscience2014s165

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study addresses the challenge of "range anxiety" in electric vehicle (EV) adoption by evaluating how the quality of Digital Elevation Models (DEMs) impacts the accuracy of energy consumption estimates. Reliable prediction of energy requirements is critical for EV route planning, and elevation data is a primary factor in these calculations. The authors aim to quantify the errors introduced by using lower-resolution, open-source DEMs compared to high-resolution local data, specifically within the context of the city of Vienna. The researchers compared three DEM datasets: NASA’s SRTM version 3.0 (approx. 90m resolution), the EU-DEM (25m resolution), and the City of Vienna’s open government DEM (5m resolution, referred to as Wien-DEM). They generated 16,500 random routes across Vienna’s street network, ensuring a minimum length of 800 meters. Energy consumption was calculated using a longitudinal dynamics model that accounts for acceleration, rolling resistance, wind resistance, and elevation changes, including energy recuperation during downhill segments. The Wien-DEM served as the reference standard due to its superior resolution and exclusion of artificial structures. The analysis assumed a constant urban speed of 35 km/h to isolate the impact of elevation data. The results demonstrate that lower-resolution DEMs systematically overestimate energy consumption. Compared to the Wien-DEM baseline, the EU-DEM produced a mean error of +2.9% (0.38 kWh per 100 km), while the SRTM3.0 yielded a significantly higher mean error of +15.8% (2.06 kWh per 100 km). The SRTM3.0 data exhibited the largest deviations, with individual route errors reaching up to 263%. Spatial analysis revealed that these errors were not uniform; the highest discrepancies occurred in the hilly north-western regions and the dense city center. The authors attribute this overestimation to the "smoothing" effect of high-resolution data, which captures the moderate slopes of actual roads, whereas lower-resolution models introduce artificial, abrupt elevation changes that increase calculated energy demand. The study concludes that high-resolution elevation data is essential for accurate EV energy modeling, not only in mountainous terrain but also in dense urban environments. For regions in Europe lacking high-resolution local data, the authors recommend using the EU-DEM over SRTM data, though users should account for a tendency toward overestimation. The findings highlight that DEM quality is a critical variable in reducing range anxiety and improving the reliability of eco-routing algorithms for electric vehicles.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• exposure measurement