Road grade estimation based on Large-scale fuel consumption data of connected vehicles

Road grade is crucial in vehicle control and emission studies but challenging to obtain in largescale road networks due to current methods' expensive deployment costs or limited accuracy. This paper proposed a scale-deployable and cost-efficient road grade estimation solution based on the fuel consumption rate (FCR) difference between flat and graded roads. Real-world road grades from design drawings and 261,814 second-by-second vehicle operating data from 680 light-duty vehicles were collected to examine the proposed method's performance. Sensitivity tests for vehicle types and sample sizes were conducted. Results show that (1) the proposed method acquired road grade with an accuracy of 0.12% mean absolute error (MAE), (2) in positive vehicle specific power (VSP) bins, a 1% road grade caused an average 16% FCR change, and (3) largerscale fuel consumption data contributed to reducing estimation error which converged from 0.25% to 0.12% as the segment passes increased from 50 to 400.

Automated summary

This paper proposes a cost-efficient road grade estimation solution based on the difference in fuel consumption rate between flat and graded roads. The proposed method achieves high accuracy in obtaining road grades in largescale road networks. Validation using real-world road grades and a large dataset of vehicle operating data shows that the method performs well, with a mean absolute error of 0.12%. The inclusion of largerscale fuel consumption data contributes to reducing estimation errors.