Global Sensitivity of Roughness-Induced Fuel Consumption to Road Surface Parameters and Car Dynamic Characteristics

Abstract

Pavement roughness is one of the key contributors to rolling resistance and thus vehicle fuel consumption. Roughness-induced fuel consumption is the result of energy dissipation in the suspension system of vehicles and therefore depends on both road surface characteristics and vehicle dynamic properties. In this paper, the sensitivity of roughness-induced excess fuel consumption to all involving factors, i.e., road roughness metrics, vehicle dynamic properties, and speed is investigated, and the dominant factors affecting fuel consumption are identified. This is achieved by using the Sobol’s method—a robust analysis of variance (ANOVA)-based technique for global sensitivity analysis. To this end, Monte-Carlo (MC) simulation is performed by generating realizations of all input parameters according to their probability distributions and estimating the energy consumption via a mechanistic roughness model. The results of the simulation are then used to obtain global Sobol sensitivity indices. Finally, the comparison between the Sobol sensitivity indices and the previously employed indices based on Spearman Rank Correlation Coefficient (SRCC) is illustrated. It is found that roughness metrics, i.e. the International Roughness Index (IRI) and the waviness number, account for 88–93% of the total variations in energy dissipation and are the most influential factors affecting the excess fuel consumption. It is also observed that among vehicle dynamic properties, the stiffness of tire is the most important parameter accounting for 2–7% of the total variance of the excess energy consumption.

Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory

“…This is shown in Figure 10 in form of the cumulative specific energy dissipation (Figure 10c), and the specific energy dissipation vs. IRI for the two test tracks ( Figure 10d). The results confirm previous findings that the dissipated energy is chiefly governed by the surface quality of the road (Botshekan et al, 2019). The slope of the accumulated specific energy (Figure 10c) scales with a power exponent of respectively 1.5 for the inner city (test track 1) and 0.5 for the highway (test track 2).…”

“…The PDF of such change is shown in Figure 11d, considering a length window of 10 km, moving with a 1 km length-step through the network. At the scale of the two networks, the results readily confirm that the dissipated energy is governed by the surface quality of the road (Botshekan et al, 2019), as the difference in the PDFs in Figure 11c mirror-images the CDFs of IRI in Figure 11b. In addition, the PDF of the change in energy dissipation with IRI in Figure 11d provides a snapshot of the type of vehicles measuring the road condition in a crowdsourced fashion.…”

“…The time-domain integration includes both transient and steady-state responses. The transient part, however, decays exponentially in strongly damped systems such as the GC model, converging quickly to the steadystate solution captured by the inverse Fourier transform approach (Botshekan et al, 2019). Otherwise said, a large enough length window, L w , ensures the compatibility of time and frequency approaches in determining IRI.…”

Smartphone-enabled road condition monitoring: from accelerations to road roughness and excess energy dissipation

“…previous findings from a global sensitivity analysis of roughness-induced dissipated energy [46] and serve as further confirmation of the 'transferability' of the proposed framework.…”

“…We note that the frequency domain approach does not capture the transient response. However, in highly damped systems, such as the golden car, this only impacts the result at the very beginning [46]. To calculate the IRI in a statistical sense, through equation (2.9), we divide the road profile into N overlapping segments with fixed length of L w = 900 m, where the i th segment is represented by its centre l i .…”