Method for Direct Measurement of Structural Rolling Resistance for Heavy Vehicles

Nielsen, Natasja Ringsing; Chatti, Karim; Nielsen, Christoffer Peder; Zaabar, Imen; Hjorth, Poul G.; Hecksher, Tina

doi:10.1177/0361198120915699

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…In the work of [48], a combined roughness and displacement energy dissipation model was utilized, showing that the influence of concrete pavement stiffness on energy consumption is negligible. Similar findings are reported for asphalt roads [49].…”

Section: Related Research Worksupporting

confidence: 89%

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

Skar

Vestergaard

Pour

et al. 2023

Sensors

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The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure’s impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle’s primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

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Section: Related Research Worksupporting

confidence: 89%

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

Skar

Vestergaard

Pour

et al. 2023

Sensors

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“…TSD is primarily capable of evaluating the pavement conditions at the network level in regard to structural capacities ( 76 ). Also, TSD can be used for direct measurement of SRR on a heavy vehicle axle ( 108 ). Indeed, the deflection slope measured by the Doppler lasers can be used to estimate the upward slope perceived by the wheel by doing an interpolation between the two nearest sensors of the tire ( 108 ).…”

Section: In Situ Measurementsmentioning

confidence: 99%

“…Also, TSD can be used for direct measurement of SRR on a heavy vehicle axle ( 108 ). Indeed, the deflection slope measured by the Doppler lasers can be used to estimate the upward slope perceived by the wheel by doing an interpolation between the two nearest sensors of the tire ( 108 ). With this method, the experimental SRR coefficient was found to be in the range of 0.005%–0.05% ( 108 ).…”

Section: In Situ Measurementsmentioning

confidence: 99%

“…Indeed, the deflection slope measured by the Doppler lasers can be used to estimate the upward slope perceived by the wheel by doing an interpolation between the two nearest sensors of the tire ( 108 ). With this method, the experimental SRR coefficient was found to be in the range of 0.005%–0.05% ( 108 ). TSD measurements on a fully loaded 10-ton axle at a speed between 50 and 60 km/h were made on a 10 km AC road section and it was found that the SRR coefficient increased on average by 59% when the temperature increased from 18°C to 35°C ( 122 ).…”

Section: In Situ Measurementsmentioning

confidence: 99%

“…There are many approaches in the literature to measure with an in situ approach the pavement contribution to RR or overall RR under real driving conditions, such as coast-down tests (106), driving torque measurements (107), instantaneous fuel consumption measurements (39), accelerometers on vehicle suspensions (22), the TSD (108), and an instrumented/modified trailer (109). The latter method is typically used for car tires (110), but the general trends obtained from this method are likely similar for heavy vehicle tires.…”

Section: In Situ Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Pavement Characteristics on Rolling Resistance of Heavy Vehicles: A Literature Review

Levesque

Bégin‐Drolet

Lépine

2023

Transportation Research Record: Journal of the Transportation R

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The effects of pavement characteristics on rolling resistance of heavy vehicles have gained more interest in recent years. Rolling resistance is the result of the combination of independent (but sometimes correlated) physical phenomena that dissipate energy, which can be regrouped under three different main themes. Road roughness (wavelengths between 0.5 and 50 m) causes movements in vehicle suspensions, which dissipate energy. Pavement macrotexture (wavelengths between 0.5 and 50 mm) creates additional viscoelastic deformations on tire treads. The viscoelastic behavior of the flexible pavement structure, which is referred to as structure-induced rolling resistance, is responsible for a perpetual upward slope perceived by heavy vehicle tires. Secondary aspects can also affect rolling resistance, such as road wetness and snow. This paper addresses each of these three main phenomena from three angles of analysis: (1) theoretical modeling, (2) laboratory experiments, and (3) in situ measurements. The literature on road roughness and structure-induced rolling resistance modeling is extensive compared to macrotexture-effect modeling, as the underlying physical mechanisms are still not well understood. There is, however, strong experimental evidence that the pavement macrotexture can significantly affect rolling resistance, but these studies are mostly related to cars. There are many in situ approaches, but the results are usually based on an indirect method and the different studies are difficult to compare and sometimes inconsistent. It appears that the bottleneck of scientific research on this topic is the fundamental inability to measure the rolling resistance of heavy vehicles with a direct in situ approach under real driving conditions.

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