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

Abstract

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.

LiRA-CD: An Open-Source Dataset for Road Condition Modelling and Research

“…In both cases, the P79 measurements were used as a reference. In [9] and [10] new road energy efficiency monitoring concepts are proposed based on vehicle speed, longitudinal acceleration, wheel torque, and traction power measurements. Overview maps of the different routes in LiRA-CD are depicted in Fig.…”

“…• The LiRA-CD provides the basis for development of road condition prediction models suitable for wide-area implementation, i.e., models for prediction of surface friction and texture, road roughness, road damages, and energy consumption (see e.g., [5][6][7][8][9][10] ), and could be useful for road operators and owners, such as road agencies and municipalities. • The data is suitable for developing new interpretation schemes (e.g., utilizing physical models) and machine-learning algorithms.…”