Development of a low-power weigh-in-motion system using cylindrical piezoelectric elements

This paper presents the development of a novel low power weigh-in-motion (WIM) system that uses cylindrical piezoelectric (PZT) elements for the dual purpose of sensing axle loads and harvesting mechanical energy for its operation. It provides details on the characterization the PZT sensing elements, the conditioning of their signals and describes the algorithms developed for determining speed, axle load and vehicle classification. These algorithms were coded in MATLAB & REG; and converted to C in a format suitable for installing in a low power microcontroller unit (MCU). The system has the capabilities of monitoring vehicle speed, number of axles, axle spacing, axle loads and vehicle classification. It was tested in the laboratory by applying a range of loads and loading frequencies through a servo-hydraulic loading system. The results suggest sufficient accuracy and precision in measuring vehicle speeds, axle loads and determining vehicle class. Its low power requirements provide an inexpensive and sustainable method for obtaining roadway traffic data.& COPY; 2021 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

This paper presents the development of a novel low power weigh-in-motion (WIM) system that utilizes cylindrical piezoelectric (PZT) elements for sensing axle loads and harvesting mechanical energy for its operation. The system is capable of monitoring various parameters such as vehicle speed, number of axles, axle spacing, axle loads, and vehicle classification. The algorithms developed for speed determination, axle load measurement, and vehicle classification were implemented using MATLAB & REG; and converted to C for installation in a low power microcontroller unit (MCU). Laboratory testing demonstrated the system's accuracy and precision in measuring vehicle speeds, axle loads, and determining vehicle class. Its low power requirements make it a cost-effective and sustainable method for obtaining roadway traffic data.