Remotely detected vehicle mass from engine torque-induced frame twisting
Abstract. Determining the mass of a vehicle from ground-based passive sensor data is important for many traffic safety requirements. This work presents a method for calculating the mass of a vehicle using ground-based video and acoustic measurements. By assuming that no energy is lost in the conversion, the mass of a vehicle can be calculated from the rotational energy generated by the vehicle’s engine and the linear acceleration of the vehicle over a period of time. The amount of rotational energy being output by the vehicle’s engine can be calculated from its torque and angular velocity. This model relates remotely observed, engine torque-induced frame twist to engine torque output using the vehicle’s suspension parameters and engine geometry. The angular velocity of the engine is extracted from the acoustic emission of the engine, and the linear acceleration of the vehicle is calculated by remotely observing the position of the vehicle over time. This method combines these three dynamic signals; engine induced-frame twist, engine angular velocity, and the vehicle’s linear acceleration, and three vehicle specific scalar parameters, into an expression that describes the mass of the vehicle. This method was tested on a semitrailer truck, and the results demonstrate a correlation of 97.7% between calculated and true vehicle mass.
Non-Contact vehicle Weigh-in-Motion using computer vision
Abstract Heavy vehicles must be closely monitored to prevent fatigue-induced deterioration and critical fractures to transportation infrastructure. This paper introduces an innovative computer vision-based non-contact vehicle weigh-in-motion method and presents a proof-of-concept study. The method is based on simple physics that tire-roadway contact force equals the contact pressure multiplied by the contact area. The area can be estimated by measuring tire deformation parameters such as tire-roadway contact length and tire vertical deflection using computer vision, while tire pressure can be streamed from on-board sensors. To demonstrate the feasibility of this method, a computer vision system was developed and experiments were conducted involving empty and fully loaded concrete trucks and a sport utility vehicle. Results show high accuracy of vehicle weight estimation, demonstrating the potential of this computer vision-based method as a non-contact means for weighing vehicles in motion.
Efficiency of the planetary gear hybrid powertrain
Abstract The planetary gear hybrid powertrain (PGHP) is known as one of the most efficient configurations for hybrid electric vehicles. The system controls the speed and torque of each component so that it can produce adequate input and output speeds for the optimal operation of the total system. In this paper, the system mechanism is analysed to understand the relation between the input and output speeds. In addition, the transmission efficiency is expressed as a function of three parameters based on the calculation of the net output power. The characteristics of the transmission system showed a large difference from those of a conventional CVT. The speed ratio was the key parameter that affected the efficiency. Furthermore, the performances of the electric motor and generator were decisive factors owing to the loss in the energy conversion. In a simulation on an urban driving schedule, the optimal operation of the engine was able to be obtained, and the system showed an improvement of 2.2 per cent in fuel economy.