The oil-line electrostatic sensor (OLS) is a developing debris monitoring sensor. Previous work has shown that electrostatic charge signals can indicate the debris by calculating the Root Mean Square (RMS) value or the correlation-based indicator, but the precision of these methods is not high. This paper further developed the more accurate methods to obtain detailed debris information. Firstly, to interpret the monitoring principle of OLS and provide the guidance for developing the debris recognition methods, this paper analyzed the possible charge sources in the lubrication system and obtained the characteristics of the OLS by establishing its mathematical model. Further, a new OLS test rig was designed and verified the correctness of the sensor’s characteristics and its mathematical model. Based on the characteristics of the sensor, two new debris recognition methods were proposed. Finally, the effects of the new debris recognition methods were verified by the practical industrial gearbox bench test. Results showed that, compared to the traditional methods, the new methods could recognize the debris effectively and provide more detailed information of the debris.
Ackermann steering is important for the steering performance of heavy multi-axle vehicle. When Ackermann steering condition is not satisfied, it will lead to abnormal tire wear. Traditional trapezoidal mechanism of heavy multi-axle vehicle is a single degree of freedom (DOF) mechanism, which is difficult to completely realize Ackermann steering. In this paper, a new two DOF electro-hydraulic servo steering system (TDEHSSS) by using a variable length tie rod is proposed for solving the issue. First, a complex nonlinear dynamic model of TDEHSSS is established. This model includes the two DOF mechanical model based on a Lagrange equation, the valve-controlled double steering power cylinders model and the valve-controlled tie rod cylinder model. Then, a simulation model is built through MATLAB/Simulink and the simulation results show that TDEHSSS can realize the proposed requirement. At last, a test bench is founded to verify model. It is indicated that the simulation and experimental curves are consistent, showing that mathematical model is in accordance with the experimental system. This research is valuable for analyzing TDEHSSS, designing advanced controllers, and finally realizing Ackermann steering for heavy multi-axle vehicles.
This paper is an introduction of terramechanics and its application in off-road vehicle mobility prediction. Firstly the concept of terramechanics is introduced. Terramechanics is an important branch of applied mechanics in which the interaction between the vehicle and its operating terrain is studied. Secondly, many well-known techniques in terramechanics, such as Cone Penetrometer technique, Bevameter technique are introduced. By comparing the RCI of soil with VCI of vehicle, trafficability of Off-road vehicle can be determined which predicts the "go/no go" property of vehicle. Furthermore, to predict the speed behavior of off-road vehicle over trafficable terrain, three types of analyzing methods, empirical, semi-empirical, computer-aided, are presented in detail. It is evident that computeraided method is much more efficient in simulating terrain-vehicle interaction and produces more realistic prediction in vehicle mobility. Therefore, it is meaningful to develop computer-aided method to provide engineers with powerful parametric analysis in vehicle performance and design.
With the mass roll-out of electric vehicles (EVs) and rapid progress in battery technology, utilizing EV charging flexibility has become a promising solution for supporting economic and secured power system operations. This work proposes a novel hybrid incentive program, which encourages EV owners to sell their charging flexibility to a charging station (CS) and achieve a win-win situation for both EV owners and the CS. Unlike existing approaches, the proposed hybrid incentive program is simultaneously featured with simplicity, consistency, and controllability. To determine the incentive payment parameters, an optimal incentive price selection model is developed. In the solution methodology, we first linearize the original problem, then develop an adaptive ADMM algorithm to efficiently solve the formulated problem. Case studies confirm the superiority of the proposed hybrid incentive program over the state-of-the-arts, achieving 22.51% of EV owners' cost reduction, 31.18% of energy market bill reduction, and 64.13% of potential charging flexibility utilization.
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