Estimation of vehicle and load conditions of a truck for fatigue estimation using real time numerical algorithms

The thesis provides the infrastructure for intelligent vehicles, and in an ARDEB project, it has been studied to provide information on the mass of the vehicle and the parameters of the vehicle which will determine the amount of fatigue on the relevant parts of the vehicles. Intelligent vehicles are generally studied for passenger vehicles. Vehicle and cargo estimates have been worked on this work for 6-wheel heavy trucks. Furthermore, when the instrumentation costs for estimating real-time vehicle parameters are high, this study could create a low-cost estimation system for experimental conditions. In this study, we focused on real time estimation of load conditions and vehicle parameters for 6x4 heavy-duty vehicles. It is known that vehicle dynamics are important parameters for vehicle vibration. Main aim is required accurate information to sensitive results. We have to know vehicle dynamics parameters e.g. center of gravity vertical displacement, roll and pitch angle. In this study it is considered that the vehicle is a 3-axle, 6-wheel, 4-drive wheel, and heavy-duty truck with leaf spring suspensions. When all this is calculated, our motion point is the basis of vehicle dynamics and vibration information. It is known that when the vehicle is in motion an oscillation will occur in all axle-wheels. If the oscillation information is obtained at 6 points of the vehicle, serious data about the general condition of the vehicle is provided. These oscillations will occur against the overall rigidity of the vehicle. Said oscillations are the displacement quantities of the suspensions. A total of 6 accelerometers are placed on each of the 3 axles to determine these displacement quantities. The first axle is connected to the midpoint of the front scissor suspension, while the second and third axles are connected to the end points of the rear scissor suspension. The acceleration data coming from the axle heads will be related to the acceleration data obtained in the other part of the project, and two integrations at the time base will provide the suspension displacement information. Also, our heavy-duty vehicle has three axles and six tires. We need to collect suspension deflection for each tire. But our type of trucks has leaf spring suspension due to heavy duties. We know that leaf spring suspension has not potentiometer to get deflection information. To get the deflection information, we are going to acquire acceleration from on each end of each axle via transducers. And acceleration information has double numerical integration. At this result of the calculation, the displacement data is obtained. Also, we have acceleration data from leaf spring located on chassis point from another project level. Thanks to this information, we could calculate the suspension deflection. Energy equations are obtained by using Lagrange energy method and then motion equations are obtained. These motion equations are written in matrix form. Time Discritization – Numerical Integration and Kalman filter are used to created algorithm. Other solution algorithm is written using Newmark method. Simulink model is also created. And these solutions are compared to each other. We build Adams/Car Model for our 6x4 heavy duty truck. Adams/Car presents different analysis opportunities to estimate different vehicle variation. We need to accelerations and displacements of hub simultaneous road to correlation our algorithm. Because, we have not simultaneous hub and suspension deflection information. Due to that problem, we need to create our sanal data to could feed to Simulink model and algorithm.