Research On Estimation Methods For Total Mass Of Commercial Vehicles With Truck

The demand for commercial vehicles in road transportation is increasing because of their low transportation cost and high economic efficiency,but the shortage of drivers and traffic safety has always been a pain point in road transportation.The development,implementation and improvement of the control system requires the vehicle state parameters as input parameters of the algorithm to solve the control problem.When the truck is fully loaded,the vehicle movement has a large impact on the vehicle,and if the vehicle mass can be obtained as the control system input,it can improve the power performance and driving performance of the vehicle.Meanwhile,the vehicle mass information can be uploaded to the traffic management department through the vehicle network to realize the monitoring of transport vehicles and reduce the accident rate.Therefore,obtaining the total vehicle mass information is not only important for vehicle development,but also plays a role in driving safety.In this paper,a more economical method for estimating the vehicle mass is proposed with the help of vehicle longitudinal dynamics related theory,which can achieve dynamic detection of the vehicle mass of commercial vehicles without adding sensors.The CAN(Controller Area Network)bus of the vehicle is used to obtain the driving data of the vehicle,and the data is analyzed and processed to achieve the estimation of the vehicle mass.After the simulation test and real vehicle data.In this paper,the following works were carried out:Based on the knowledge of vehicle dynamics,the correlation between the vehicle load and vehicle dynamics in the driving state is analyzed,and then the vehicle longitudinal dynamics model is established,and the established model is adjusted according to Newton’s second theorem to design the whole vehicle mass estimation algorithm.The vehicle speed,total vehicle mass and road slope are selected as state variables,and the state space equations are established based on the selected variables.Then the vehicle longitudinal dynamics model is processed using the forward Euler method and discretized to obtain the set of differential equations.Based on the established state space equations combined with the extended Kalman filter algorithm,a mass estimator is built,and the vehicle speed and slope are used as observations to iterate and output the mass estimates according to the updated equations.The co-simulation was performed by Trucksim/Simulink,and the built mass estimation model was verified by building a vehicle model and obtaining the vehicle driving parameters for the simulation test by setting different operating conditions.The mass estimation is not good when climbing higher grades,but the mass estimation results are more satisfactory for the rest of the operating conditions.Real-vehicle tests were conducted.Firstly,unknown parameters are calibrated by real vehicle data,and air and rolling resistance coefficients are estimated based on longitudinal dynamics model and recursive least squares method.Then the driving data of different road conditions with different loads and other operating conditions are selected for offline calculation,so as to verify the practicality of the mass estimation model.