Predictive Cruise Control Of Heavy-Duty Truck Based On Road Information

With the rapid development of the logistics and freight industry and e-commerce industry,the commercial vehicle industry has also continued to develop at a high speed,and it does not account for a high proportion of the total vehicle ownership.The total fuel consumption of heavyduty trucks and the fuel consumption of the remaining passenger vehicles are roughly the same.So the energy consumption and environmental protection caused by commercial vehicles are becoming increasingly severe.In order to reduce the fuel consumption of commercial vehicles,this article mainly researches cruise control technology for heavy-duty trucks.It aims to use road information to predict the future road slope in advance and integrate it into the cruise control system.The optimization control algorithm is used to optimize engine torque and gear position to maximize the fuel economy of heavy-duty trucks while ensuring speed-tracking performance.This article first designs the overall control scheme of the predictive cruise control system,establishes the control architecture of the system,and clarifies the function and realization of each module in the control architecture.Then complete the establishment of vehicle longitudinal dynamics model and engine fuel consumption rate model in traction mode and braking mode.Aiming at the 12-speed electronically controlled mechanical automatic transmission of heavy-duty truck discussed in this article,a shifting model of heavy-duty truck is established.Then,a multiobjective optimization function is established for the cruise speed optimization problem,and the constraint conditions of the control problem are determined,which lays the foundation for the solution of the subsequent optimization function.In order to obtain the global optimal solution of the established multi-objective optimization control problem in the target section,an optimal control based on dynamic programming algorithm is proposed.First,the predictive cruise control system is discretized,and the target optimal control problem is converted into a multi-stage decision-making problem for solution.The state variables are discretely divided to form a solution grid,and the target road section is divided equally to solve the discrete step length,and then by comparing different networks The simulation results of grid density and step size reasonably select the density and step size of the grid discretization,so as to take into account the solution speed and optimization effect.Finally,the dynamic programming algorithm is numerically solved according to the Bellman optimality principle to realize the multiobjective optimization problem.Global optimal control.A multi-objective optimal control strategy and fast solving algorithm based on model predictive control are proposed.The solution of the model predictive control algorithm is realized based on the principle of Pondriakin’s minimum value,and a fast solution algorithm is proposed.Through the introduction of comorphic variables,the multi-objective optimization problem is transformed into a two-point boundary value problem,and then the two-point boundary value problem is solved iteratively.For point-boundary value problems,the fill function method(dichotomy)is used to iteratively solve the optimal initial comorphic variables,so as to obtain the optimal control sequence and the optimal state sequence according to the solution equation of the optimal control variable,and finally realize the predictive cruise control optimization problem The fast solution.In order to verify the superiority of the predictive cruise control algorithm that integrates road slope information proposed in this paper,the vehicle dynamics simulation software Trucksim and the dynamic system simulation software MATLAB/Simulink are selected to conduct a joint simulation test on the developed predictive cruise control system.First,in Trucksim Establish vehicle system model and road environment model,build predictive cruise controllers based on DP algorithm,MPC algorithm and PID algorithm in Simulink,and then carry out Trucksim and Simulink joint simulation experiments under virtual road simulation conditions and real road simulation conditions Finally,the experimental results of different control algorithms are analyzed and compared: The predictive cruise control system based on the MPC algorithm and the DP algorithm proposed in this paper can save 4～5% fuel on the real sloped road section compared with the adaptive cruise control system based on the PID algorithm.But the driving time did not change significantly.The optimization results based on the MPC algorithm are very similar to the global optimization results of the DP algorithm,but the solution time of the MPC algorithm using the fast solving algorithm is much shorter than that of the DP algorithm,and it can achieve realtime control of the actual vehicle.