Research On Iterative Optimization Control Of Autonomous Vehicle For Eco-Driving

With the rapid development of the automobile industry,the continuous growth of automobile ownership facilitates People’s Daily travel and cargo transportation,bringing huge economic benefits,but also causes a series of problems such as frequent traffic accidents,road congestion,emission pollution,energy shortage and so on.Autonomous driving integrates on-board sensing,wireless communication,computer control,navigation and positioning technologies,and based on rational control strategies to enable the vehicle to travel in a more intelligent,safer and more energy-efficient method.Among the current decision control algorithms used for vehicles,the global optimization algorithm can obtain the global optimal solution in most cases,but it is difficult to be applied in practice due to excessive computing burden,while the local optimization algorithm has the disadvantage of insufficient optimization ability.Therefore,it is very challenging to realize optimal decision control in complex environment.Iterative Optimization-based Predictive Control(IOPC)is a trajectory optimization method for iterative tasks in nonlinear dynamical systems.Data driven terminal state constraint set and terminal cost function are used to ensure the comprehensive performance of controlled objects.IOPC can realize the optimal control of the system through multiple iterations,but its current application has the disadvantage of low iterative efficiency.Therefore,this paper adopts the combination of Dynamic Programming(DP)and IOPC to improve its iterative efficiency,and further optimize the vehicle performance.This paper realizes the minimum time control and eco-driving of autonomous vehicles.The main work completed in this paper and the research results obtained are as follows:(1)Firstly,a three-degree-of-freedom bicycle dynamic model is established,and its dynamic equations applied to the longitudinal and lateral control scenarios are derived.Then,the vehicle model based on road errors is obtained by further transformation in Frenet coordinate system.Secondly,a vehicle longitudinal dynamics model applied to eco-driving of heavy vehicles is established,and the reduction of wind resistance coefficient of following vehicles is estimated by data fitting to construct a heavy vehicle platoon model.A six-term fuel consumption model based on torque and speed is fitted with engine fuel consumption data to estimate vehicle fuel consumption.The iterative optimization problem is modeled,and we describe the details of how the terminal state constraint set and terminal cost functions are constructed,as well as the process of IOPC iterative optimization.(2)A time-optimal iterative optimization controller for autonomous vehicle is designed based on IOPC.The local terminal state constraint set and terminal time function are built by historical data,which are constructed as convex set to expand the optimization space.The terminal state constraint set and the terminal time function guarantee the stability of the vehicle and the non-increment of the travel time in the iterative process.The IOPC is verified by Car Sim,Matlab/Simulink co-simulation platform and ROS real vehicle control platform respectively.Simulation test and real vehicle test results show that IOPC can effectively reduce the time of vehicles passing through the road,and selecting a longer prediction domain and a better initial trajectory can greatly improve the iteration efficiency of IOPC.(3)A data-driven speed trajectory iterative optimization method combining DP and IOPC for eco-driving of heavy vehicle platoon is proposed.Firstly,the initial economic velocity curve of the platoon is obtained by DP,which provides an initial feasible solution for IOPC iterations.By learning the correlation between vehicle state trajectories and fuel consumption from historical state data,IOPC continuously optimizes the vehicle fuel consumption while meeting the total time limit and real-time calculation requirements.Simulation results show that IOPC can achieve iterative optimization of the platoon fuel economy on hilly roads.After multiple iterations,the platoon fuel consumption decreases by approximately 16.7% compared to MPC-based cruise control.Besides,using the results of DP as the initial trajectory,the velocity trajectory iterative optimization method can greatly improve the iteration efficiency and reduce the number of iterations,and the energy saving effect is further increased by2.5-3% compared with DP.