Research On Vehicle Multi-Objective Adaptive Cruise Control Algorithm

In recent years,to better coordinate the conflicting goals,such as safety,following,comfort and fuel economy,such as adaptive cruise control system arises at the historic moment,its comprehensive coordination conflicting goals,in the process of driving better accepted by the masses,become the popular intelligent auxiliary driving system.In this paper,a multi-objective adaptive cruise control system is designed by using layered control,and its spacing strategy,upper controller and lower controller are studied.A co-simulation environment based on Carsim&Simulink is built to verify the effectiveness of the algorithm.The main content includes the following aspects:To improve the safety and following performance of the adaptive cruise control system when the vehicle in front of you is slowing down,the strategy of variable time headway spacing is studied.In view of the fact that the current variable time headway strategy cannot meet the complex and changeable decelerating driving environment,an improved variable time headway(VTH)spacing strategy is proposed which changes with the deceleration duration and deceleration of the leading vehicle,and when the deceleration reaches a certain value,the upper limit of the saturation function is removed.The numerical simulation shows that the improved spacing strategy can effectively improve the safety and car-following performance of the adaptive cruise control system when the leading vehicle decelerates.The longitudinal kinematics model of the workshop was established.According to model predictive control theory,the objective function of the performance indicators such as safety,following,comfort and fuel economy and the constraint conditions of the physical performance of the vehicle were designed.A relaxation factor vector is introduced to soften the hard constraint boundary to solve the problem of no row solution.In the process of rolling optimization,an improved particle swarm optimization algorithm with the ability to solve multiple constraint problems is introduced to solve the constrained objective function.Through numerical simulation analysis,the results show that the multi-objective adaptive cruise control algorithm based on the improved particle swarm optimization algorithm can effectively improve fuel economy and driving comfort.Firstly,the torque characteristic curve of the hydraulic torque converter and the MAP of the engine were obtained based on a B-class passenger car model in CarSim,and the two-dimensional table looking up module of the reverse engine model was obtained from the MAP.Then,the expected throttle opening was obtained according to the engine speed and the expected output torque.Secondly,the calculation model of expected braking pressure was obtained by force analysis of the vehicle.To ensure the efficiency and stability of system switching,the switching curve between throttle control and braking control was constructed.Finally,based on the PID algorithm as the lower control algorithm,the function of stably following the expected acceleration calculated by the upper controller is realized.CarSim software was used to build the vehicle model,road environment,sensors and other experimental Settings.The upper controller,the lower controller,the switching strategy,the throttle controller and the braking force controller are built in Simulink.In order to verify the effectiveness of the algorithm,ECE+EUDC conditions were selected for co-simulation.