Research On TTC Model Control Strategy In Commercial Vehicle Air Pressure AEB System

As an important means of transportation,commercial vehicles are deeply involved in the economic life of the national society.90% of accidents in traffic accidents are accidents caused by traffic operators' misoperation or non-operation.Collision accidents account for 70%.Most of the collision accidents are rear-end collisions,and the traffic accidents caused by vehicle failures account for a small proportion.In the event of a traffic accident in a commercial vehicle,it is a major traffic accident,causing significant loss of personnel and property.The AEB system can reduce the speed of the collision or avoid collisions.In this paper,the commercial vehicle pneumatic brake system for AEB is taken as the research object.Starting from the commercial vehicle pneumatic active system control strategy,the commercial vehicle pneumatic brake system is simulated and modeled,and the commercial vehicle pneumatic brake system brake for AEB system is proposed.The control strategy is to improve the TTC model control strategy of the commercial vehicle AEB system,and verify the effectiveness of the optimized TTC model control strategy in the hardware-in-the-loop test bench.(1)The research analyzes the composition of the brake delay of the air brake system,and establishes the brake response valve response delay,the pneumatic loop response delay model,the pneumatic actuator action delay model and the integrated pneumatic brake system brake delay model and verify.Analyze the emergency braking conditions of commercial vehicles and establish models such as tires and brake models.The fuzzy controller is established in MATLAB/Simulink according to the fuzzy control theory.The simulation experiment is carried out in IPG/TruckMaker,and the effectiveness of fuzzy control in the air brake control strategy considering brake delay is verified.(2)BP neural network algorithm is used to predict the braking distance of commercial vehicle emergency braking process considering the air brake delay and fuzzy control braking control strategy.It is applied to the TTC model to obtain the optimal TTC of the braking distance prediction model based on BP neural network.Model control strategy and comparison with conventional AEB early warning algorithms.Under the emergency braking condition of commercial vehicles,the TTC model control strategy is optimized to improve the braking accuracy of the AEB system and improve the safety of the AEB system.(3)This section introduces the structure of HIL hardware in ring test bench and AEB system test evaluation standards,and describes AEB system test methods of ECE R131 standard in detail.Completed the ECE R131 standard AEB system test method of HIL hardware in the loop simulation test,compared with the conventional AEB system control strategy and optimal TTC model control strategy,proposed AEB system performance evaluation system based on analytic hierarchy process(ahp),verified that the optimization of AEB system control strategy can improve the braking accuracy of AEB system.