The Study On Active Noise Control Of Hybrid Vehicle Engine Exhaust System

With the rapid development of automobile industry and the deepening of environmental protection concept, new energy vehicles are well welcomed by consumers, hybrid vehicles are emerging in the new energy vehicles because of its many advantages. At the same time as people focus on vehicle ride comfort, automobile research and exploitation pay more attention to the vehicle’s NVH performance. The performances of vehicle exhaust system have important influence on the overall performance of the vehicle. Noise is one of the important indicators of automobile pollution to the environment. Noise control methods are classified into active control and passive control. At present, passive control method is mostly applied to the noise control of the exhaust system, mainly for the high frequency noise control. But the noise control effect is not very good for the low frequency. The active noise control method has very good silencing effect for low frequency noise, it can solve the low frequency part of automobile exhaust noise. In active noise control system, the selection of control algorithm directly affects the noise control effect. The more widely used control algorithm which has a small amount of calculation, simple and easy to implement is the control algorithm based on the theory of linear adaptive filter, such as the LMS algorithm. But when it came to solve the nonlinear complex problem, a linear control algorithm cannot reflect its advantages, then it need to apply the nonlinear algorithm to deal with the problem. Due to the research of high frequency part of the control in the automotive exhaust system noise is comparatively mature, this paper studies low frequency part of the automobile exhaust system noise control, the nonlinear control algorithm is used to substitute the LMS algorithm which has been widely used.The BP neural network algorithm has strong ability of practical application in solving nonlinear problems. But with the deepening of the research and application of BP algorithm,it also shows some defects,such as low convergence precision accuracy, easy to fall into local minimum value, sensitive to the initial setting of weights and so on. In this paper, the genetic algorithm with the characteristics of global optimization and high convergence precision is applied to determine the initial time of BP network weights and threshold. GA- BP algorithm was put forward.The active noise control model of engine exhaust system was established in this paper, on the basis ofanalysis of the generation mechanism and frequency distribution of engine exhaust, conducted a simulation experiment by MATLAB/Simulink, and compared the result with which used LMS algorithm、BP network and GA-BP network. The results show, for nonlinear system, neural network was better than LMS algorithm, the GA-BP network is better than BP network. The power spectrum of the simulation results was plotted in MATLAB, it could be seen that because of GA-BP neural network algorithm proposed in this paper, noise signal got an offset over a wide frequency range. In order to verify the effectiveness of the algorithm proposed in this paper, a measurement of a hybrid vehicle exhaust noise was conducted, and achieved a good effect. Furth suggested that the active control method based on noliner algorithm could better eliminate automotive exhaust noise, it had a certain practical ability, and provides certain reference to The research of automobile exhaust noise active control.