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With the rapid development of automobile industry and the accelerated process of urbanization, pollution caused by automobile exhaust had become one of the major sources of urban environment and atmospheric environment pollution. According to the automobile exhaust emission characteristics in this paper, the generation mechanism of major automobile exhaust pollutants which were suspended solid particles, carbon monoxide nitrogen oxides and hydrocarbons, were described, as well as emission control measures with environmental materials.

The traditional SEPIC converter switch is turned on for a long time and as time is short, resulting in loss and temperature rise is too large, etc, isolated SEPIC converter is proposed to analyze the working principle and the transmission efficiency. And the circuit is improved, designed clamp isolated converters, photovoltaic cells based on the characteristics of the circuit used in photovoltaic power generation MPPT (Maximum Power Point Tracking, referred to as MPPT) system. Simulation and experimental results verify its feasibility.

Because the output voltage and current of wind and solar power generation system are nonlinear, PID control has its control of energy storage system to reach the best results. This paper presents a fuzzy control system, which intelligent controlling its energy storage system, and details fuzzy control design process, using Infineon’s XC164 microcontroller for the control of the company's core, designing a wind and solar energy storage system, for fast charging, reliable intelligent control system.

The no.10 coal seam in the first mining area of Yuandian coal mine lies in “Taiyuan Group limestone” aquifer with rich water, the underlying limestone water is an important danger for mine water-filling. To ensure the safety of mining, it is necessary to study and analyze the mining conditions. On this basis, with the design of targeted prevention and treatment of water, we achieve the purpose of safe mining.

In order to improve the performance of fault diagnosis systems based on a wavelet neural network, according to the frequency domain characteristics of the vibration signals of the ball bearings, a diagnosis system which based on the wavelet packet analysis for picking up character and improved wavelet neural network is proposed, the conception of wavelet packet analysis and the basic idea of fault diagnosis of wavelet and neural network are also involved. The energy distributing of each frequency segment which is decomposed by wavelet packet is treated as the eigenvector and input the IWNN, and the recognition of the fault models of the ball bearings is completed by using improved wavelet neural network. The result of test and theory shows that circuit fault can be detected and located quickly by using this method and the training speed of wavelet neural network is dramatically accelerated.