Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse-fine-grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence....
The coordinated control method of Unmanned Electric Formula Racing (UEFC) was studied to improve the handling stability of UEFC. The UEFC’s mechanical structure, which is based on the driving system and transmission system, was designed. In accordance with mechanical structure of the designed racing car, a seven-degree of freedom mathematical model of the UEFC was established. In accordance with the built mathematical model of racing car, the lateral controller of racing car was designed by using a fuzzy neural network method. The longitudinal controller of the racing car was designed by using the method of incremental PID control, and the coordination controller of the racing car was designed by combining the lateral controller and the longitudinal controller so as to realize the lateral and longitudinal coordination control of the UEFC. The experimental results showed that the output parameters such as yaw rate, vehicle speed and heading angle were slightly different from the expected output. It was confirmed that the research method can enhance the handling stability of the UEFC....
In order to solve the problems of low power factor and large harmonic pollution of some electrical equipment connected to the power grid, such as electric vehicle charger system, the author proposes a high-performance control simulation study of a PFC converter for electric vehicle charger. Using the staggered parallel boost power factor correction circuit topology of electric vehicle chargers as the front stage, its high power factor and low harmonic current characteristics can reduce the pollution to the power grid, and the detailed design process and loss analysis of the circuit are given. Through the digital control method and hardware optimization design, the loss is reduced, and the conversion efficiency of the power factor correction converter in the full power range is high, which meets the efficiency requirements of the platinum version and achieves the goal of energy saving and environmental protection. The test results show that the actual efficiency of the experimental prototype is 97.43%, 97.55%, and 97.36%, which are far higher than the efficiency requirements of the platinum version. Conclusion. The high-performance control of the PFC converter of the electric vehicle charger has certain guiding significance for the application in the electric vehicle....
Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand....
Within the presented research study we want to estimate the State of Health (SOH) of a fleet of electric vehicles solely using field data. This information may not only help operators during mission planning, but it can reveal causes of accelerated aging. For this purpose, we use a customized neural network that is able to process the data of all fleet vehicles simultaneously. Thus, information between batteries of the different vehicles is transferred and the extrapolation properties are enhanced. We firstly show results with data gathered from a fleet of 25 electric buses. A prediction accuracy of below 5 mV could be obtained for most validation sections. Furthermore, a proof-of-concept experiment illustrates the advantages of the fleet learning approach....
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