KEYWORDS: Design, Control systems, Data processing, Computing systems, Data acquisition, Resistance, Inductance, Signal processing, Servomechanisms, Computer architecture
The rapid global development of electric vehicles has heightened the imperative for exacting performance assessments of Electric Power Steering (EPS) systems, a pivotal subsystem within the automotive domain This research focuses on the EPS automated testing equipment as the research subject, proposing an automated testing system solution for EPS motors in new energy vehicles based on the testing requirements of the entire vehicle factory. The system is primarily composed of three parts: a testing platform, a control cabinet, and an industrial computer, incorporating both hardware devices and software programs. Its purpose is to conduct motor performance tests specifically tailored for EPS power-assisted motors. The testing system can achieve automated execution, data collection, and data analysis, displaying test results and saving test reports. Experimental findings affirm that the motor testing system demonstrates elevated stability and reliability, contributing significantly to the enhancement of testing efficiency and precision, as substantiated by relevant academic literature.
As the robot enters the carriage or container for stacking, it is inevitable to develop a reasonable stack shape detection method to timely find the wrong situations such as stacking boxes tipping and non-conforming to packing constraints, and timely correct them to ensure the smooth stacking. Therefore, a high precision stack detection method based on three-dimensional reconstruction of solid state lidar (Livox Mid-70) is designed. The actual palletizing type point cloud was obtained by laser SLAM reconstruction. Combined with the theoretical palletizing type point cloud result, the overall root mean square error of the actual reconstructed palletizing type and the theoretical planned palletizing type was calculated by point cloud registration technology, and the palletizing effect was tested. The point cloud registration experiment is carried out by using the point cloud obtained by simulation and the point cloud extracted by planning stack shape. The results show that the stack shape detection method can meet the actual requirements in detection accuracy and detection time, and verify the effectiveness of the method.
With the development of new energy vehicles, the demand for auto parts is also rising. It is difficult to design the control strategy of EPS system because of multiple control objectives. In this study, a control strategy is introduced which is based on the super twisting algorithm. The control objectives are to provide ideal assist torque, respond quickly and return road information. The EPS dynamic analysis and built model in order to generate the desired motor angle. In order to achieve control objectives, the controller based on super-twisting algorithm is designed. To estimate the EPS system state and unknow inputs, the observer with unknow input is employed. Auxiliary outputs are constructed because the EPS system is not satisfied the observer-matching condition. Using of the second order SM differentiators estimate the auxiliary outputs. Finally, the performance of the controller, observer and differentiator is verified by simulation, and the controller can provide expected the assist torque. The result of simulation show that the control strategy is effectiveness and robustness.
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