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The Dynamic Electromagnetic Distribution and Electromagnetic Interference Suppression of Smart Electric Vehicle
Technical Paper
2019-01-1061
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Smart electric vehicles need more accurate and more timely information as well as control than traditional vehicles, which depends on great environmental sensors such as millimeter-wave radar. In this way, the electromagnetic compatibility of whole vehicle would confront more serious challenges because of its high frequency range. Thus, this paper studies the electromagnetic distribution and electromagnetic interference suppression of smart electric vehicles with the followings. Firstly, the millimeter wave radar is modeled and optimized. Micro strip patch antenna, with small size, light mass and low cost, is used as array element of antenna. Millimeter wave radar is modeled and simulated step by step from array element to line array to planar matrix. Then the Cross Shape - Uniplanar Compact - Electromagnetic Band Gap (CS-UC-EBG) structure is deployed to optimize its electromagnetic characteristics, based on finite time domain difference model theory. Secondly, considering that the most of electromagnetic interference (EMI) in electric vehicle comes from drive system, the conduction model of electric drive system is established on the basis of analyzing mechanism of EMI in electric drive system, which is verified by corresponding experiments. Thirdly, according to requirements of vehicle electromagnetic modeling, DC/DC system, millimeter wave radar, drive system and high-voltage cables are modeled as excitation sources to simulate the dynamic electromagnetic distribution of vehicle. Finally, aiming at overshoot frequency point in whole vehicle radiation test, the shielding wiring and common mode choke are used to suppress EMI to meet requirements.
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Zhang, J., Lv, Y., and Shen, M., "The Dynamic Electromagnetic Distribution and Electromagnetic Interference Suppression of Smart Electric Vehicle," SAE Technical Paper 2019-01-1061, 2019, https://doi.org/10.4271/2019-01-1061.Data Sets - Support Documents
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