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High Frequency Impedance and Electromagnetic Interference Suppression of Lithium-Ion Power Battery Pack
Technical Paper
2019-01-1060
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
When electric vehicle speeds up or slows down, rapidly changing current and voltage (di/dt and du/dt) would occurs in its lithium-ion power battery. In this way, the impedance of power battery would changes with parasitic parameters because that the ion transport in electrolytes would influence diffusion effect and polarization effect of battery. Thus, the lithium-ion battery cannot be regarded as ideal component in high frequency, which could cause unpredictable problem in electromagnetic interference (EMI). However, most previous studies took lithium-ion power batteries as disturbed objects or transmission routes, which ignore the electromagnetic interference of battery itself. Based on it, this paper analyses the internal mechanism of EMI in lithium-ion power battery, and simulates the distribution of electromagnetic field as well as it corresponding interference suppression measures. Firstly, the test platform for parameter extraction of battery cell is built. The parasitic parameters are extracted through testing. The high-frequency equivalent circuit of power battery is consequently established. Secondly, peripheral circuit of lithium-ion battery (DC/AC system) is established to obtain the current at DC bus, which would be imported to an electromagnetic simulation tool as excitation source to conduct co-simulation of field and circuit for distribution of electromagnetic field in battery. Thirdly, the lithium-ion battery pack is optimally designed in EMI. The structure and parameters of the filter are selected according to energy distribution frequency band of EMI signal. Results of simulation and testing turn out that the designed filter could decrease EMI effectively.
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Zhang, J. and Lv, Y., "High Frequency Impedance and Electromagnetic Interference Suppression of Lithium-Ion Power Battery Pack," SAE Technical Paper 2019-01-1060, 2019, https://doi.org/10.4271/2019-01-1060.Data Sets - Support Documents
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