Wavelet-Based Multi-Scale Identification of Crosstalk EMI in Electric Vehicle Wiring Harness Systems

Crosstalk-induced electromagnetic interference (EMI) in high- and low-voltage wire harnesses poses a significant challenge to electromagnetic compatibility (EMC) in electric vehicles (EVs). Traditional time-domain and spectral methods often fail to accurately capture the nonstationary and transient nature of such disturbances. This paper proposes a wavelet-based interference recognition framework utilizing the db4 discrete wavelet transform (DWT) to perform multi-resolution analysis of EMI signals. By decomposing measured or simulated waveforms into frequency-localized components, the method isolates high-frequency spikes, modulated oscillations, and low-frequency drifts across distinct scales. An energy-based feature extraction and classification scheme is developed to identify and attribute interference to specific physical sources. The proposed approach is validated through both CST-based simulation and real-vehicle measurement data, demonstrating robust performance in denoising, feature enhancement, and source-level interference classification. The results highlight the method’s potential for real-time EMC diagnostics and system-level EMI analysis in complex vehicular environments.