<p>Permanent Magnet Synchronous Motors (PMSMs) are widely used in Electric Vehicles (EVs) owing to their high efficacy and power density. Nevertheless, it is vulnerable to electrical, mechanical and thermal faults under dynamic operating conditions. To address the limitation of conventional diagnosis methods, this paper proposes a fault diagnosis system based on Artificial Intelligence (AI) powered Black Winged Kite Optimization Algorithm (BWKOA) with hybrid Neuro-inspired Radial Basis Function Spiking Convolution Network (NeuroRBF-SCNet) for PMSM based EV system. Data preprocessing involves data cleaning, data integration and data transformation for removing noise, outliers, combining heterogeneous sensor signals and ensuring numerical stability. Exploratory Data Analysis (EDA) is performed using univariate and bivariate analysis for understanding feature distribution, correlation and fault sensitive pattern. Feature Engineering based on feature scaling is used for balancing multi-sensor inputs, and reducing bias caused by dominant features. The proposed NeuroRBF-SCNet, learns intricate defect patterns by simulating biological spiking responses and Radial Basis Functions (RBF) layer for enhancing nonlinear fault diagnosis. Inspired by the strategic hunting behavior of black-winged kites, a BWKOA is included for dynamic hyperparameter adjustment to improve the system’s learning accuracy. This ensures superior global convergence and avoidance of local optima. The proposed framework is implemented using Python and evaluated on the New Energy Vehicle Diagnosis Dataset across four operating conditions. Experimental results demonstrate the proposed model attaining superior performance with accuracy, recall, F1-Score of 99.09% and precision of 99.11% compared to existing models.</p>