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Accurate prediction of electric vehicle (EV) charging behavior in residential settings is essential for optimizing grid load management and supporting the transition to sustainable transportation. This paper introduces a novel transformer-based methodology for inferring EV charging events directly from smart meter time series data. Our approach, which employs a divide-and-conquer strategy, effectively captures intricate charging patterns by processing segmented load data in parallel. This enables high-resolution, short-term forecasting of EV charging occurrences without relying on explicit charging records. The efficacy of our method is validated using real-world smart meter data, demonstrating its potential to provide valuable predictive insights for grid operators and facilitate more efficient energy distribution and management strategies.