By incorporating human behavioral and terrain variations, our dataset and modeling framework serves as a benchmark for developing personalized SOC models for real-world EV operations. This approach enables more accurate and reliable predictions of charge usage under diverse conditions and bridges the gap between laboratory-based models and practical EV applications. Our findings have significant implications for the design of advanced battery management systems, particularly for real-time charge estimation and adaptive feedback that respond to individual and terrain variations. These improvements can lead to optimized energy usage, while enhancing battery cycle life and overall vehicle efficiency.