The performance of predictive models is mainly dependent on the quality of data available for model training. To date, numerous battery datasets have been reported for state of charge (SOC) estimation. These datasets typically consist of charge and discharge steps derived from standardized velocity drive cycles to mimic real-world EV battery usage. For example, Chemali et al. and He et al. conducted battery tests for varying temperatures using dynamic driving schedules to generate time-series current data for SOC estimation. Although these time-series current data capture detailed battery characteristics that enable accurate SOC estimation, they are inherently derived from the velocity-based drive cycles. This raises the possibility of estimating SOC directly from velocity-based drive cycle data, which is often more readily accessible via vehicle telemetry compared to current-time profiles extracted from a battery management system (BMS). However, existing literature does not establish a systematic comparison between SOC estimation based on drive cycles and time-series current data. To address this gap, this study introduces a new dataset comprising three interrelated fidelities, 1) velocity-based drive cycles, 2) simulated current profiles derived from drive cycles and 3) laboratory tested time-series current data. The simulated current profiles bridges the link between the initial velocity drive cycles and measured current data and allows for a direct comparison of models based on these different data sources. Furthermore, this dataset captures trip-specific variations, including driving behavior, charging patterns and terrain conditions, offering a more comprehensive foundation for SOC modeling in real-world EV operations. Conventional SOC estimation methods include equivalent circuit, open circuit, impedance-based, single particle and pseudo-two-dimensional (P2D) models with each method presenting its own advantages and trade-offs. For instance, circuitbased methods are computationally efficient but less accurate, as they rely on simple circuit components. On the other hand, single particle and P2D models provide higher accuracy by using partial differential equations to represent internal battery dynamics but are computationally expensive. Recent research has shown promise in using machine learning (ML) methods to combine the computational efficiency of circuitbased models with the accuracy of physics-based models. Notably, Chaoui et al. demonstrated that simple neural networks can achieve better SOC predictions without the knowledge of underlying battery physics but rather learning the correlation patterns between voltage, current, and temperature. Similarly, Hong et al.