Modern battery management systems have a critical need for highly accurate battery terminal voltage models, which are a key component of algorithms which estimate or predict power capability, range, temperature, and other factors. While electro-chemical and equivalent circuit models are widely used for this purpose, they typically struggle to capture the complex, non-linear dynamics and degradation effects inherent in real-world battery operation, necessitating frequent recalibration. This study addresses these limitations by proposing a robust, data-driven approach for terminal voltage estimation using a feedforward neural network (FNN) machine learning model. An LG E66 cell, a high-performance lithium-ion battery with a Nickel Cobalt Manganese (NCM) cathode, was selected for this study due to its prevalent use in light duty electric vehicles. Data collected at temperatures ranging from 40 °C to -20 °C is used to train and test the models, with model error reported for HWFET, UDDS, US06, and LA92 cycles. Models were systematically trained with 1–2 hidden layers and 400–35,000 neurons, while varying the filtered-power corner frequencies (0.01–100 millihertz (mHz)), and the least-error configuration was chosen. The model inputs at each time step include power, state of charge (SOC), battery temperature, and additionally, filtered power values as part of a feature engineering effort. The RMS error across different temperatures and drive cycles never exceeds 20 mV and is about 25% less than error reported in the literature for more complex recurrent neural networks applied to similar cells.