Numerical models for battery management systems must be computationally efficient with enough accuracy for predictive usage when the vehicle is operating. For electric vehicles (EVs), this requires accounting for capacity offset, temperature dependency, and battery aging effects. This effort provides an enhanced formulation of Peukert's equation including temperature effects and the inclusion of an absolute capacity that is calibrated to five different cathodes (LiCoO2, LiCoNiAlO2, LiNiMnCoO2, LiMnNiO2, and LiFePO4) with two types of crystal structures (layered and olivine) from four manufacturers. After data collection using a Vencon battery analyzer and two thermistors measuring self-heating temperature swings, the results demonstrate that the model works relatively well in predicting the State of Charge curve. As expected, the capacity specific parameter is near unity when simulating low offset olivine compounds; whereas, the temperature dependent variable illustrates a wide-range of values with cobalt constituted chemistries on the higher end. Additionally, the model tends to perform better for non-spinel compounds and that manufacturer specified nominal capacities are around 95–99% of the model defined absolute capacity. Overall, the technique of separating current and temperature based phenomena and recognizing modeled patterns that align with current literature are useful steps in developing an efficient battery model.