It is expensive to test and train data-driven algorithms for detecting and predicting electrical faults in battery systems by solely relying on laboratory experiments. Acquiring battery data, including malfunctions, is a challenge. To address this, our simulation framework generates synthetic battery pack data that simulates real-world variations, such as production quality and environmental conditions. This data includes three randomly occurring electrical faults: abnormal internal resistance, contact resistance and short circuits.