Accurate prediction of the remaining driving range in electric vehicles (EVs) is critical for efficient trip planning, reducing the risk of battery depletion, and improving user experience. One of the significant challenges in achieving high prediction accuracy is battery degradation, which gradually reduces battery capacity and impacts the vehicle’s range. This study uses machine learning algorithms to investigate the impact of incorporating battery degradation—expressed through the State of Health (SoH) indicator—into range prediction models. A comparative analysis is conducted between models with and without SoH input based on real-world EV telemetry data. The results demonstrate that integrating degradation metrics into the prediction model significantly enhances the accuracy of remaining range estimation. The findings may benefit developers of energy management systems and EV software applications.
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