Reducing Range Anxiety through Predictive Modeling of EV Battery Degradation

Authors

Caleb Thornsbury, Southern Methodist UniversityFollow
Christian Castro, Southern Methodist UniversityFollow
Bivin Sadler, Southern Methodist UniversityFollow

Abstract

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery degradation and examine patterns to improve battery performance with age, reducing EV range anxiety and educating buyers/manufacturers. Because the degradation metric is a calculated variable from the same electrochemical features that are used as model inputs, results should be used as an estimation rather than an independent prediction.

Recommended Citation

Thornsbury, Caleb; Castro, Christian; and Sadler, Bivin (2025) "Reducing Range Anxiety through Predictive Modeling of EV Battery Degradation," SMU Data Science Review: Vol. 9: No. 3, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol9/iss3/1

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License