Data-Driven Electric Vehicle Performance Analysis: Integrating Large-Scale Telematics for Charging Duration, Battery Degradation, and Range Estimation
Abstract: This presentation explores data-driven models to advance electric vehicle (EV) performance analysis through large-scale telematics integration. Using Tesla Scope data from over 600 Tesla vehicles, we collect minute-level measurements of location, state of charge (SOC), odometer readings, temperature, and charging events. These variables enable the development of charging duration estimation models that capture the influence of ambient conditions, vehicle usage, and charging infrastructure characteristics. Battery degradation is analyzed using multivariate regression and time-series modeling to quantify the impacts of age, temperature cycles, mileage accumulation, and user charging patterns on long-term energy capacity. In parallel, high-resolution telematics data retrieved from OBD-II ports are used to estimate remaining driving range. This modeling framework incorporates driver acceleration dynamics, traffic congestion levels, ambient temperature, and terrain slope into predictive remaining driving range estimation models. The integration of these datasets and analytical approaches provides a robust methodology for understanding real-world EV operation, optimizing battery management strategies, and improving range estimation accuracy. By advancing predictive methodologies rather than static models, this work creates scalable analytical tools to accelerate EV electrification and support data-informed climate mitigation strategies.
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- 85 Engineer's Way
- Charlottesville, Virginia
- United States 22903
- Building: UVA RICE HALL
- Starts 14 February 2026 05:00 AM UTC
- Ends 10 March 2026 04:00 AM UTC
- No Admission Charge
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Brian
Data-Driven Electric Vehicle Performance Analysis: Integrating Large-Scale Telematics for Charging Duration, Battery Deg
This presentation explores data-driven models to advance electric vehicle (EV) performance analysis through large-scale telematics integration. Using Tesla Scope data from over 600 Tesla vehicles, we collect minute-level measurements of location, state of charge (SOC), odometer readings, temperature, and charging events. These variables enable the development of charging duration estimation models that capture the influence of ambient conditions, vehicle usage, and charging infrastructure characteristics. Battery degradation is analyzed using multivariate regression and time-series modeling to quantify the impacts of age, temperature cycles, mileage accumulation, and user charging patterns on long-term energy capacity. In parallel, high-resolution telematics data retrieved from OBD-II ports are used to estimate remaining driving range. This modeling framework incorporates driver acceleration dynamics, traffic congestion levels, ambient temperature, and terrain slope into predictive remaining driving range estimation models. The integration of these datasets and analytical approaches provides a robust methodology for understanding real-world EV operation, optimizing battery management strategies, and improving range estimation accuracy. By advancing predictive methodologies rather than static models, this work creates scalable analytical tools to accelerate EV electrification and support data-informed climate mitigation strategies.
Biography:
B. Brian Park is a Professor in the Civil & Environmental Engineering and Systems & Information Engineering Departments at the University of Virginia and a member of Link Lab. He earned his Ph.D. from Texas A&M University in 1998. A prolific researcher, Dr. Park has published over 190 papers and amassed more than 9,500 citations. His awards include the 2014 George N. Saridis Best Transactions Paper Award and the 2024 Governor’s Environmental Excellence Award. He holds editorial roles with several leading transportation journals. Dr. Park’s work focuses on connected and automated vehicles, traffic operations, and cyber-physical transportation systems. Through UVA’s Link Lab, he develops technology solutions—particularly cooperative vehicle control algorithms—to enhance the efficiency and safety of surface transportation. He also contributes his expertise to the Transportation Research Board and ASCE technical committees, helping to shape the future of intelligent transportation.