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DESCRIPTION:Abstract: This presentation explores data-driven models to adva
 nce electric vehicle (EV) performance analysis through large-scale telemat
 ics integration. Using Tesla Scope data from over 600 Tesla vehicles\, we 
 collect minute-level measurements of location\, state of charge (SOC)\, od
 ometer readings\, temperature\, and charging events. These variables enabl
 e the development of charging duration estimation models that capture the 
 influence of ambient conditions\, vehicle usage\, and charging infrastruct
 ure characteristics. Battery degradation is analyzed using multivariate re
 gression and time-series modeling to quantify the impacts of age\, tempera
 ture cycles\, mileage accumulation\, and user charging patterns on long-te
 rm energy capacity. In parallel\, high-resolution telematics data retrieve
 d from OBD-II ports are used to estimate remaining driving range. This mod
 eling framework incorporates driver acceleration dynamics\, traffic conges
 tion levels\, ambient temperature\, and terrain slope into predictive rema
 ining driving range estimation models. The integration of these datasets a
 nd analytical approaches provides a robust methodology for understanding r
 eal-world EV operation\, optimizing battery management strategies\, and im
 proving range estimation accuracy. By advancing predictive methodologies r
 ather than static models\, this work creates scalable analytical tools to 
 accelerate EV electrification and support data-informed climate mitigation
  strategies.\n\nSpeaker(s): Brian\, \n\nAgenda: \nMust RSVP. Dinner will b
 e served.\n\nBldg: UVA RICE HALL\, 85 Engineer's Way\, Charlottesville\, V
 irginia\, United States\, 22903
LOCATION:Bldg: UVA RICE HALL\, 85 Engineer's Way\, Charlottesville\, Virgin
 ia\, United States\, 22903
ORGANIZER:anabathula@ieee.org
SEQUENCE:4
SUMMARY:Data-Driven Electric Vehicle Performance Analysis: Integrating Larg
 e-Scale Telematics for Charging Duration\, Battery Degradation\, and Range
  Estimation
URL;VALUE=URI:https://events.vtools.ieee.org/m/539243
X-ALT-DESC:Description: <br /><p class="MsoNormal">Abstract: This presentat
 ion explores data-driven models to advance electric vehicle (EV) performan
 ce analysis through large-scale telematics integration. Using Tesla Scope 
 data from over 600 Tesla vehicles\, we collect minute-level measurements o
 f location\, state of charge (SOC)\, odometer readings\, temperature\, and
  charging events. These variables enable the development of charging durat
 ion estimation models that capture the influence of ambient conditions\, v
 ehicle usage\, and charging infrastructure characteristics. Battery degrad
 ation is analyzed using multivariate regression and time-series modeling t
 o quantify the impacts of age\, temperature cycles\, mileage accumulation\
 , and user charging patterns on long-term energy capacity. In parallel\, h
 igh-resolution telematics data retrieved from OBD-II ports are used to est
 imate remaining driving range. This modeling framework incorporates driver
  acceleration dynamics\, traffic congestion levels\, ambient temperature\,
  and terrain slope into predictive remaining driving range estimation mode
 ls. 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 wor
 k creates scalable analytical tools to accelerate EV electrification and s
 upport data-informed climate mitigation strategies.</p><br /><br />Agenda:
  <br /><p>Must RSVP. Dinner will be served.&nbsp\;</p>
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