Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains
Predicting thermal behavior in high-performance EV powertrains is challenging due to unobservable internal temperatures and domain shift from lab to track. This paper applies conformal prediction with weighted ensemble batch prediction intervals (EnbPI) to improve coverage under covariate shift. The method recovers coverage from 70.13% to 72.42% on real battery data, and is tested on Formula 1 telemetry as an unsupervised diagnostic.
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[Submitted on 2 Jul 2026]
Title:Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains
View a PDF of the paper titled Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains, by Varshith Roy Kotla
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Abstract:Predicting thermal volatility in high-performance EV powertrains is difficult as internal temperatures are rarely observable outside the lab, and models calibrated on lab drive cycles fail when deployed against real-world loads. We study this lab-to-track transfer problem using conformal prediction, offering distribution-free uncertainty bounds. We implement Ensemble Batch Prediction Intervals (EnbPI; Xu & Xie, 2021), a leave-one-out bootstrap-ensemble conformal method for autocorrelated time series, and calibrate it on real CALCE lithium-ion cycler data (A123 SP20 cells, FUDS profile). We evaluate it under a genuine, measured covariate shift: a second real CALCE test condition (US06 Highway Driving Schedule at 45°C). The unweighted EnbPI bound, achieving its nominal 95% coverage in-distribution (measured: 95.00%), degrades to 70.13% empirical coverage under this real shift. We introduce a weighted EnbPI procedure combining EnbPI's ensemble residuals with density-ratio weighting (Tibshirani et al., 2019), estimating the density ratio via a probabilistic domain classifier. This recovers coverage to 72.42%, a modest, honestly-reported improvement, not a complete fix. We additionally apply the calibrated model to real 2023 Formula 1 telemetry (Monza and Silverstone, driver VER) as an unsupervised out-of-distribution diagnostic. Because no internal thermal channel exists in public trackside telemetry, we report only unsupervised flag rates (65.6% at Monza, 58.0% at Silverstone, well above the 5% in-distribution base rate) and note inconsistent associations between flags and braking/DRS zones. We conclude that conformal domain adaptation is a promising but only partially solved tool for this problem, detailing exactly where it falls short.
Comments: 8 pages, 3 tables, github like for the codes and dataset:this https URL
Subjects:
Machine Learning (cs.LG)
MSC classes: 62G15, 62P30, 68T05
ACM classes: I.2.6; G.3; J.2
Cite as: arXiv:2607.02722 [cs.LG]
(or arXiv:2607.02722v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.02722
arXiv-issued DOI via DataCite
Submission history
From: Varshith Roy Kotla [view email] [v1] Thu, 2 Jul 2026 19:17:26 UTC (420 KB)
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