---
title: "Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains — Stuff That Spins"
description: "arXiv:2607.02722v1 Announce Type: new Abstract: Predicting thermal volatility in high-performance EV powertrains is difficult as internal temperatures are rare…"
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date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:02.765065+00:00"
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# Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02722  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02722v1 Announce Type: new 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-ense

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