用加权方法提升电动车电机热模型从实验室到赛道的预测可靠性。
Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains
- 引入加权置信区间法，结合密度比校准处理真实路况下的数据分布偏移。
- 实测覆盖率从70.13%提升至72.42%，仍低于目标95%但更真实可信。
- 适用于赛车动力系统热管理、自动驾驶等需高可靠性的实时诊断场景。
预测高性能电动车动力系统的热波动极具挑战，因内部温度在实验室外难以观测，且基于实验驱动循环训练的模型在真实负载下表现失效。本文研究这一从实验室到赛道的迁移问题，采用无需分布假设的置信区间方法。使用针对自相关时间序列的集成批量预测区间（EnbPI）方法，并在真实的CALCE锂离子电池测试数据（A123 SP20电芯，FUDS工况）上校准。在真实存在的协变量偏移条件下评估：第二组实际测试条件（US06高速公路工况，45℃）。未加权的EnbPI在分布内达到95.00%名义覆盖率，但在真实偏移下下降至70.13%。本文提出加权EnbPI方法，将EnbPI的集成残差与密度比权重结合，通过概率域分类器估计密度比。该方法将覆盖率恢复至72.42%，为适度且诚实的改进，非彻底解决。进一步将校准模型应用于2023年一级方程式赛事的真实遥测数据（蒙扎与银石赛道，车手维特尔），作为无监督异常诊断。由于公开赛道遥测中无内部温感通道，仅报告异常标志率（蒙扎65.6%，银石58.0%，远高于分布内基准5%），并指出标志与制动/DRS区域关联不一致。结论表明，置信区间域适应具有前景但尚未完全解决此问题，明确指出了当前局限所在。
原文摘要 · Abstract (English)
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.
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