发表机构
FZI Research Center for Information Technology, University of Tübingen Germany（弗赖堡大学信息科技研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究电动汽车车载自适应电池功率预测问题，通过将预训练模型转换为可适应版本，结合在线和离线适应策略，提升预测性能，在线和离线技术分别使平均绝对误差降低7.49%和14.88%，凸显车载适应在实际场景中的优势。
AI 中文摘要
电动汽车中的自适应功率管理需要准确的功率预测。虽然深度学习模型在该领域的时间序列预测中已非常有效，但当暴露于与训练数据分布不同的数据时，其性能容易下降。我们引入了一种新颖的方法，使资源受限的电动汽车系统能够进行车载学习，以不断使预训练的电池预测模型适应新的、未见过的数据。我们通过将现有预训练模型转换为可适应版本来利用它们，这些版本保留了初始训练中的关键超参数知识。我们全面研究了在线和离线模型适应策略。结果表明，在各种模型和时间范围内，预测性能有显著提高，在线和离线适应技术分别实现了高达7.49%和14.88%的平均绝对误差降低。这项研究突出了车载适应的巨大好处，在实际电动汽车场景中，与未适应的模型部署相比，可提高电池功率预测。
英文摘要
Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction. Although deep learning models have emerged as highly effective for time-series forecasting in this domain, their performance is prone to degradation when exposed to data with distributions different from the training data. We introduce a novel approach that enables on-device learning in resource-constrained EV systems to continuously adapt pretrained battery prediction models to new, unseen data. We leverage existing pretrained models by transforming them into adaptable versions that retain critical hyperparameter knowledge from their initial training. We comprehensively investigate both online and offline model adaptation strategies. Our results demonstrate significant improvements in forecasting performance across various models and time horizons, achieving mean absolute error reductions of up to 7.49\% and 14.88\% with online and offline adaptation techniques, respectively. This study highlights the substantial benefit of on-device adaptation, resulting in enhanced battery power predictions than unadapted model deployments in real-world EV scenarios.
Comments6 pages, 3 tables, 5 figures; Accepted to IEEE EdgeCom 2025
DOI：10.1109/EdgeCom66327.2025.00026
