让电动车在本地持续优化电池功率预测，提升真实场景下的准确性。
On-Device Adaptive Battery Power Prediction for Electric Vehicles
- 用已有预训练模型构建可本地自适应的版本，保留关键参数知识。
- 在线与离线适配分别降低7.49%和14.88%的平均绝对误差。
- 适合资源受限的车载系统，提升真实驾驶环境中的预测能力。
电动汽车的自适应电源管理需要精确的功率预测。尽管深度学习模型在该领域表现出色，但其性能在遇到分布不同的数据时容易下降。本文提出一种新型方法，使资源受限的车载系统能够对预训练的电池预测模型进行本地学习，持续适应新数据。通过将现有预训练模型转化为可适配版本，保留初始训练中的关键超参数知识，我们全面研究了在线与离线模型适配策略。实验结果表明，多种模型与时间跨度下预测性能显著提升：在线适配最高降低7.49%的均方绝对误差，离线适配最高降低14.88%。本研究证明了本地自适应带来的巨大优势，在真实电动车场景中优于未适配的模型部署。
原文摘要 · Abstract (English)
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.
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