机器学习驱动的 eVTOL 飞行器锂离子电池功率能力预测
Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft
AI总结：
针对 eVTOL 高倍率放电下的电池功率极限预测，本文融合物理机理与机器学习预测电压温度及剩余放电时间，并加速最大功率搜索。
摘要：
电动垂直起降（eVTOL）飞行器已成为变革城市交通的一种有前景的解决方案。它们给电池管理带来了若干技术挑战，其中一个突出挑战是预测其锂离子电池系统的功率能力。该挑战源于 eVTOL 飞行期间所需的高 C 倍率放电条件，以及锂离子电池电热动力学的复杂性。本文首次构建了一个面向 eVTOL 的功率极限预测问题，该问题显式考虑了较长的预测时域以及紧急着陆可能发生的情况。随后，我们以两种相互交织的方式利用机器学习来解决该问题。首先，我们采用一种将物理机理与机器学习相结合的动态模型，以高精度预测锂离子电池的电压和温度行为。其次，在执行最大功率搜索时，我们利用机器学习预测剩余放电时间，并使用该预测通过快速计算来加速搜索。我们的验证结果表明了所提出研究对 eVTOL 运行的有效性。
英文摘要：
Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging conditions required during eVTOL flights as well as the complexity of lithium-ion batteries' electro-thermal dynamics. This paper, for the first time, formulates a power limit prediction problem for eVTOL which explicitly considers long prediction horizons and the possible occurrence of emergency landings. We then harness machine learning to solve this problem in two intertwined ways. First, we adopt a dynamic model that integrates physics with machine learning to predict a lithium-ion battery's voltage and temperature behaviors with high accuracy. Second, while performing search for the maximum power, we leverage machine learning to predict the remaining discharge time and use the prediction to accelerate the search with fast computation. Our validation results show the effectiveness of the proposed study for eVTOL operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
