预测电动垂直起降(eVTOL)飞机电池退化:方法、挑战和未来趋势的综合回顾

IF 17 1区 工程技术 Q1 ENERGY & FUELS
Baoji Wang , Teng Xu , Bailin Zheng , Yue Kai , Kai Zhang
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引用次数: 0

摘要

随着航空智能技术的快速发展,电动垂直起降飞机已成为低空经济领域的重要参与者,其电池性能直接影响到飞机的安全性和成本,因此准确的预测至关重要。本文全面回顾了eVTOL飞机电池退化预测方法的文献,简要概述了早期建模方法,并重点介绍了eVTOL在频繁起降、高功率负载和复杂环境条件等独特操作场景下的适用性和局限性。目前的预测工作主要针对关键指标,包括电池寿命、健康状态和容量保留,采用了一系列技术方法,如电化学建模、等效电路建模、数据驱动算法(如机器学习和深度学习),以及将领域知识与数据分析相结合的混合物理模型。本文系统地总结了eVTOL技术在不同发展阶段的主要预测方法及其演变。在此基础上,我们强调了现有的技术瓶颈和尚未解决的挑战,包括对数据和计算资源的高需求限制了实时性能,传统模型在高放电率和极端条件下的准确性较差,在准确建模复杂的多物理场相互作用以及实现预测精度,可解释性和实时计算效率之间的稳定平衡方面的挑战。历史飞行数据的稀缺性也影响了模型的可靠性和泛化。最后,提出了提高电池退化预测的可靠性和准确性的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting battery degradation for electric vertical take-off and landing (eVTOL) aircraft: A comprehensive review of methods, challenges, and future trends

Predicting battery degradation for electric vertical take-off and landing (eVTOL) aircraft: A comprehensive review of methods, challenges, and future trends
With the rapid development of intelligent technologies in aviation, electric vertical take-off and landing (eVTOL) aircraft have emerged as key players in the low-altitude economy, their battery performance directly impacts safety and cost, making accurate prediction essential. This paper presents a comprehensive review of the literature on battery degradation prediction methods for eVTOL aircraft, providing a brief overview on early modeling approaches and placing primary emphasis on recent advances in their applicability and limitations under unique operational scenarios of eVTOL, such as frequent takeoffs and landings, high power loads, and complex environmental conditions. Current prediction efforts primarily target key indicators including battery lifespan, health status, and capacity retention, employing a range of technical approaches such as electrochemical modeling, equivalent circuit modeling, data-driven algorithms like machine learning and deep learning, and hybrid physics-informed models that integrate domain knowledge with data analysis. The review systematically summarizes the main prediction methods and their evolution in different phases of the development of eVTOL technology. On this basis, we highlight existing technical bottlenecks and unresolved challenges, including the high demand for data and computational resources limiting real-time performance, poor accuracy of traditional models under high discharge rates and extreme conditions, challenges in accurately modeling complex multi-physics interactions and achieving a stable balance among prediction accuracy, interpretability, and real-time computational efficiency, as well as the scarcity of historical flight data affecting model reliability and generalization. This review also proposes future research directions to enhance the reliability and accuracy of battery degradation forecasting for eVTOL applications.
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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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