eVTOL Insights 2026年10月数字杂志探讨eVTOL领导力与自主飞行发展
eVTOL Insights 2026年10月数字杂志发布,聚焦eVTOL领域领导力、自主飞行及先进空中交通下一步发展。Joby Aviation展示其自主飞行技术,一架改装塞斯纳飞机完成3,199英里无操控飞行。Horizon Aircraft CEO分享创业与航空业务建设经验,探讨如何将项目转化为商业可行服务。
研发设计收录论文、预印本和会议论文;标题保留原文,预印本与设计目标请核对原文。未经过编辑判断的资料展示在全部动态中。
eVTOL Insights 2026年10月数字杂志发布,聚焦eVTOL领域领导力、自主飞行及先进空中交通下一步发展。Joby Aviation展示其自主飞行技术,一架改装塞斯纳飞机完成3,199英里无操控飞行。Horizon Aircraft CEO分享创业与航空业务建设经验,探讨如何将项目转化为商业可行服务。
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该研究通过相位同步方法分析eVTOL飞机的降噪机制,提出了一种优化旋翼相位配置以降低噪声的方案。此方法对eVTOL行业在噪声控制方面具有重要参考价值,尤其在多旋翼系统中可能有效减少远场噪声。研究结果支持旋翼相位调整在降低噪声方面的潜力,但未涉及适航认证或实际飞行测试。
该研究提出了一种新型飞行员控制概念,旨在减少 eVTOL 飞机多阶段飞行操作中的飞行员工作负担。通过模拟器验证,该系统利用触觉反馈和自由度优化,支持更直观的飞行控制。研究结果对 eVTOL 适航审定和控制系统的开发具有参考价值。
该研究展示了24S NCM电池系统在eVTOL飞机中的设计、制造及飞行演示,重点评估了其在高倍率放电条件下的性能。研究结果对eVTOL电池系统的适航认证和实际应用具有参考价值,尤其在高功率需求场景下的热管理和容量保持方面提供了数据支持。依据来源摘要。
变革性的电动垂直起降(eVTOL)飞行器在过去十年中因其高效的翼载巡航能力和对地面基础设施的依赖减少而受到广泛关注。然而,这些飞行器的控制系统设计仍然具有挑战性,因为它们必须在多个飞行阶段中运行,每个阶段都有不同的主导动态。如果未加以解决,这种复杂性将显著增加飞行员的工作负担,因此推动了多阶段飞行操作的飞行员控制系统开发。简化的飞行操作概念为减少飞行员工作负担提供了一种有前景的策略。本研究提出了eVTOL飞机的一种新型飞行员控制系统设计与实现,并通过配备主动力反馈侧杆的全动模拟器上的串联倾斜翼飞机模拟进行验证。该系统在特定飞行阶段为飞行员提供触觉反馈,支持直观控制。所提出的方案通过利用可用的自由度实现了无缝过渡和多阶段飞行操作。此外,还提出了一种基于最优控制的方法作为评估指令滤波器引起的性能损失和操纵器活动的指标。结果表明,所提出的指令滤波器与闭环系统相比不会显著增加任务持续时间,而主动侧杆有助于减少操纵器活动。(预印本;同行评审状态请核对原文。)
Accurate demand forecasting is a prerequisite for eVTOL vertiport dispatch, pad staffing, battery service planning, and access management, yet short-horizon demand is affected by confounded interactions among weather, road access, queue feedback, aircraft availability, and pricing interventions. This study evaluates the proposed model on a synthetic but engineering-constrained eight-vertiport network, so the reported results should be interpreted as controlled methodological evidence rather than direct commercial demand measurement. The paper proposes a Causal Aware Feature Fusion DeepONet Mamba model that learns a function-to-function mapping from urban mobility states to vertiport demand fields. Causal gates separate contextual variables, network states, and intervention-sensitive variables before a Mamba branch encoder and coordinate-aware DeepONet trunk produce interval demand forecasts. Experiments with 184 simulated operating days show that the proposed model achieves an MAE of 18.7 requests per 15-minute interval, reducing error by 15.6% relative to the strongest non-causal baseline. Peak interval error decreases to 54 requests, 0.9-quantile pinball loss decreases to 11.3, and scenario ranking error is reduced by 32.8% compared with Mamba without causal fusion. Spatial transfer tests, missing-sensor experiments, and capacity-restriction scenarios indicate that causal feature control improves robustness when operating conditions deviate from ordinary correlations. The results show that combining causal fusion, neural operator learning, and selective state-space memory
Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment. Flight planning traditionally relies on classic algorithms that struggle to incorporate flexible human preferences. We present FRAMe, an End-to-End Large Language Model (LLM) Flight Planning tool with RAG-based Memory and Multi-modal Coach Agent. Our system integrates a planner LLM with a multi-modal coach agent and retrieval augmented generation (RAG)-based memory to generate flight plans that satisfy mission constraints while aligning with human flight operator preferences. We demonstrate the system in a range of real-world-inspired scenarios of varying difficulty levels. Across four LLMs, the full FRAMe system (RAG and coach) yields the highest validity for every planner (up to 93.8% aggregate, 99% on Easy scenarios for the strongest planner) and shifts preference-relevant metrics in the operator-favored direction where the metric has headroom. FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural language instructions into safe, efficient, and flexible flight routes. The code is available at: github.com/amin-tabrizian/FlightPlanningLLMs(预印本;同行评审状态请核对原文。)
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该研究探讨了氢燃料电池在eVTOL推进系统中的优化方法,分析了电池、燃料电池堆和氢气罐容量对航程和载荷的影响。研究结果有助于理解eVTOL在推进系统设计中的权衡关系,对适航审定和行业技术发展具有参考价值。依据来源摘要
该研究针对eVTOL飞机的高功率密度电机设计提出了形状优化方案,重点解决电机重量和冷却问题。研究通过分析模型和有限元模型验证了设计效果,为eVTOL电机开发提供了参考。材料显示优化设计可有效降低电机质量,但分析模型存在一定的预测偏差。
该研究通过高保真CFD分析和PD控制器评估,探讨了多旋翼eVTOL飞行器的空气动力学行为和控制效果,为eVTOL的适航审定和飞行控制设计提供了理论支持。研究结果有助于理解分布式电动推进系统的复杂动态特性,对推动eVTOL在城市空中交通中的应用具有重要意义。
本文介绍了由AVL、Unisphere和blueflite开发的集成仿真工作流程,用于电动飞机和eVTOL的设计与运营,重点在于通过数字孪生技术结合高保真度模型进行任务可行性评估。该研究对适航认证中涉及的能源管理、热行为和任务持续时间等关键因素提供了支持,有助于推动电动航空领域的标准化和实际应用。
Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together(预印本;同行评审状态请核对原文。)
电动推进预计将在先进空中交通(AAM)的可持续发展中发挥重要作用。然而,电池的有限能量密度促使需要在飞行过程中最小化能耗。本文研究了全电动飞机在稳定巡航飞行中的最小总能耗问题。该问题被表述为一个最优控制问题,其中巡航空速和最终巡航时间是优化变量。电池供电电压被建模为电池电量的仿射函数。利用庞特里亚金最小原理推导出最优性的必要和充分条件,从而得到巡航空速和最终巡航时间的闭式表达式。还推导了额外的分析条件,以确定全电动运行是否可行,其中之一是必须有足够的电荷。基于BETA Technologies CX300全电动飞机和一个代表性AAM场景的数值模拟展示了飞机重量、巡航高度、电气系统效率和初始电池电量如何影响最优空速和全电动巡航的可行性。(预印本;同行评审状态请核对原文。)
本文中,我们生成了电动垂直起降(eVTOL)飞行器的概念工程设计。我们遵循基于仿真的推理(SBI)范式,通过学习eVTOL设计空间上的后验分布来实现。为了学习这一分布,我们对离散的飞行器配置(拓扑结构)及其对应的连续参数集进行采样。因此,我们引入了一个分层的概率模型,由两个扩散模型组成。第一个模型利用了近期关于黎曼扩散语言建模(RDLM)和统一世界模型(UWMs)的研究成果,使我们能够从离散和连续空间中采样拓扑结构。对于第二个模型,我们引入了一种掩码扩散方法,以根据拓扑结构采样相应的参数。我们的方法重新发现了已知的设计趋势和物理定律,同时显著加速了设计生成。(预印本;同行评审状态请核对原文。)
The Low-Altitude Economy (LAE) is rapidly emerging as a new technological and industrial frontier, with unmanned aerial vehicles (UAVs), electric vertical takeoff and landing (eVTOL) aircraft, and aerial swarms increasingly deployed in logistics, infrastructure inspection, security, and emergency response. However, the large-scale development of the LAE demands a reliable aerial foundation that ensures not only real-time connectivity and computational support, but also navigation integrity and safe airspace management for safety-critical operations. High-Altitude Platforms (HAPs), positioned at around 20 km, provide a unique balance between wide-area coverage and low-latency responsiveness. Compared with low earth orbit (LEO) satellites, HAPs are closer to end users and thus capable of delivering millisecond-level connectivity, fine-grained regulatory oversight, and powerful onboard computing and caching resources. Beyond connectivity and computation, HAPs-assisted sensing and regulation further enable navigation integrity and airspace trust, which are essential for safety-critical UAV and eVTOL operations in the LAE. This article proposes a five-stage evolutionary roadmap for HAPs in the LAE: from serving as aerial infrastructure bases, to becoming super back-ends for UAV, to acting as frontline support for ground users, further enabling swarm-scale UAV coordination, and ultimately advancing toward edge-air-cloud closed-loop autonomy. In parallel, HAPs complement LEO satellites and cloud infrastructures to form a global-regional-local three-tier architecture. Looking forwa(预印本;同行评审状态请核对原文。)
Advanced Air Mobility (AAM) operations are planned to utilize strategic flight planning services that predict temporal uncertainties to validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. This paper presents a Kalman Filter-based uncertainty propagation method that models Flight Management System (FMS) correction behavior through a sigmoid-blended measurement noise covariance. The sigmoid formulation generalizes existing discrete FMS activation thresholds into a continuous, tunable function that smoothly transitions the filter's measurement noise based on progress toward each waypoint. When the measurement noise is high, due to an inverse relationship, the Kalman gain is small and thus uncertainty grows; as the aircraft nears a waypoint, measurement noise decreases as a function of progress, the Kalman gain increases, and state covariance contracts which models the FMS progressively correcting toward the planned trajectory. The approach is computationally efficient (up to two orders of magnitude faster than Monte Carlo methods), scales with control inputs, and is parametrically tunable for different classes of aircraft. The measurement noise covariance is calibrated using real Automatic Dependent Surveillance-Broadcast (ADS-B) data from commercial Instrument Flight Rules (IFR) flights serving as surrogates for future AAM operations, achieving coverage probability conservative relative to theoretical Gaussian predictions at the 1-sigma confidence level on a hold out verification dataset (N = 36). Parameter sensitivity (预印本;同行评审状态请核对原文。)
垂直起降飞机的电气化需要高保真度的电池管理系统,能够预测在激烈功率动态下的电压响应。虽然数据驱动模型具有高精度,但它们通常需要复杂的架构和大量的训练数据。相反,等效电路模型(ECMs),如二阶模型,提供了物理可解释性,但在高C率非线性方面存在困难。本文研究了将基于物理的信息整合到数据驱动的替代模型中的影响。具体而言,我们评估基于物理的特征是否允许简化神经网络架构而不影响精度。使用开源电动垂直起降(eVTOL)电池数据集,我们将纯数据驱动模型与基于物理的数据模型进行比较。结果表明,基于物理的模型在精度上与复杂的纯数据驱动模型相当,但使用的可训练参数最多可减少75%,显著降低了潜在机载部署的计算开销。(预印本;同行评审状态请核对原文。)
本调查回顾了现有和设想中的安全漏洞及防御机制,这些与先进空中交通(AAM)系统相关,重点是电动垂直起降(eVTOL)飞机。借鉴商业航空中航电系统的漏洞和自动化无人机系统(UAS)的漏洞,本文提出了攻击分类,分析了缓解策略,并提出了针对未来AAM生态系统的安全系统架构。本文还强调了关键威胁向量,包括全球定位系统(GPS)干扰/欺骗、空中交通管制(ATC)射频误用、对TCAS和ADS-B的攻击、通过电子飞行包(EFB)的可能后门、由飞机自动化和连接性引入的新漏洞,以及飞行管理系统(FMS)软件、数据库和云服务的风险。最后,本文描述了针对这些攻击的新兴防御技术,并指出需要解决的开放性技术问题以实现更好的防御机制。(预印本;同行评审状态请核对原文。)
Conventional optimization-based metering depends on strict adherence to precomputed schedules, which limits the flexibility required for the stochastic operations of Advanced Air Mobility (AAM). In contrast, multi-agent reinforcement learning (MARL) offers a decentralized, adaptive framework that can better handle uncertainty, required for safe aircraft separation assurance. Despite this advantage, current MARL approaches often overfit to specific airspace structures, limiting their adaptability to new configurations. To improve generalization, we recast the MARL problem in a relative polar state space and train a transformer encoder model across diverse traffic patterns and intersection angles. The learned model provides speed advisories to resolve conflicts while maintaining aircraft near their desired cruising speeds. In our experiments, we evaluated encoder depths of 1, 2, and 3 layers in both structured and unstructured airspaces, and found that a single encoder configuration outperformed deeper variants, yielding near-zero near mid-air collision rates and shorter loss-of-separation infringements than the deeper configurations. Additionally, we showed that the same configuration outperforms a baseline model designed purely with attention. Together, our results suggest that the newly formulated state representation, novel design of neural network architecture, and proposed training strategy provide an adaptable and scalable decentralized solution for aircraft separation assurance in both structured and unstructured airspaces.(预印本;同行评审状态请核对原文。)
电动垂直起降(eVTOL)飞机已成为改变城市交通的有前景的解决方案。它们在电池管理方面存在一些技术挑战,其中一个重要问题是预测其锂离子电池系统的功率能力。这一挑战源于eVTOL飞行过程中所需的高C率放电条件以及锂离子电池的电热动态复杂性。本文首次将功率限制预测问题公式化为eVTOL,明确考虑了长期预测范围和紧急着陆的可能性。然后,我们利用机器学习以两种相互关联的方式解决这一问题。首先,我们采用一个动态模型,将物理与机器学习相结合,以高精度预测锂离子电池的电压和温度行为。其次,在进行最大功率搜索时,我们利用机器学习预测剩余放电时间,并利用预测结果通过快速计算加速搜索。我们的验证结果表明,所提出的研究所用于eVTOL操作是有效的。(预印本;同行评审状态请核对原文。)
随着航空旅行需求的增加以及减少排放的紧迫性,从化石燃料推进系统向可持续航空转型是至关重要的一步。尽管电池在城市空中交通中被广泛使用,但其较长的充电时间限制了其在连续飞行中的可行性。混合推进系统通过整合燃料电池和电池,由于其更高的能量密度和改进的效率,提供了一个有前景的替代方案。本文提出了一种用于区域飞机的新型混合动力传动架构,结合了氢燃料电池、锂离子电池和辅助铝空气电池。该系统通过使用塞斯纳208飞机的实际功率需求数据进行评估。氢燃料电池作为主要电源,确保连续运行,而锂离子电池管理瞬态功率波动以提高系统稳定性。铝空气电池被引入作为高能量的应急备用电源,在关键情况下提供延长的续航能力。建立了一个混合整数优化模型用于系统尺寸和功率调度,确保在各个电源之间最优的能量分配。分析了多种操作场景以评估系统性能,特别是在紧急情况下,电力可靠性至关重要。结果突显了所提出的混合架构在提高区域飞机应用中的能源效率和飞行安全方面的可行性和有效性。(预印本;同行评审状态请核对原文。)
本研究提出了一种利用先进的计算流体力学(CFD)模拟和强制振荡测试对飞行器过渡阶段动态气动导数进行全面评估的方法。研究分析了两个案例:三维战斗机(标准动态模型,SDM)和UT24 eVTOL模型。通过谐波振荡技术分析从垂直悬停到前向巡航的过渡阶段,以捕捉非稳态气动载荷和力矩。网格敏感性研究和多区域网格策略确保了模拟的准确性,而ANSYS Fluent有限体积求解器和耦合压力速度算法提供了高保真度的结果。动态导数来源于攻角、飞行路径和旋转运动的变化,实验和数值数据验证了该方法。研究结果为稳健控制设计和稳定性分析提供了有价值的见解,支持未来城市空中交通和航空航天工程的发展。总体而言,该方法在优化飞行器关键过渡阶段性能方面展现出巨大的潜力。这些结果为未来创新奠定了基础(预印本;同行评审状态请核对原文。)
With rapid urbanization and increasing population density, urban traffic congestion has become a critical issue, and traditional ground transportation methods are no longer sufficient to address it effectively. To tackle this challenge, the concept of Advanced Air Mobility (AAM) has emerged, aiming to utilize low-altitude airspace to establish a three-dimensional transportation system. Among various components of the AAM system, electric vertical take-off and landing (eVTOL) aircraft plays a pivotal role due to their flexibility and efficiency. However, the immaturity of Ultra Reliable Low Latency Communication (URLLC) technologies poses significant challenges to safety-critical AAM operations. Specifically, existing Stacked Intelligent Metasurfaces (SIM)-based eVTOL systems lack rigorous mathematical frameworks to quantify probabilistic delay bounds under dynamic air traffic patterns, a prerequisite for collision avoidance and airspace management. To bridge this gap, we employ network calculus tools to derive the probabilistic upper bound on communication delay in the AAM system for the first time. Furthermore, we formulate a complex non-convex optimization problem that jointly minimizes the probabilistic delay bound and the propagation delay. To solve this problem efficiently, we propose a solution based on the Block Coordinate Descent (BCD) algorithm and Semidefinite Relaxation (SDR) method. In addition, we conduct a comprehensive analysis of how various factors impact regret and transmission rate, and explore the influence of varying load intensity and total delay on th(预印本;同行评审状态请核对原文。)
阵风具有固有的随机性,可能显著影响飞行器的安全性和性能。本研究探讨了一个三维不确定性量化(UQ)问题,以探讨阵风和飞行条件中的不确定性如何影响Lift-Plus-Cruise eVTOL飞行器机翼的结构响应。分析采用了一个非稳态气动弹性模型,通过板方法气动求解器与壳体分析结构求解器的一维耦合来预测机翼在不同条件下的响应。此外,本文还比较评估了常用的非侵入式UQ方法,包括非侵入式多项式混沌、克里金法、蒙特卡洛法、单变量降维法和梯度增强单变量降维法。这些方法根据其在估计各种风险指标(均值、标准差和95百分位数)方面的有效性进行评估,这些风险指标涉及关键结构响应输出,如最大尖端位移和平均应变能。数值结果揭示了即使在相对较小的不确定输入范围内,结构响应输出也存在显著变化。这突显了系统对阵风和飞行条件不确定性的敏感性。此外,所实施的UQ方法的性能根据特定的风险指标和分析的量度兴趣而显著变化。(预印本;同行评审状态请核对原文。)
随着智能交通系统的发展和航空技术的进步,先进空中交通(AAM)的概念日益受到关注。本研究旨在通过基于轨迹的操作(TBO)提高城市空中交通(UAM)的操作安全性和服务质量。引入了一个多层操作风险评估模型,以捕捉飞机故障场景对关键城市实体的影响,包括地面人员、车辆和飞行中的无人机(无人飞行器)。基于此,设计了一个单机轨迹规划模型,以在eVTOL(电动垂直起降)飞机的性能约束下平衡操作风险和运输成本。使用带有安全缓冲区的定制轨迹规划算法来识别最有效的飞行路径。此外,提出一个多机调度优化模型,以减少延误并降低空中碰撞风险。实验结果表明,所提出的方案提高了效率和安全性,为UAM操作提供了实际解决方案。(预印本;同行评审状态请核对原文。)
传统的电池等效电路模型(ECMs)在预测高放电速率下的性能方面能力有限,其中锂耗尽区域可能会形成并导致电池端电压突然指数下降。在电动垂直起降(eVTOL)飞行器应用中,需要准确预测在这些条件下的性能,因为在故障场景下可能需要高放电电流,而无法提供这些电流可能是安全关键的。为了解决这一挑战,我们利用数据驱动建模方法,推导出一种对传统ECM的简洁补充,可以仅通过一个额外的状态来捕捉观察到的电压急剧下降。我们还提供了一种详细的方法来识别由此产生的模型参数,包括一个广泛的表征数据集以及一个良好的正则化目标函数公式。该模型通过一个包含超过150次飞行的新型数据集进行验证,涵盖了eVTOL飞行器的各种条件,使用一个应用特定且与安全相关的储备持续时间指标来量化准确性。该模型被证明可以预测着陆悬停能力,其误差均值和标准差分别为2.9秒和6.2秒,定义了模型在高放电电流下捕捉电池电压行为的能力。(预印本;同行评审状态请核对原文。)
Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence ma(预印本;同行评审状态请核对原文。)
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a self-organized vertiport arrival system based on deep reinforcement learning. The airspace around the vertiport is assumed to be circular, and the vehicles can freely operate inside. Each aircraft is considered an individual agent and follows a shared policy, resulting in decentralized actions that are based on local information. We investigate the development of the reinforcement learning policy during training and illustrate how the algorithm moves from suboptimal local holding patterns to a safe and efficient final policy. The latter is validated in simulation-based scenarios, including robustness analyses against sensor noise and a changing distribution of inbound traffic. Lastly, we deploy the final policy on small-scale unmanned aerial vehicles to showcase its real-world usability.(预印本;同行评审状态请核对原文。)
The rise of advanced air mobility (AAM) is expected to become a multibillion-dollar industry in the near future. Market-based mechanisms are touted to be an integral part of AAM operations, which comprise heterogeneous operators with private valuations. In this work, we study the problem of designing a mechanism to coordinate the movement of electric vertical take-off and landing (eVTOL) aircraft, operated by multiple operators each having heterogeneous valuations associated with their fleet, between vertiports, while enforcing the arrival, departure, and parking constraints at vertiports. Particularly, we propose an incentive-compatible and individually rational vertiport reservation mechanism that maximizes a social welfare metric, which encapsulates the objective of maximizing the overall valuations of all operators while minimizing the congestion at vertiports. Additionally, we improve the computational tractability of designing the reservation mechanism by proposing a mixed binary linear programming approach that leverages the network flow structure.(预印本;同行评审状态请核对原文。)
在开发电动垂直起降飞行器(eVTOL)以满足飞行功率和能量需求的过程中,电池负载和热管理系统质量是一个持续的挑战。克服这一问题的一个可能策略是采用优化技术,以获得满足结构和热要求的轻量电池组。在本研究中,使用水平集拓扑优化方法对高能量密度圆柱形电池组的结构电池组进行了优化。使用高保真度电化学模型预测电池在给定eVTOL飞行剖面下的发热情况。然后将电池发热的最坏情况作为弱耦合稳态热力有限元模型中用于优化的源项。优化问题的目标是最小化热合规性和结构合规性的加权和,受体积约束。该方法通过不同权重集的数值示例进行了演示。不同权重导致的优化结果进行了比较、讨论和评估,使用热和结构性能指标。优化后的电池组拓扑结构接受了瞬态热有限元分析,以评估电池组的热响应。(预印本;同行评审状态请核对原文。)