电动起降飞机用电池多维健康指标联合估算
收稿日期: 2024-10-23
修回日期: 2024-11-11
录用日期: 2024-12-14
网络出版日期: 2025-01-10
基金资助
国家自然科学基金(52307244);上海商用飞机系统工程科创中心联合研究基金(CASEF-2023-XM3);中国博士后科学基金(2023M742255)
Joint estimation of multidimensional battery health indicators for electric vertical take-off and landing aircraft applications
Received date: 2024-10-23
Revised date: 2024-11-11
Accepted date: 2024-12-14
Online published: 2025-01-10
Supported by
National Natural Science Foundation of China(52307244);Shanghai Center for Systems Engineering of Commercial Aircraft Joint Research Foundation(CASEF-2023-XM3);China Postdoctoral Science Foundation(2023M742255)
电动垂直起降飞机（eVTOL）是绿色低空飞行器发展的重要趋势之一。为满足eVTOL安全可靠运行需求，需要电池管理系统结合飞行工况特性准确高效估算机载锂离子电池系统的健康状态。目前，大部分面向eVTOL应用的电池健康状态估计算法仅考虑了电池容量损失，缺乏根据飞行工况数据同时对电池能量衰退及功率衰减进行全面精准感知。针对上述问题，提出使用eVTOL起飞阶段电池电流和电压数据集作为估计算法输入参数，并选取电池容量、欧姆内阻和极化内阻作为健康指标综合表征电池性能衰退。为了提高多指标估算问题中模型的训练效率和估算精度，提出了一种基于多输出最小二乘支持向量回归的电池多健康指标联合估算方法，通过考虑输出变量间的耦合关系实现电池容量和内阻参数的快速准确估算。最后，基于eVTOL电池数据集进行了实验验证。对比分析结果表明：基于该方法估算所得电池容量和内阻的总体平均绝对百分比误差均低于2.5%，且模型训练时间控制在0.05 s以内，成功实现了面向eVTOL应用锂离子电池健康指标的全面、高效和准确估算。
关键词： 电动垂直起降飞机; 锂离子电池; 电池管理系统; 健康指标估算; 最小二乘支持向量回归
杨驹丰 , 黄文新 , 孙久康 , 马忠臣 , 范国栋 , 张希 . 电动起降飞机用电池多维健康指标联合估算[J]. 航空学报, 2025 , 46(14) : 331440 -331440 . DOI: 10.7527/S1000-6893.2024.31440
Electric Vertical Take-off and Landing （eVTOL） aircraft is one of the significant trends in the development of renewable low-altitude aircraft. To meet the requirements for safe and reliable operation of eVTOL， it is necessary for the battery management system to accurately and efficiently estimate the State-of-Health （SoH） of the on-board lithium-ion battery system based on the characteristics of the flight profile. At present， most SoH estimation algorithms for eVTOL applications only consider the battery capacity loss， and lack the ability to comprehensively and precisely estimate battery degradation in both energy and power based on the data recorded during the specific flight phase. To address the aforementioned issue， the battery current and voltage measured during the take-off phase of the eVTOL aircraft are selected as the inputs for the estimation algorithm， and the battery capacity， the ohmic resistance， and the polarization resistance are utilized as the health indicators to comprehensively characterize the battery degradation. To improve the training efficiency and estimation accuracy of the model for multi-indicator estimation， a multi-output least squares support vector regression-based method for joint estimation of multiple battery health indicators is proposed， which can achieve rapid and accurate estimation of the battery capacity and resistance by considering the coupling relationship among output parameters. Finally， experimental verification is conducted based on the eVTOL Battery Dataset. The results of the comparative study demonstrate that based on the proposed method， the mean absolute percentage error of the estimated battery capacity and internal resistance are overall below 2.5% and the model training time is within 0.05 s， successfully realizing a comprehensive， efficient， and accurate estimation of lithium-ion battery health indicators for eVTOL applications.
| [1] | YANG X G， LIU T， GE S H， et al. Challenges and key requirements of batteries for electric vertical takeoff and landing aircraft［J］. Joule， 2021， 5（7）： 1644-1659. |
| [2] | 苏宁， 黄文新. 电推进飞机定子双绕组感应发电机并联系统［J］. 航空学报， 2022， 43（8）： 325409. |
| SU N， HUANG W X. Parallel power generation system based on dual-stator winding induction generator for electric propulsion aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（8）： 325409 （in Chinese）. | |
| [3] | MITICI M， HENNINK B， PAVEL M， et al. Prognostics for lithium-ion batteries for electric vertical take-off and landing aircraft using data-driven machine learning［J］. Energy and AI， 2023， 12： 100233. |
| [4] | 邓景辉. 电动垂直起降飞行器的技术现状与发展［J］. 航空学报， 2024， 45（5）： 529937. |
| DENG J H. Technical status and development of electric vertical take-off and landing aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（5）： 529937 （in Chinese）. | |
| [5] | 赵冬冬， 赵国胜， 夏磊， 等. 无人机用燃料电池阴极供气系统建模与控制［J］. 航空学报， 2021， 42（7）： 324659. |
| ZHAO D D， ZHAO G S， XIA L， et al. Modeling and control of fuel cell cathode gas supply system for UAV［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（7）： 324659 （in Chinese）. | |
| [6] | 王超君， 陈翔， 彭思侃， 等. 锂离子电池发展现状及其在航空领域的应用分析［J］. 航空材料学报， 2021， 41（3）： 83-95. |
| WANG C J， CHEN X， PENG S K， et al. Recent advances in lithium-ion batteries and their applications towards aerospace?［J］. Journal of Aeronautical Materials， 2021， 41（3）： 83-95 （in Chinese）. | |
| [7] | 刘文学， 侯聪， 杨亚联， 等. 面向城市空中交通的电动飞行汽车关键性能指标分析［J］. 机械工程学报， 2024， 60（22）： 257-275. |
| LIU W X， HOU C， YANG Y L， et al. Analysis of key performance metrics of electric flying cars for urban air mobility［J］. Journal of Mechanical Engineering， 2024， 60（22）： 257-275 （in Chinese）. | |
| [8] | VALI? D， HLINKA J， FORBELSKá M， et al. Perspective study on charge time measurement of long-term stored lithium-ion batteries used in electric-powered aircraft assessed and modelled by specific growth model with diffusion process backup［J］. Journal of Energy Storage， 2024， 80： 110385. |
| [9] | BISHT A， AMIN R， DIXIT M， et al. Impact of cycling conditions on lithium-ion battery performance for electric vertical takeoff and landing applications?［J］. Journal of Power Sources， 2024， 602： 234335. |
| [10] | HATHERALL O， BARAI A， NIRI M F， et al. Novel battery power capability assessment for improved eVTOL aircraft landing?［J］. Applied Energy， 2024， 361： 122848. |
| [11] | WEI Z B， RUAN H K， LI Y， et al. Multistage state of health estimation of lithium-ion battery with high tolerance to heavily partial charging?［J］. IEEE Transactions on Power Electronics， 2022， 37（6）： 7432-7442. |
| [12] | MOHTAT P， LEE S， SIEGEL J B， et al. Comparison of expansion and voltage differential indicators for battery capacity fade?［J］. Journal of Power Sources， 2022， 518： 230714. |
| [13] | YANG J F， CAI Y F， MI C. Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario?［J］. Energy， 2022， 241： 122879. |
| [14] | GONG D L， GAO Y， KOU Y L， et al. State of health estimation for lithium-ion battery based on energy features［J］. Energy， 2022， 257： 124812. |
| [15] | YANG J F， LI X， SUN X D， et al. An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time?［J］. Energy， 2023， 263： 125743. |
| [16] | YANG J F， XIA B， HUANG W X， et al. Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis?［J］. Applied Energy， 2018， 212： 1589-1600. |
| [17] | YANG J F， CAI Y F， MI C C. State-of-health estimation for lithium-ion batteries based on decoupled dynamic characteristic of constant-voltage charging current?［J］. IEEE Transactions on Transportation Electrification， 2022， 8（2）： 2070-2079. |
| [18] | LUO Y W， QIAN Y P， ZENG Z Z， et al. Simulation and analysis of operating characteristics of power battery for flying car utilization［J］. eTransportation， 2021， 8： 100111. |
| [19] | BILLS A， SRIPAD S， FREDERICKS L， et al. A battery dataset for electric vertical takeoff and landing aircraft［J］. Scientific Data， 2023， 10（1）： 344. |
| [20] | GRANADO L， BEN-MARZOUK M， SOLANO SAENZ E， et al. Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications［J］. Journal of Power Sources， 2022， 548： 232051. |
| [21] | 廖龙飞. 多输出支持向量机及其应用研究［D］. 北京： 北京化工大学， 2011： 6-7. |
| LIAO L F. Research on multi-output support vector machine and its application［D］. Beijing： Beijing University of Chemical Technology， 2011： 6-7 （in Chinese）. | |
| [22] | 杨驹丰. 基于等效电路模型的锂离子动力电池参数辨识与状态估计［D］. 南京： 南京航空航天大学， 2019： 37-38. |
| YANG J F. Parameter identification and state estimation of lithium-ion power battery based on equivalent circuit model［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2019： 37-38 （in Chinese）. | |
| [23] | ZHENG Y S， CHE Y H， HU X S， et al. Sensorless temperature monitoring of lithium-ion batteries by integrating physics with machine learning［J］. IEEE Transactions on Transportation Electrification， 2024， 10（2）： 2643-2652. |
| [24] | 戴鹏， 周平， 梁延灼， 等. 基于多输出最小二乘支持向量回归建模的自适应非线性预测控制及应用［J］. 控制理论与应用， 2019， 36（1）： 43-52. |
| DAI P， ZHOU P， LIANG Y Z， et al. Multi-output least squares support vector regression modeling based adaptive nonlinear predictive control and its application［J］. Control Theory & Applications， 2019， 36（1）： 43-52 （in Chinese）. | |
| [25] | XU S， AN X， QIAO X D， et al. Multi-output least-squares support vector regression machines［J］. Pattern Recognition Letters， 2013， 34（9）： 1078-1084. |
| [26] | 马忠臣. 集成剪枝评估准则及PS-ELMs模型在时间序列预测中的应用研究［D］. 南京： 南京航空航天大学， 2014： 17-18. |
| MA Z C. Research on the application of integrated pruning evaluation criterion and PS-ELMs model in time series prediction［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2014： 17-18 （in Chinese）. |
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