电动起降飞机用电池多维健康指标联合估算

doi:10.7527/S1000-6893.2024.31440

Abstract

Abstract:

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.

Key words: electric Vertical Take-off and Landing (eVTOL) aircraft, lithium-ion battery, battery management system, health indicator estimation, least squares support vector regression

CLC Number:

V242

Jufeng YANG, Wenxin HUANG, Jiukang SUN, Zhongchen MA, Guodong FAN, Xi ZHANG. Joint estimation of multidimensional battery health indicators for electric vertical take-off and landing aircraft applications[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(14): 331440.

Figures/Tables 11

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电池号	容量		R_{1 s}		R_{30 s}
电池号	MAE/（mA·h）	MAPE/%	MAE/mΩ	MAPE/%	MAE/mΩ	MAPE/%
总体	32.0	1.20	0.50	1.98	0.57	1.61
VAH06	21.5	0.78	0.25	1.18	0.35	1.05
VAH10	46.0	1.77	0.53	1.94	0.76	2.21
VAH13	24.0	0.88	0.78	3.05	0.64	1.65
VAH27	26.2	0.94	0.30	1.33	0.29	0.83

模型输入	容量		R_{1 s}		R_{30 s}
模型输入	MAE/（mA·h）	MAPE/%	MAE/mΩ	MAPE/%	MAE/mΩ	MAPE/%
I_L	41.8	1.58	0.74	3.16	1.00	2.97
V_t	25.2	0.95	1.06	4.33	1.74	4.67
T	122	4.71	1.64	6.96	1.95	5.02
I_L+V_t+T	59.9	2.33	1.25	4.94	1.42	3.75
I_L+V_t	32.0	1.20	0.50	1.98	0.57	1.61

飞行阶段	容量		R_{1 s}		R_{30 s}
飞行阶段	MAE/（mA·h）	MAPE/%	MAE/mΩ	MAPE/%	MAE/mΩ	MAPE/%
起飞	32.0	1.20	0.50	1.98	0.57	1.61
巡航	35.3	1.35	0.71	2.79	0.75	1.96
降落	79.3	3.08	0.65	2.73	1.06	2.88

方法	容量		R_{1 s}		R_{30 s}		训练时间/s
方法	MAE/（mA·h）	MAPE/%	MAE/mΩ	MAPE/%	MAE/mΩ	MAPE/%	训练时间/s
ELM	46.4	1.75	0.61	2.65	0.90	2.60	5.60
MLP	50.9	1.93	0.55	2.26	0.88	2.54	6.57
SLS-SVR	45.8	1.73	0.58	2.42	0.79	2.28	0.008 1
MLS-SVR	32.0	1.20	0.50	1.98	0.57	1.61	0.015

Joint estimation of multidimensional battery health indicators for electric vertical take-off and landing aircraft applications

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[4]	XING Yalan, WANG Shengbin, ZHANG Shichao, WANG Wenxu. Research on New Three-dimensional Nanostructured Anode Materials for Lithium-ion Batteries [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(10): 2776-2783.
[5]	CHEN Xiongzi, YU Jinsong, TANG Diyin, WANG Yingxun. Probabilistic Residual Life Prediction for Lithium-ion Batteries Based on Bayesian LS-SVR [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(9): 2219-2229.