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РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASAWith the widespread application of fully electric multi-rotor UAVs (Unmanned Aerial Vehicles), the overheating issue of high-power-density motors under high-load conditions has become a critical factor restricting performance enhancement and long-term stable operation capability. This paper addresses the challenges in cooling system design for UAV motors by proposing a simulation analysis method based on a lumped parameter thermal network model using motor design software SPEED and system simulation software AMESim. First, thermal analysis of the motor was conducted through SPEED software to generate a lumped parameter thermal network model. Then, utilizing the interface between AMESim and SPEED to obtain thermal network model information, an AMESim model was automatically generated for system simulation analysis. Through this methodology, the simulation model of the motor cooling system was successfully established, accompanied by a detailed temperature rise simulation analysis. By comparing the simulated temperature of the motor’s end windings with measured data, the average error is less than 7.6%, indicating high simulation accuracy and practical engineering applicability. Further simulation verification demonstrates that the designed cooling system effectively reduces the temperature at various nodes inside the motor, ensuring stable long-term operation under high-load conditions. The research outcomes not only improve modeling efficiency but also provide new insights for designing and optimizing motor cooling systems, contributing to enhanced performance and operational stability of fully electric-driven UAVs.
With the widespread application of fully electric multi-rotor UAVs (Unmanned Aerial Vehicles), the overheating issue of high-power-density motors under high-load conditions has become a critical factor restricting performance enhancement and long-term stable operation capability. This paper addresses the challenges in cooling system design for UAV motors by proposing a simulation analysis method based on a lumped parameter thermal network model using motor design software SPEED and system simulation software AMESim. First, thermal analysis of the motor was conducted through SPEED software to generate a lumped parameter thermal network model. Then, utilizing the interface between AMESim and SPEED to obtain thermal network model information, an AMESim model was automatically generated for system simulation analysis. Through this methodology, the simulation model of the motor cooling system was successfully established, accompanied by a detailed temperature rise simulation analysis. By comparing the simulated temperature of the motor’s end windings with measured data, the average error is less than 7.6%, indicating high simulation accuracy and practical engineering applicability. Further simulation verification demonstrates that the designed cooling system effectively reduces the temperature at various nodes inside the motor, ensuring stable long-term operation under high-load conditions. The research outcomes not only improve modeling efficiency but also provide new insights for designing and optimizing motor cooling systems, contributing to enhanced performance and operational stability of fully electric-driven UAVs.
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Xi’an Aerospace Propulsion Testing Technology Research Institute, Xi’an, 710025, China
Li Song
School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130022, China
Yiran Tao, Jichen Xie & Xin Wang
Authors
Correspondence to Xin Wang.
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Jianrong Tan
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Zhenyu Liu
Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Weifei Hu
© 2027 The Chinese Mechanical Engineering Society
Song, L., Tao, Y., Xie, J., Wang, X. (2027). Simulation Analysis of Motor Cooling System for Electric Driven UAV. In: Tan, J., Liu, Z., Hu, W. (eds) Advances in Mechanical Design. ICMD 2025. Mechanisms and Machine Science, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-95-7904-4_11
Download citationDOI: https://doi.org/10.1007/978-981-95-7904-4_11
Published: 25 June 2026
Publisher Name: Springer, Singapore
Print ISBN: 978-981-95-7903-7
Online ISBN: 978-981-95-7904-4
eBook Packages: Mechanical Engineering (R0)Springer Nature Proceedings excluding Computer Science