Abstract The cryostat equipped with superconducting materials is one of the most important component of high-temperature superconducting maglev vehicles. However, due to the multiple energy disturbance modes in the cryostat, such as AC losses of superconductors, vibration of the cryostat and so on, the evaporation of liquid nitrogen (LN2) will be stimulated to accelerate, which will seriously threaten the vehicle safety. Therefore, this paper designs an intelligent controller which can realize the function of auto-filling LN2 based on the Kalman filter (KF) algorithm. Moreover, in this publication the corresponding methodology will be described as well in detail. Initially, the platinum resistance sensors were used to study the relationship between the temperature and LN2 level, meanwhile, the system state equation and observation equation were established. Then, the KF algorithm was introduced to estimate the true LN2 level. Based on the feedback signal of LN2 level, the system utilizes the STM32 microprocessor as control chip to control the solenoid valves and realizes the function of auto-filling. The controller has been implemented and the total time for filling one cryostat was reduced from 86 minutes to 16 minutes. This result is very encouraging and inspiring for the practical application of HTS maglev systems.

中文翻译：

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基于卡尔曼滤波算法的高温超导磁悬浮车液氮自动加注设计与实现

摘要 搭载超导材料的低温恒温器是高温超导磁悬浮车辆的重要组成部分之一。然而，由于低温恒温器中存在多种能量扰动模式，如超导体交流损耗、低温恒温器振动等，会刺激液氮（LN2）蒸发加速，严重威胁车辆安全。因此，本文设计了一种基于卡尔曼滤波器（KF）算法的智能控制器，可以实现自动填充LN2的功能。此外，在本出版物中也将详细描述相应的方法。最初，铂电阻传感器用于研究温度与 LN2 水平之间的关系，同时，建立了系统状态方程和观测方程。然后，引入 KF 算法来估计真实的 LN2 水平。该系统基于LN2电平的反馈信号，利用STM32微处理器作为控制芯片来控制电磁阀，实现自动加注功能。控制器已经实施，填充一个低温恒温器的总时间从 86 分钟减少到 16 分钟。这一结果对高温超导磁悬浮系统的实际应用非常鼓舞人心。控制器已经实施，填充一个低温恒温器的总时间从 86 分钟减少到 16 分钟。这一结果对高温超导磁悬浮系统的实际应用非常鼓舞人心。控制器已经实施，填充一个低温恒温器的总时间从 86 分钟减少到 16 分钟。这一结果对高温超导磁悬浮系统的实际应用非常鼓舞人心。