Precise Fitting Algorithm for Speed Performance Curve of Medium-Low Speed Maglev Train Based on On-Site Testing Operation Data and Kalman Filtering

Medium-low speed maglev, as a more comfortable type of medium volume urban rail transit system, has its own advantages. As the core entity carrying passengers, the acceleration and deceleration performance of maglev train undoubtedly has an important impact on the performance of the whole medium-low speed maglev system. The speed curve characteristics of the train under different operation conditions are the important design basis of the operation control system (OCS). Therefore, it is of great significance to study the accurate acceleration and deceleration performance of medium-low speed maglev train for improving the design of OCS and even the whole medium-low speed maglev system. Till now, most scholars have just focused on train speed profile optimization, and there are seldom considerations about the accuracy of the original train speed profile. In this paper, a fitting algorithm for speed performance curve of medium-low speed maglev train employing on-site testing operation data has been proposed, and based on Kalman filtering, the train speed curve with high confidence level under different working conditions can be precisely fitted.