• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
ZHOU Guohua, XUE Ning, BI Qiang. Improved and Fast Global Maximum Power Point Tracking Algorithm of Photovoltaic Power Generation System[J]. Journal of Southwest Jiaotong University, 2024, 59(6): 1266-1274. doi: 10.3969/j.issn.0258-2724.20220863
Citation: CHEN Qinghua, GE Xin, WANG Kaiyun. Identification of Wheel-Rail Vertical Forces of Rail Vehicles Based on Square Root Cubature Kalman Filter Algorithm[J]. Journal of Southwest Jiaotong University, 2025, 60(2): 403-410. doi: 10.3969/j.issn.0258-2724.20230048

Identification of Wheel-Rail Vertical Forces of Rail Vehicles Based on Square Root Cubature Kalman Filter Algorithm

doi: 10.3969/j.issn.0258-2724.20230048
  • Received Date: 10 Feb 2023
  • Rev Recd Date: 10 May 2023
  • Available Online: 15 Jan 2025
  • Publish Date: 11 May 2023
  • The wheel-rail interaction force is the key index to evaluate the operation quality of rail vehicles. An identification method based on the square root cubature Kalman filter (SRCKF) algorithm was proposed for online monitoring of the wheel-rail vertical force of rail vehicles. By taking the vehicle-track vertical coupled dynamics model considering the nonlinearity of suspension elements as an example, a nonlinear process function of wheel-rail vertical forces and state variables of vehicle components was established. The vertical accelerations of the vehicle body, frame, and wheelset were adopted as observations, and the SRCKF algorithm was employed for the recursive estimation of wheel-rail vertical forces. Furthermore, a vehicle dynamics model and its corresponding wheel-rail vertical force estimation model of 17 degrees of freedom were established to identify the vehicle’s left and right wheel-rail vertical forces under actual irregularity excitation. Simulation results indicate that the proposed method can precisely identify wheel-rail vertical forces of vehicles excited by the random irregularity, rail corrugation irregularity, and rail weld irregularity, with the identified value of wheel-rail vertical forces is in good agreement with the simulation value in both time domain and frequency domain. The correlation coefficients are 0.988, 0.999, and 0.969, respectively. When the proposed method is used to identify the vehicle’s wheel-rail vertical force, the lowest correlation coefficients of the left and right wheel-rail vertical forces are 0.747 and 0.720, respectively, and the correlation coefficient of the sum of the left and right wheel-rail vertical forces is 0.999.

     

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