重载货运列车的在线重量预估方法OA

This study proposes an online weight estimation method based on Recursive Least Squares(RLS)targeting at the technical challenges in real-time identification of dynamic train weight for freight trains.In terms of methodological design,this paper firstly artificially establishes a deterministic mapping relationship between traction force and acceleration during the open loop control phase of traction acceleration by actively maintaining a constant traction notch position,to create a stable dynamic observation window for precise identification of train weight parameters.Secondly,it integrates real-time train operational data under the existing software framework for autonomous driving to couple the traditional dynamics modeling,control algorithms,and parameter identification processes.Finally,this paper carries out iterative updates through RLS to continuously optimize weight estimates until convergence.To validate the effectiveness of the proposed method,a MATLAB-based simulation platform is established by using SS4B locomotives hauling multiple C80 wagons.The experimental results demonstrate that this method maintains weight estimation errors within±1%accuracy even under test scenarios with±50%formation errors.