State Estimation Method for Distributed Electric Buses Using a Square Root H-∞ Filtering Algorithm

During the driving process of a distributed drive electric bus, due to complex nonlinear, strong coupling conditions, and high-frequency ride-on/drop-off phenomena, its dynamic model has high-dimensional nonlinearity and non-Gaussian characteristics, so it cannot accurately control key control states in real time. Parameter Estimation. Aiming at this problem, this paper first proposes a square root <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}\infty$</tex> volumetric Kalman filter (SRCHIF) algorithm based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}\infty$</tex> filter and volumetric Kalman filter (CKF) to solve the longitudinal and lateral vehicle speed, roll angular velocity, The problem of high-dimensional nonlinearity of the model and non-Gaussian noise caused by time-varying key motion state parameters such as yaw rate; then, based on the state quantities calculated by the SRCHIF algorithm, the inertial parameters such as the mass of the vehicle, the position of the center of mass, and the load distribution coefficient are calculated. Estimation forms a two-level joint estimation framework of motion state-inertial parameters; finally, the proposed state estimation algorithm framework is verified by using Trucksim and Matlab joint simulation. The results show that the proposed framework can accurately and robustly estimate the vehicle's driving state and inertia parameters, and provide a solid foundation for the development of control algorithms for distributed drive electric buses.