Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information

Abstract

Accurate and reliable estimation information of sideslip angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of sideslip angle estimation of an autonomous vehicle, a sideslip angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive sideslip angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

Energy sharing mechanisms for multi prosumers considering privacy protection

“…The model proposed in this paper contains four prosumers, which contain DG, ESS, FL, PV and WT, respectively. The predicted power of load and new energy is referenced [4] and electricity price information is referred to [8]. The results of the arithmetic example analysis are presented below.…”

“…The synergistic operation architecture of multi prosumers can be divided into centralized trading model and decentralized trading model according to the existence of central institutions [6]. In the centralized trading model, there exists a third-party coordination center to coordinate the planning of prosumers and achieve overall unified scheduling, such as virtual power plant operators [7], electric vehicle aggregators [8], and energy management and control centers for smart buildings [9]. The centralized trading model has the advantages of simple model and efficient solution.…”

Extended kalman filter based state estimation of formula student autonomous racing

“…As racing cars need to maintain a high speed in the race, how to accurately and real-time estimate the status information of the vehicle at high speed is one of the keys to realize driverless. Currently, longitudinal velocity and yaw rate can be easily measured by sensors, but other vehicle state parameters such as the sideslip angle are still difficult to measure directly due to the high cost of sensors and the limitations of sensor technology [1][2][3] . Kalman filtering is a common state estimation method, which can effectively reduce the influence of noise in the observation process and get the optimal estimate of the system state.…”