Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

The accurate acquisition of information regarding the state of a vehicle's driving is essential for the implementation of active safety control measures in vehicles. To tackle the challenge of accurately measuring the sideslip angle in distributed electric vehicles, this study proposes an optimized maximum correntropy square-root cubature Kalman filter based on African vulture optimization algorithm (AVOA-MCSCKF). This method aims to provide accurate estimation of the sideslip angle. The real-time estimation of the total vehicle mass is conducted through the application of forgetting factor recursive least squares method. Additionally, the African vulture algorithm is utilized to adaptively adjust MCSCKF. This adjustment aims to mitigate estimation inaccuracies stemming from the uncertain nature of the noise covariance matrix, ultimately leading to a more accurate estimation of the sideslip angle. In the collaborative simulation environment of Carsim/Simulink, the algorithm's accuracy and robustness are validated across various operational scenarios. The research findings indicate that AVOA-MCSCKF algorithm enhances the accuracy of sideslip angle estimation by a minimum of 51.8% when compared to both the standard covariance Kalman filter and square-root cubature Kalman filter filter. This approach effectively addresses the challenging estimation issue of the sideslip angle in distributed drive electric vehicles operating under complex conditions, thereby improving the vehicle's active safety.