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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.

In order to solve the problem of motor performance degradation caused by torque ripple and large cogging torque of in-wheel permanent magnet synchronous motor for electric vehicles, Taguchi is used to optimize the magnetic pole of built-in Permanent magnet synchronous motor, and the orthogonal optimization matrix of magnetic pole parameters is established. Through the parameter optimization matrix, the workload of pole optimization is reduced and the optimization speed is improved. The optimization results show that the orthogonal matrix optimization can not only improve the output torque of permanent magnet synchronous motor, but also further control its torque ripple and cogging. The orthogonal matrix optimization method used in this paper for V-I type built-in permanent magnet synchronous motor has a certain positive significance, and provides a certain reference value for the subsequent motor performance optimization.