Longitudinal Vehicle Speed Estimation for Four-Wheel-Independently-Actuated Electric Vehicles Based on Multi-Sensor Fusion

In this paper, an enabling multi-sensor fusion-based longitudinal vehicle speed estimator is proposed for four-wheel-independently-actuated electric vehicles using a Global Positioning System and Beidou Navigation Positioning (GPS-BD) module, and a low-cost Inertial Measurement Unit (IMU). For accurate vehicle speed estimation, an approach combing the wheel speed and the GPS-BD information is firstly put forward to compensate for the impact of road gradient on the output horizontal velocity of the GPS-BD module, and the longitudinal acceleration of the IMU. Then, a multi-sensor fusion-based longitudinal vehicle speed estimator is synthesized by employing three virtual sensors which generate three longitudinal vehicle speed tracks based on multiple sensor signals. Finally, the accuracy and reliability of the proposed longitudinal vehicle speed estimator are examined under a diverse range of driving conditions through hardware-in-the-loop tests. The results show that the proposed method has high estimation accuracy, robustness, and real-time performance.