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

2020/09/23 by Xiaolin Ding, Zhenpo Wang, Lei Zhang +1 · 200 citations
Engineering · #Acceleration #Artificial intelligence #Automotive engineering #Autonomous Vehicle Technology and Safety #Computer science #Control theory (sociology) #Engineering #Estimator #Global Positioning System #Inertial frame of reference #Inertial measurement unit #Inertial navigation system #Real-time simulation and control systems #Robustness (evolution) #Sensor fusion #Simulation #Vehicle Dynamics and Control Systems #Vehicle dynamics

paper · doi:10.1109/tvt.2020.3026106

published in IEEE Transactions on Vehicular Technology 69(11), 12797-12806 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2020/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

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.

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