Acceleration

Physics

Automotive engineering

Mathematics

Inertial measurement unit

Gene

Statistics

Inertial navigation system

Engineering

Inertial frame of reference

Estimator

Artificial intelligence

Control (management)

Chemistry

Sensor fusion

Computer science

Global Positioning System

Quantum mechanics

Vehicle dynamics

Simulation

Control theory (sociology)

Biochemistry

Robustness (evolution)

Classical mechanics

Telecommunications

IEEE Transactions on Vehicular Technology

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.

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