Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Jiangyi Lv, Hongwen He, Wei Liu, Yong Chen and Fengchun Sun
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Jiangyi Lv: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Hongwen He: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Wei Liu: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Yong Chen: School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Fengchun Sun: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

Energies, 2019, vol. 12, issue 7, 1-24

Abstract: Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.

Keywords: electric vehicle; vehicle state estimation; kinematic model; data fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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