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Distributed Drive Electric Vehicle Longitudinal Velocity Estimation with Adaptive Kalman Filter: Theory and Experiment
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Velocity is one of the most important inputs of active safety systems such as ABS, TCS, ESC, ACC, AEB et al. In a distributed drive electric vehicle equipped with four in-wheel motors, velocity is hard to obtain due to all-wheel drive, especially in wheel slipping conditions. This paper focus on longitudinal velocity estimation of the distributed drive electric vehicle. Firstly, a basic longitudinal velocity estimation method is built based on a typical Kalman filter, where four wheel speeds obtained by wheel speed sensors constitute an observation variable and the longitudinal acceleration measured by an inertia moment unit is chosen as input variable. In simulations, the typical Kalman filter show good results when no wheel slips; when one or more wheels slip, the typical Kalman filter with constant covariance matrices does not work well. Therefore, a gain matrix adjusting Kalman filter which can detect the wheel slip and cope with that is proposed. Simulations are carried out in different conditions, including no wheel slips, one wheel slips, all wheel slip, passing a bump, and variable acceleration drive, and the results show that wheel slip has very little impact on estimation velocity. On-road experiments, including drive with sudden acceleration and deceleration, pass a bump, and accelerate on wet tile road, show satisfying results. On wet tile road, where the maximum slip rate is larger than 0.9, the velocity estimation error converges to within 5% in one second and to zero at last.
CitationZhang, Y., Leng, B., Xiong, L., Yu, Z. et al., "Distributed Drive Electric Vehicle Longitudinal Velocity Estimation with Adaptive Kalman Filter: Theory and Experiment," SAE Technical Paper 2019-01-0439, 2019, https://doi.org/10.4271/2019-01-0439.
Data Sets - Support Documents
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