Distributed Drive Electric Vehicle Longitudinal Velocity Estimation with Adaptive Kalman Filter: Theory and Experiment
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is 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
|[Unnamed Dataset 1]|
- Jiang, F. and Gao, Z., “An Adaptive Nonlinear Filter Approach to the Vehicle Velocity Estimation for ABS,” presented at IEEE International Conference on Control Applications 2000, USA, September 27-27, 2000, doi: 10.1109/CCA.2000.897472.
- Chu, L., Shi, Y., Zhang, Y., Liu, H. et al., “Vehicle lateral and longitudinal velocity estimation based on Adaptive Kalman Filter,” presented at 2010 3rd International Conference on Advanced Computer Theory and Engineering, China, August 20-22, 2010, doi: 10.1109/ICACTE.2010.5579565.
- Villagra, J., D'Andrea-Novel, B., Fliess, M. and Mounier, H., “Estimation of Longitudinal and Lateral Vehicle Velocities: An Algebraic Approach,” presented on in 2008 American Control Conference, USA, June 11-13, 2008, doi: 10.1109/ACC.2008.4587108.
- Kobayashi, K., Cheok, K.C., and Watanabe, K., “Estimation of Absolute Vehicle Speed Using Fuzzy Logic Rule-Based Kalman filter,” presented at 1995 American Control Conference, USA, June 21-23, 1995, doi: 10.1109/ACC.1995.532084.
- Song, C., Uchanski, M., and Hedrick, J.K., “Vehicle Speed Estimation Using Accelerometer and Wheel Speed Measurements,” SAE Technical Paper 2002-01-2229, 2002, doi:10.4271/2002-01-2229.
- Bian, M., Sun, F., Chen, S., and Li, J., “Monitoring Longitudinal Vehicular Velocity by Using Driving Wheels Information,” Journal of Beijing Institute of Technology 11(3):251-255, 2002, doi:10.15918/j.jbit1004-0579.2002.03.006.
- Hsu, L.Y. and Chen, T.L., “Vehicle Full-State Estimation and Prediction System Using State Observers,” IEEE Transactions on Vehicular Technology 58(6):2651-2662, 2009, doi:10.1109/TVT.2008.2008811.
- Van Zanten, A.T., Erhardt, R., Pfaff, G. Kost, F. et al., “Control Aspects of the Bosch-VDC,” presented at International Symposium on Advanced Vehicle Control, Germany, 1996.
- M'Sirdi, N. K., Rabhi, A., Fridman, L., Davila, J. et al., “Second Order Sliding Mode Observer for Estimation of Velocities, Wheel Sleep, Radius and Stiffness,” presented at 2006 American Control Conference, USA, June 14-16, 2006, doi: 10.1109/ACC.2006.1657230.
- Alvarez, J.C., “Estimation of the Longitudinal and Lateral Velocities of a Vehicle Using Extended Kalman Filters,” Ph.D dissertation, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2006.
- Chu, L., Shi, Y., Zhang, Y., Ou, Y., et al., “Vehicle Velocity Estimation Based on Adaptive Kalman Filter,” presented at 2010 International Conference on Computer, China, August 24-26, 2010, doi: 10.1109/CMCE.2010.5610261.
- Li, B., Xiong, L., and Leng, B., “Adaptive Anti-Slip Regulation Method for Electric Vehicle with In-Wheel Motors Considering the Road Slope,” presented at 29th IEEE Intelligent Vehicles Symposium, China, June 26-29, 2018, doi: 10.1109/IVS.2018.8500399.