TY - GEN
T1 - Research on Longitudinal Speed Estimation Methods for Multi-wheel Skidding of Multi-axle Distributed Electric Vehicles Based on Wheel Speed Prediction
AU - Tong, Mingyang
AU - Li, Junqiu
AU - Yang, Yongxi
AU - Li, Xiaohan
AU - Wang, Wuze
N1 - Publisher Copyright:
© The Author(s) 2026.
PY - 2026
Y1 - 2026
N2 - In order to solve the problem of estimating the longitudinal speed caused by wheel slippage of multi-axle distributed electric vehicles under off-road conditions, a longitudinal vehicle speed fusion estimation algorithm is proposed. Based on triple exponential smoothing and a long short-term memory network, the wheel speed is predicted, and an interactive multi-model method is used to fuse the output prediction results. In this paper, a noise covariance matrix adjusting method based on the trend of wheel speed change is adopted, which the actively adjusted Kalman filter (AAKF) for speed estimation is realized. The acceleration signal is not used ensuring the availability of the AAKF when the inertial measurement unit fails. The AAKF vehicle speed estimation results are fused to ensure the reliability of the algorithm. The simulation and real vehicle test results verify the accuracy of the proposed algorithm.
AB - In order to solve the problem of estimating the longitudinal speed caused by wheel slippage of multi-axle distributed electric vehicles under off-road conditions, a longitudinal vehicle speed fusion estimation algorithm is proposed. Based on triple exponential smoothing and a long short-term memory network, the wheel speed is predicted, and an interactive multi-model method is used to fuse the output prediction results. In this paper, a noise covariance matrix adjusting method based on the trend of wheel speed change is adopted, which the actively adjusted Kalman filter (AAKF) for speed estimation is realized. The acceleration signal is not used ensuring the availability of the AAKF when the inertial measurement unit fails. The AAKF vehicle speed estimation results are fused to ensure the reliability of the algorithm. The simulation and real vehicle test results verify the accuracy of the proposed algorithm.
KW - Actively adjusted Kalman filter
KW - Fusion estimation
KW - Longitudinal speed
KW - Multi-axle distributed electric vehicles
UR - https://www.scopus.com/pages/publications/105019495593
U2 - 10.1007/978-981-96-5527-4_42
DO - 10.1007/978-981-96-5527-4_42
M3 - Conference contribution
AN - SCOPUS:105019495593
SN - 9789819655298
T3 - Lecture Notes in Mechanical Engineering
SP - 525
EP - 539
BT - The 9th International Conference on Advances in Construction Machinery and Vehicle Engineering - ICACMVE 2024
A2 - Saman, Halgamuge
A2 - Peng, Yan
A2 - Zhao, Dingxuan
A2 - Bian, Yongming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Advances in Construction Machinery and Vehicle Engineering, ICACMVE 2024
Y2 - 7 November 2024 through 10 November 2024
ER -