Experiments for Online Estimation of Heavy Vehicle’s Mass and Time-Varying Road Grade

In this paper application of recursive least squares with multiple forgetting factors is explained for online estimation of Heavy Duty Vehicle mass and road grade. The test data is obtained from highway experiments with a Freightliner truck. The experimental setup is explained in detail. This data is used to validate the longitudinal dynamics model of the truck. Then two distinct driving cycles are used to investigate the performance of the mass and grade estimation scheme. In he first scheme no gear shift occurs and the only concern is persistence of excitations. It is shown that if the excitations are persistent, mass and time-varying grade are estimated with good accuracy. In the second cycle gearshifts occur and the challenge is the unmodelled dynamics during gearshifts which cause large overshoots in the estimates. A method is proposed to circumvent this problem and good estimation results are shown with this provision.

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Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments

Vehicle System Dynamics ◽

10.1080/00423110412331290446 ◽

2005 ◽

Vol 43 (1) ◽

pp. 31-55 ◽

Cited By ~ 234

Author(s):

A. Vahidi ◽

A. Stefanopoulou ◽

H. Peng

Keyword(s):

Least Squares ◽

Recursive Least Squares ◽

Online Estimation ◽

Vehicle Mass ◽

Road Grade ◽

Theory And Experiments

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Using the equal-power-based MCE to transform driving cycles with road grade for testing heavy-duty vehicle fuel consumption on dynamometer

IEEE Access ◽

10.1109/access.2021.3111296 ◽

2021 ◽

pp. 1-1

Author(s):

Man Zhang ◽

Wendong Cheng ◽

Yunbo Shen

Keyword(s):

Fuel Consumption ◽

Heavy Duty ◽

Driving Cycles ◽

Heavy Duty Vehicle ◽

Road Grade ◽

Equal Power

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Optimization-based online estimation of vehicle mass and road grade: Theoretical analysis and experimental validation

Mechatronics ◽

10.1016/j.mechatronics.2021.102663 ◽

2021 ◽

Vol 80 ◽

pp. 102663

Author(s):

Andreas Ritter ◽

Fabio Widmer ◽

Basil Vetterli ◽

Christopher H. Onder

Keyword(s):

Theoretical Analysis ◽

Experimental Validation ◽

Online Estimation ◽

Vehicle Mass ◽

Road Grade

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Kernel Recursive Least-Squares Tracker for Time-Varying Regression

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2012.2200500 ◽

2012 ◽

Vol 23 (8) ◽

pp. 1313-1326 ◽

Cited By ~ 95

Author(s):

S. Van Vaerenbergh ◽

M. Lazaro-Gredilla ◽

I. Santamaria

Keyword(s):

Least Squares ◽

Recursive Least Squares ◽

Time Varying

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Slip-Clutch Torque Estimation via Real-Time Adaptive Lookup Table

ASME Letters in Dynamic Systems and Control ◽

10.1115/1.4052462 ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Wenpeng Wei ◽

Hussein Dourra ◽

Guoming Zhu

Keyword(s):

Friction Coefficient ◽

Real Time ◽

Mean Squared Error ◽

Surface Friction ◽

Lookup Table ◽

Recursive Least Squares ◽

Time Varying ◽

Squared Error ◽

Output Torque ◽

Clutch Torque

Abstract Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

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