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Vehicle Load Estimation Using Recursive Total Least Squares for Rollover Detection
Technical Paper
2022-01-0914
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper will describe the development of a load estimation algorithm that is used to estimate the load parameters necessary to detect a vehicle’s proximity to rollover. When operating a vehicle near its handling limits or with large loads, vehicle rollover must be considered for safe operation. Vehicle mass and center of gravity (CG) height play a large role in a vehicle’s rollover propensity. Cargo and passenger vehicles operate under a range of load configurations; therefore, changes in load should be estimated. Researchers have often developed load estimation and rollover detection algorithms separately. This paper will develop a load estimation algorithm and use the load estimates and vehicle states to detect rollover. The load estimation algorithm uses total least squares and is broken into two parts. First, mass is estimated based on a “full-car” dynamic ride model. Next, the CG height and inertia are estimated using the previously estimated mass and a dynamic roll model. Least squares is a popular method for load estimation. Least Squares (LS) assumes that there is no measurement noise which is violated in this application. Total Least Squares (TLS) accounts for measurement noise and provides more accurate estimates when measurement noise is present. Simulated data from CarSim is used to produce sensor measurements. Inertial measurement unit (IMU) and suspension defection sensors are used to measure the appropriate vehicle states. Noise is added to each measurement. Accuracy of the load estimation will be discussed and compared to the least squares approach. Rollover detection using load estimates will be analyzed and compared to rollover detection that does not account for changes in load.
Topic
Citation
Hilyer, T. and Bevly, D., "Vehicle Load Estimation Using Recursive Total Least Squares for Rollover Detection," SAE Technical Paper 2022-01-0914, 2022, https://doi.org/10.4271/2022-01-0914.Also In
References
- Lapapong , S. December 2010
- Rhode , S. , and Gauterin , F. Vehicle Mass Estimation Using a Total Least-Squares Approach 2012 15th International IEEE Conference on Intelligent Transportation Systems 1584 89 2012 https://doi.org/10.1109/ITSC.2012.6338638
- Fathy , H.K. , Kang , D. , and Stein , J.L. Online Vehicle Mass Estimation Using Recursive Least Squares and Supervisory Data Extraction 2008 American Control Conference 1842 48 2008 https://doi.org/10.1109/ACC.2008.4586760
- Lin , N. , Zong , C. , and Shi , S. The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics Energies 12 1 2019 52 https://doi.org/10.3390/en12010052
- Reineh , M.S. , Enqvist , M. , and Gustafsson , F. IMU-Based Vehicle Load Estimation under Normal Driving Conditions 53rd IEEE Conference on Decision and Control 3376 81 2014 https://doi.org/10.1109/CDC.2014.7039912
- Rajamani , R. , Piyabongkarn , D. , Tsourapas , V. , and Lew , J.Y. Parameter and State Estimation in Vehicle Roll Dynamics IEEE Transactions on Intelligent Transportation Systems 12 4 December 2011 1558 1567 https://doi.org/10.1109/TITS.2011.2164246
- Pence , B.L. , Fathy , H.K. , and Stein , J.L. Sprung Mass Estimation for Off-Road Vehicles via Base-Excitation Suspension Dynamics and Recursive Least Squares 2009 American Control Conference, 5043-48 2009 https://doi.org/10.1109/ACC.2009.5160126
- Yu , W. , Zhang , X. , Guo , K. , Karimi , H.R. et al. Adaptive Real-Time Estimation on Road Disturbances Properties Considering Load Variation via Vehicle Vertical Dynamics Mathematical Problems in Engineering 2013 1 9 https://doi.org/10.1155/2013/283528
- Vukobratovic , M. and Borovac , B. Zero-Moment Point - Thirty Five Years of its Life International Journal of Humanoid Robotics 01 01 2004 157 173 10.1142/S0219843604000083
- Stankiewicz , P.G. , Brown , A.A. , and Brennan , S.N. Preview Horizon Analysis for Vehicle Rollover Prevention Using the Zero-Moment Point Journal of Dynamic Systems, Measurement, and Control 137 9 September 1, 2015 091002 https://doi.org/10.1115/1.4030390
- Jazar , R.N. Vehicle Dynamics: Theory and Applications 1st New York, NY Springer 2009
- Rajamani , R. Vehicle Dynamics and Control. Mechanical Engineering Series Boston, MA Springer US 2012 https://doi.org/10.1007/978-1-4614-1433-9
- Stengel , R.F. Optimal Control and Estimation Dover Publications 1994
- Markovsky , I. and Van Huffel , S. Overview of Total Least-Squares Methods Signal Processing 87 10 October 2007 2283 2302 https://doi.org/10.1016/j.sigpro.2007.04.004
- Rhode , S. Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation KIT Scientific Publishing 2018
- Board , T.R. The National Highway Traffic Safety Administration’s Rating System for Rollover Resistance Washington D.C. National Academy Press 2002
- Dukkipati , R.V. , Pang , J. , Qatu , M.S. , Sheng , G. et al. Road Vehicle Dynamics Warrendale, PA Society of Automotive Engineers, Inc 2008
- Ervin , R.D. , and Guy , Y. 1986
- Stankiewicz , P. 2015
- Groves , P.D. Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems Second. Artech 2013
- Bevly , D.M. , Ryu , J. , and Christian Gerdes , J. Integrating INS Sensors With GPS Measurements for Continuous Estimation of Vehicle Sideslip, Roll, and Tire Cornering Stiffness IEEE Transactions on Intelligent Transportation Systems 7 4 December 2006 483 493 https://doi.org/10.1109/TITS.2006.883110