Sprung mass estimation for off-road vehicles via base-excitation suspension dynamics and recursive least squares

Pence, Benjamin L.; Fathy, Hosam K.; Stein, Jeffrey L.

doi:10.1109/acc.2009.5160126

Cited by 23 publications

(10 citation statements)

References 9 publications

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“…In this paper a discrete-time filter is formulated using an Euler approximation for the following state vector at time k. (6) v x is longitudinal vehicle velocity while θ 1 and θ 2 are defined in equation (5). The system propagates in one time step as (7) with differentiate state transition model (8) and process noise (9) T s is the sampling period, and process noise w(k) is assumed to be zero-mean with diagonal covariance matrix Q. Velocity is the observable quantity, thus the observation model is (10) The observation matrix H is [1 0 0] and v(k + 1) represents observation noise with zero mean and covariance R. The model described in equations (7,8,9,10,11) may be implemented with the extended Kalman filter. A thorough explanation of the filter, including linearization and equations for the prediction and update step, is found in [13].…”

Section: Extended Kalman Filter (Ekf)mentioning

confidence: 99%

“…This avoids redundant computations and conflicting parameter estimates, which is crucial when a supervisory controller is coordinating the behavior of multiple control systems [6]. While there have been numerous studies for inertial load estimation which employ various prediction methods and modeling approaches [3,7,8,9,10], many previous efforts toward on-line mass and grade estimation using existing on-board sensor systems have been based on vehicle longitudinal dynamics models due to the many common driving scenarios for which such models apply. Estimation approaches include recursive least squares (RLS) with multiple forgetting factors [11,12,13], extended Kalman filtering (EKF) [5], a dynamic grade observer (DGO) [14] requiring only longitudinal acceleration and an estimate of powertrain torque, and grade estimation using kinematic information provided by a longitudinal accelerometer [15].…”

Section: Introductionmentioning

confidence: 99%

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Methods in Vehicle Mass and Road Grade Estimation

Kidambi¹,

Harne²,

Fujii³

et al. 2014

SAE Int. J. Passeng. Cars - Mech. Syst.

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Dynamic vehicle loads play critical roles for automotive controls including battery management, transmission shift scheduling, distance-to-empty predictions, and various active safety systems. Accurate real-time estimation of vehicle loads such as those due to vehicle mass and road grade can thus improve safety, efficiency, and performance. While several estimation methods have been proposed in literature, none have seen widespread adoption in current vehicle technologies despite their potential to significantly improve automotive controls. To understand and bridge the gap between research development and wider adoption of real-time load estimation, this paper assesses the accuracy and performance of four estimation methods that predict vehicle mass and/or road grade. These include recursive least squares (RLS) with multiple forgetting factors; extended Kalman filtering (EKF); a dynamic grade observer (DGO); and a method developed by this research: parallel mass and grade (PMG) estimation using a longitudinal accelerometer. The estimation methods and models were constructed, numerous vehicle tests were performed, and data was evaluated off-line by the estimation approaches. It is found that RLS and EKF yield estimates within 5% of their actual values if provided initial values close to true initial states. To improve estimation when inaccurate initial values are provided, a mass selection algorithm is proposed that determines mass based on converged values from concurrently-operating EKF estimators. Its potential for accurate mass and grade estimation is demonstrated. PMG estimation provides the most reliable and accurate results, and demonstrates the greatest potential for successful real-time implementation to advance the performance, economy, and reliability of future vehicle controls.

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Section: Extended Kalman Filter (Ekf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methods in Vehicle Mass and Road Grade Estimation

Kidambi¹,

Harne²,

Fujii³

et al. 2014

SAE Int. J. Passeng. Cars - Mech. Syst.

View full text Add to dashboard Cite

show abstract

“…Pence et al [8] estimated the sprung mass of an off-road vehicle using RLS method. By considering the sprung and unsprung mass accelerations in a quarter car model, the mass was estimated within 8% error, under 100s.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation

Büyükköprü¹,

Uzunsoy²

2021

Journal of Innovative Science and Engineering (JISE)

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“…Best and Gordon [9] developed an Extended Kalman Filter for estimation of vehicle states and parameters. Pence et al [10] used a quarter-car model and a base excitation concept to identify mass of an off-road vehicle using a Least Squares estimator with measurement of accelerations of sprung and unsprung masses. Bae et al [11] proposed an RLS identifier for estimation of vehicle mass and aerodynamic drag.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on identification of vehicle inertial parameters

Zarringhalam

Rezaeian

Melek

et al. 2012

2012 American Control Conference (ACC)

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This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.

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Sprung mass estimation for off-road vehicles via base-excitation suspension dynamics and recursive least squares

Cited by 23 publications

References 9 publications

Methods in Vehicle Mass and Road Grade Estimation

Methods in Vehicle Mass and Road Grade Estimation

Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation

A comparative study on identification of vehicle inertial parameters

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