Development of a Novel Sensorless Longitudinal Road Gradient Estimation Method Based on Vehicle CAN Bus Data

Mangan, S.; Wang, Jihong

doi:10.1109/tmech.2007.897286

Cited by 29 publications

(23 citation statements)

References 3 publications

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“…Moreover, only parts of vehicle parameters are considered (e.g. mass and road gradient [6,8,9,12,14]), where other parameters are assumed to be known (e.g. rolling resistance coefficient in [14,15]) or negligible (e.g.…”

Section: Problem Formulationsmentioning

confidence: 99%

Adaptive estimation of road gradient and vehicle parameters for vehicular systems

Yang

Guo

2015

IET Control Theory & Applications

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To improve the vehicle driving performance, adaptive parameter estimation is studied to simultaneously estimate the road gradient, vehicle mass and other vehicle parameters, which requires the vehicle longitudinal velocity and driving force only. Different to conventional gradient and recursive least square methods, the parameter estimation error is obtained to drive the adaptive laws for estimating unknown parameters, where exponential convergence can be guaranteed under the classical persistently excitation condition. Moreover, finite-time parameter estimation is achieved by incorporating the sliding mode technique into the adaptive laws. The robustness of the proposed adaptations against bounded disturbances is studied. Simulation results illustrate that the presented methods can obtain faster transient and better steady-state performance than some available methods.

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Section: Problem Formulationsmentioning

confidence: 99%

Adaptive estimation of road gradient and vehicle parameters for vehicular systems

Yang

Guo

2015

IET Control Theory & Applications

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“…The next step is to calculate the derivative of the measured velocity in order to acquire the acceleration at the wheels. Mangan and Wang [1] differentiate the acceleration measured by the accelerometer and the acceleration measured at the wheels. The difference between these two measurements is due to the road slope.…”

Section: State Of the Artmentioning

confidence: 99%

“…Knowledge of the slope of the road is valuable because can optimise the performance or reduce the fuel consumption of modern vehicles in order to meet the constantly increasing demand for better and more efficient vehicles [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Road gradient detection using Kalman filter

et al. 2013

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Accurate, real-time knowledge of the road inclination is necessary for in-car electronic systems which are related to battery recharging system, electric motor, engine and gearbox control units. When the vehicle is moving, the measurement of the slope is not straightforward. The force measured by the inclination or acceleration sensors is coupled with the force due to the vehicle acceleration.This research examines the application of Kalman filter to estimate the road inclination. Furthermore, it compares the performance of the Kalman filter approach against the acceleration discrepancy method which is currently used by some car manufactures.

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“…Mangan et al presented a novel sensorless longitudinal road gradient estimation method, which was developed from a benchmark system design. The inclinometer and acceleration-based sensors were used to estimate the road grade information [15]. Hsu et al presented a full-state vehicle model with 6 degrees of freedom (DOFs) to predict the vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus

Sun

Zhang

Yan

et al. 2016

Mechanical Systems and Signal Processing

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Development of a Novel Sensorless Longitudinal Road Gradient Estimation Method Based on Vehicle CAN Bus Data

Cited by 29 publications

References 3 publications

Adaptive estimation of road gradient and vehicle parameters for vehicular systems

Adaptive estimation of road gradient and vehicle parameters for vehicular systems

Road gradient detection using Kalman filter

A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus

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