Longitudinal road gradient estimation using vehicle CAN bus data

Mangan, S.; Wang, J.

doi:10.1109/icsmc.2003.1244233

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…The data will include information such as velocity, drivetrain ratio, engine torque and torque delivered at the wheels (see Fig 2). In addition to this data, Mangan and Wang [1] and Mangan et al [2] use information such as air drag coefficient and frictions to calculate the slope of the road. It is noteworthy, that this method does not use an accelerometer to acquire information about the acceleration.…”

Section: State Of the Artmentioning

confidence: 99%

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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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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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“…A gradient estimation algorithm using just CAN bus data and common sensor data can be seen in Ref. [4]. It was proved this algorithm can give a good result in normal cases.…”

Section: Introductionmentioning

confidence: 97%

Application of neural network to downhill shift strategy for automatic transmission

Chen

2010

J. Shanghai Jiaotong Univ. (Sci.)

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A method is proposed to estimate the longitudinal road gradient with a concept "general gradient force (GGF)", in which uncertain factors such as additional vertical load, road surface change, and strong wind are also taken into account. An adaptive downhill shift control system is then developed to help driver to use the engine brake with lower gears while downhill driving. In the adaptive system, a three-layer neural network is built to evaluate the necessity to make use of engine brake capability in current downhill situation, and the neural network is trained with samples from experienced drivers. Field test results of the adaptive system are introduced to verify the effectiveness of the approach mentioned above.

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“…[3][4][5][6] It is quite convenient to calculate the road grade from the CAN bus signal. This method does not need any additional sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The longitudinal dynamics method mainly uses driving torque information from a controller area network (CAN) bus signal. [3][4][5][6] It is quite convenient to calculate the road grade from the CAN bus signal. This method does not need any additional sensor.…”

Section: Introductionmentioning

confidence: 99%

Road grade estimation based on the curvature of the vertical profile of a road

Nie

Shi

2016

Proceedings of the Institution of Mechanical Engineers, Part D:

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A road grade estimation model which uses the curvature to express the rate of change in the grade is proposed in this paper. The assumption that the rate of change in the road grade equals zero is widely accepted in the field of online road grade estimation. This assumption is reasonable to some extent, but it results in an inevitable time lag in the rolling-hills situation. This paper offers a road curvature estimation method which can be used to express the rate of change in the road grade. The recursive least-squares algorithm is used to the estimate the curvature, and then the Kalman filter is employed to estimate the road grade from the other vehicle states. Field tests are performed on a highway in a mountainous area. The offline road grade is used to analyse the instantaneous error and the time lag. The field test results show that the model performs well in reducing the time lag, especially in periods where the gradient changes rapidly.

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Longitudinal road gradient estimation using vehicle CAN bus data

Cited by 5 publications

References 2 publications

Road gradient detection using Kalman filter

Road gradient detection using Kalman filter

Application of neural network to downhill shift strategy for automatic transmission

Road grade estimation based on the curvature of the vertical profile of a road

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