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Robust Estimation of Longitudinal Velocity and Road Slope in Hybrid Electric Vehicles using an Adaptive Kalman Filter

Matthijs Klomp ; Yunlong Gao (Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system) ; Fredrik Bruzelius (Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system)
Proceedings of the 23st International Symposium on Dynamics of Vehicles on Roads and Tracks (2013)
[Konferensbidrag, refereegranskat]

Accurate knowledge of the vehicle longitudinal velocity is essential for wheel slip control. Estimation of the slope angle in turn is important for real-life fuel economy optimization and improved traction control. In order to meet these demands also during slippery conditions, an accurate and efficient method of longitudinal velocity and slope estimation is proposed in paper. The research object of this work is a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque, which offered by electric motor is used to find out over-slipping wheels. Also, only one wheel speed is selected as the observation variable of Kalman Filter by means of a best-wheel selection method, which reduces the influence of slipping wheels as well as the calculation quantity. Furthermore, the slope estimation and accelerometer bias are also taken into consideration. Finally, this algorithm is verified on a winter test ground with excellent results also when all four wheels are spinning.



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Denna post skapades 2014-01-09. Senast ändrad 2014-01-09.
CPL Pubid: 191918

 

Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system

Ämnesområden

Transport
Farkostteknik

Chalmers infrastruktur