Longitudinal velocity and road slope estimation in hybrid electric vehicles employing early detection of excessive wheel slip

• Published in: Vehicle system dynamics Vol. 52; no. sup1; pp. 172 - 188
• Main Authors: Klomp, Matthijs, Gao, Yunlong, Bruzelius, Fredrik
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2014; Taylor & Francis Ltd
• Subjects: 911 Road: Components of the vehicle; Acceleration; Algorithms; Automotive wheels; Axles; best-wheel speed; Combustion; Dead reckoning; Electric motors; Electric vehicles; Estimation; Extreme values; Hybrid electric vehicles; Hybrid vehicle; Hybrid vehicles; Kalman filter; Kalman filters; Mathematical model; Shafts (machine elements); Slip; Slipperiness; Slope (gradient); slope estimation; Trucks; Vehicle wheels; Vehicles; velocity estimation; Weel; wheel torque
• ISSN: 0042-3114; 1744-5159; 1744-5159
• DOI: 10.1080/00423114.2014.887737

Vehicle speed is one of the important quantities in vehicle dynamics control. Estimation of the slope angle is in turn a necessity for correct dead reckoning from vehicle acceleration. In the present work, estimation of vehicle speed is applied to a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque information, provided by electric motor, is used to early detect excessive wheel slip and improve the accuracy of the estimate. A best-wheel selection approach is applied as the observation variable of a Kalman filter which reduces the influence of slipping wheels as well as reducing the computational effort. The performance of the proposed algorithm is illustrated on a test data recorded at a winter test ground with excellent results, even for extreme conditions such as when all four wheels are spinning.