Load torque estimation for an automotive electric rear axle drive by means of virtual sensing using Kalman filtering

2022 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Gerald Kelz ◽  
Katrin Ellermann ◽  
Robert Kalcher
Author(s):  
Pieter Nguyen Phuc ◽  
Dimitar Vaskov Bozalakov ◽  
Hendrik Vansompel ◽  
Kurt Stockman ◽  
Guillaume Crevecoeur

2021 ◽  
Vol 157 ◽  
pp. 106876
Author(s):  
Justino A.O. Cruz ◽  
Pedro M.T. Marques ◽  
Jorge H.O. Seabra ◽  
Ramiro C. Martins

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4683
Author(s):  
Qiang Tong ◽  
Hui Xie ◽  
Kang Song ◽  
Dong Zou

Engine brake torque is a key feedback variable for the optimal torque split control of an engine–motor hybrid powertrain system. Due to the limitations in available sensors, however, engine torque is difficult to measure directly. For torque estimation, the unknown external load torque and the overlap of the expansion stroke between cylinders introduce a great disturbance to engine speed dynamics. This makes the conventional cycle average engine speed-based estimation approach unusable. In this article, an in-cycle crankshaft speed-based indicated torque estimation approach is proposed for a four-cylinder engine. First, a unique crankshaft angle window is selected for load torque estimation without the influence of combustion torque. Then, an in-cycle angle-domain crankshaft speed dynamic model is developed for engine indicated torque estimation. To account for the effects of model inaccuracy and unknown external disturbances, a “total disturbance” term is introduced. The total disturbance is then estimated by an adaptive observer using the engine’s historical operating data. Finally, a real-time correction method for the friction torque is proposed in the fuel cut-off scenario. Combining the aforementioned torque estimators, the brake torque can be obtained. The proposed algorithm is implemented in an in-house developed multi-core engine control unit (ECU). Experimental validation results on an engine test bench show that the algorithm’s execution time is about 3.2 ms, and the estimation error of the brake torque is within 5%. Therefore, the proposed method is a promising way to accurately estimate engine torque in real-time.


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