Diesel Engine Torque Estimation Based on ENN

Ge, Yanwu; Li, Gang; Di, Xiang

doi:10.4271/2015-01-0232

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Three methodologies are commonly used in crankshaft speed-based torque estimation, consisting of black-box model-based estimation, frequency analysis-based mapping, and crankshaft dynamic model-based estimation [13]. The first solution is to use a black-box model (such as a neural network and nominal function) to describe the relationship between the engine torque and crankshaft instantaneous speed [14][15][16][17][18]. However, these methods need an amount of data to train the black-box model.…”

Section: Introductionmentioning

confidence: 99%

A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

et al. 2019

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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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