Abstract
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.
Highlights
Engine–motor hybrid powertrain systems have been widely used in passenger vehicles [1] to meet increasingly strict emission legislation and improve fuel economy
The degradation in engine torque control performance will, in turn, have an adverse effect on the overall fuel economy of the hybrid powertrain systems [4]. This drives the need for real-time estimation of the engine torque, especially in the application of hybrid electric vehicles (HEVs) [5]
(1) An in-cycle crank angle-based crankshaft dynamic model was established, where a crank angle interval is chosen by experiments to estimate the load torque without influence from the combustion torque
Summary
Engine–motor hybrid powertrain systems have been widely used in passenger vehicles [1] to meet increasingly strict emission legislation and improve fuel economy. After the crankshaft’s instantaneous speed and indicated torque are processed by DFT (discrete Fourier transform), a significantly positive correlation can be observed between the two signals in the main harmonic order [20] This requires complicated signal and computational processing and is unsuitable for online applications in the engine control unit (ECU). In order to increase the adaptability and accuracy of the torque estimation algorithm, the intake process and combustion process are considered in crankshaft speed modeling This makes the physical model too complicated to implement in an ECU without many model parameters for calibration [27]. The existing torque online estimation methods are primarily based on look-up tables calibrated offline This is simple-to-straightforward to implement, but the estimation accuracy deteriorates as the engine ages.
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