A model-based indicated torque estimation method for a turbocharged diesel engine is presented in this study. The proposed model consists of two submodels: a steady-state indicated torque model; a transient torque coefficient model using the Elman neural network. Experiments are designed to acquire the database for the model. The optimal parameters of the Elman neural network are determined; the results show that the mean absolute percentage error of the transient torque coefficient for the estimated values using the Elman neural network and the experimental values is within 2% and the maximum error is about 7%. A comparison of the usability of the back-propagation network and that of the Elman neural network for transient estimation problems is studied; the results show that the Elman neural network is more applicable in terms of the transient accuracy and the convergence time. To validate the accuracy of the model, the experimental results for a new engine speed with two new processes are employed as test data; it is shown that the mean absolute percentage error of the indicated torque is within 2% and the maximum error is about 6%. Furthermore, explicit formulation of the Elman neural network model is acquired and rewritten as C code. Then, online validation is conducted and the results show that the mean absolute percentage error of the indicated torque is within 6%, with a maximum error of 15%.

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For hybrid electric vehicles, there are output shaft torque fluctuations during the working condition switching process, which reduce the driving comfort of the vehicle. Therefore, corresponding control is necessary to eliminate the torque fluctuations. In this paper, for a dual-mode power-split hybrid system, the steady state energy management strategy under the typical power flow in two modes is studied and an operational condition switching control strategy based on engine torque control and motor speed control is proposed for the system characteristics. Meanwhile, the reason for fluctuations on the switching process based on engine torque control is found out to be the too large inertia moment in the coupling power mechanism. Considering the characteristics of fast speed and torque response of the motor, dynamic coordinated control strategy is proposed to eliminate the torque fluctuations and improve the accuracy of the actual torque relative to the target torque for the two models (i.e., the motor torque compensation control strategies). The model of dual-mode hybrid system was built and the simulation results show that the proposed control strategy has a positive effect on eliminating the torque fluctuations and the target torque of the driver can be accurately tracked.

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