Establishing a prediction model, with linearity and few dof (degree of freedom), is a key step for the design of a control algorithm based on the modern control theory. In this paper, such a model is needed for active suppression of vehicle longitudinal low-frequency vibration. However, many dynamic processes in the vehicle have different effects on the vibration. Therefore, a detailed coupling model is firstly established, considering the dynamics of the torsional vibrations of the driveline and the tire, the tire force nonlinearity, and the vehicle vertical and pitch vibrations. Based on this model, sensitivity analysis is conducted and the results show that the tire slip, the torsional stiffness of the half-shaft, and the tire have great influences on the longitudinal vibration. Then a three-dof model is obtained by linearizing the tire slip into damping. A parameter estimation method is designed to obtain the model parameters. Finally, the model is validated. The time domain response, error analysis, and frequency response results demonstrate that the 3-dof model has a good consistency with the detailed coupling model. It is suitable as a control-oriented model.

In this article, a model predictive control strategy is presented for an all-speed governor of heavy-duty vehicles that satisfies the requirements of fast tracking and fuel economy. The control-oriented torque-based engine model is used for the design of a model predictive control-based speed tracking control algorithm. Two methods for improving the speed tracking and fuel economy synthesis are presented, which include engine load estimation and variable weighting factor. The engine speed and fuel mass are used to estimate the real-time engine torque. The variable weighting factor based on the driver's intention is used to adjust the control algorithm in MATLAB/Simulink. The simulation results show that the tracking performance and fuel economy of the model predictive controller are better than that of a proportional-integral-derivative controller.

Rapid increase of vehicle longitudinal acceleration is required in an engine torque increasing phase, whereas little overshoot and oscillating acceleration are required in a torque holding phase. These two features give satisfying results with respect to both drivability and comfortability. However, when subjected to a sudden torque change in the tip-in condition, the driveline undergoes strong low-frequency torsional vibration which has an adverse impact on vehicle comfortability. Normally, a linear quadratic (LQ) controller has a good comfort performance in reducing the vibration but with negative impact on the dynamic response of the vehicle which weakens the drivability. The two different performance demands in the two phases cannot be achieved simultaneously by only adjusting the weighting coefficients of the LQ controller. Therefore, a new control strategy decoupling the two phases is necessary and proposed in this paper. A linear quadratic regulator (LQR) is used in the torque increasing phase for dynamic response demand while a linear quadratic tracking (LQT) controller is applied in the torque holding phase for comfortability demand. The two controllers are switched smoothly via a fusion weighting factor based on the proposed fuzzy logic switching strategy. A quantitative evaluation method is used to evaluate the performances of the proposed control strategy. The results show that the double-targets switching control keeps better performances in both drivability and comfortability. The comfortability index of the proposed strategy is improved by 79.74% compared with that of the LQT whereas the dynamic response index is improved by 21.88% compared with that of the LQR.

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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Abstract-The low frequency longitudinal vibration is one of the key factors that affect the drivability. Therefore, the analysis and control of the low frequency longitudinal vibration of the vehicle has always been a key issue in the vehicle Vibration and Harshness (NVH). In this paper, a new active control strategy for motor torque compensation is proposed to apply to the driveline's counterforce so as to realize the active suppression of low frequency vehicle longitudinal vibration. In the MPC controller, reasonable parameter selection can improve not only the system's closed-loop performance, but also its stability and robustness. Based on the design of vehicle low frequency longitudinal vibration controller, we choose and design some important control parameters (e.g., prediction time domain, control time domain and weighting factor) to assure the MPC controller achieve the best performance. Besides, we also study the impact of different control parameters by its results, finding out the general impact of the law. The simulation results show that the variable weights MPC controller can achieve better performance by selecting MPC controller parameters reasonably.

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