This contribution is focused on the fuel economy improvement of the Miller cycle under part-load characteristics on a supercharged DI (Direct Injection) gasoline engine. Firstly, based on the engine bench test, the effects with the Miller cycle application under 3000 rpm were studied. The results show that the Miller cycle has different extents of improvement on pumping loss, combustion and friction loss. For low, medium and high loads, the brake thermal efficiency of the baseline engine is increased by 2.8%, 2.5% and 2.6%, respectively. Besides, the baseline variable valve timing (VVT) is optimized by the test. Subsequently, the 1D CFD (Computational Fluid Dynamics) model of the Miller cycle engine after the test optimization at the working condition of 3000 rpm and BMEP (Brake Mean Effective Pressure) = 10 bar was established, and the influence of the combined change of intake and exhaust valve timing on Miller cycle was studied by simulation. The results show that as the effect of the Miller cycle deepens, the engine’s knocking tendency decreases, so the ignition timing can be further advanced, and the economy of the engine can be improved. Compared with the brake thermal efficiency of the baseline engine, the final result after simulation optimization is increased from 34.6% to 35.6%, which is an improvement of 2.9%.

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To improve the performance of predictive energy management strategies for hybrid passenger vehicles, this paper proposes an Encoder–Decoder (ED)-based velocity prediction modelling system coupled with driving pattern recognition. Firstly, the driving pattern recognition (DPR) model is established by a K-means clustering algorithm and validated on test data; the driving patterns can be identified as urban, suburban, and highway. Then, by introducing the encoder–decoder structure, a DPR-ED model is designed, which enables the simultaneous input of multiple temporal features to further improve the prediction accuracy and stability. The results show that the root mean square error (RMSE) of the DPR-ED model on the validation set is 1.028 m/s for the long-time sequence prediction, which is 6.6% better than that of the multilayer perceptron (MLP) model. When the two models are applied to the test dataset, the proportion with a low error of 0.1~0.3 m/s is improved by 4% and the large-error proportion is filtered by the DPR-ED model. The DPR-ED model performs 5.2% better than the MLP model with respect to the average prediction accuracy. Meanwhile, the variance is decreased by 15.6%. This novel framework enables the processing of long-time sequences with multiple input dimensions, which improves the prediction accuracy under complicated driving patterns and enhances the generalization-related performance and robustness of the model.

<div class="section abstract"><div class="htmlview paragraph">Diesel engine is vital in the industry for its characteristics of low fuel consumption, high-torque, reliability, and durability. Existing diesel engine technology has reached the upper limit. It is difficult to break through the fuel consumption and emission of diesel engines. VVA (Variable Valve Actuation) is a new technology in the field of the diesel engines. In this paper, GT-Suite and ANN (artificial neural network) model are established based on engine experimental data and DoE simulation results. By inputting Intake Valve Opening crake angle (IVO), Intake Valve Angle Multiplier (IVAM) and Exhaust Valve Angle Multiplier (EVAM) into the ANN Model, and by using SA (simulated annealing algorithm), the optimized results of intake and exhaust valve lift under the target conditions are obtained. According to the optimized results, the fuel consumption of BSFC (brake specific fuel consumption) can be saved by 3.9%, 0.9%, and 7.3% at three different target working conditions, respectively (1000r/min with 50% load, 2000 r/min with 35% load, and 3000r/min with 20% load. Under the three conditions, the intake valve lifts are 1.10, 0.96, 1.10 times as the original respectively and the exhaust valve lifts are 0.98,0.94 and 1.10 times respectively. In addition, by adding the secondary opening strategy of the exhaust valve lift, internal exhaust gas recycling (IEGR) can be achieved. The exhaust gas temperature increased by 9.55% (66.64K) under low-speed working condition, and fuel consumption increased slightly by 6.15%, which is also important for exhaust thermal management and external emission control in the cold start phase of diesel engines. By changing the intake and exhaust valve control, there are obvious differences in the optimal valve lift under different working conditions. Therefore, the application of the VVA system in the diesel engine is of great significance.</div></div>

Fuel consumption is the most important parameter that characterizes the fuel economy of the engines. Instead of manual fuel consumption calibration based on the experience of engineers, the establishment of a fuel consumption model greatly reduces the time and cost of multiparameter calibration and optimization of modern engines and realizes the further exploration of the engine fuel economy potential. Based on the bench test, one-dimensional engine simulation, and design of experiment, a partially shared neural network with its sampling and training method to establish the engine fuel consumption model is proposed in this paper in view of the lack of discrete working conditions in the traditional neural network model. The results show that the proposed partially shared neural network applying Gauss distribution sampling and the frozen training method, after an analysis of the number of hidden neurons and epochs, showed optimal prediction accuracy and excellent robustness in full coverage over the whole load region on the test data set obtained through the bench test. Eighty-seven percent of the prediction errors are less than 3%, all prediction errors are less than 10%, and the R 2 value is improved to 0.954 on the test data set.