Numerical Investigations of Injection Timing Effects on a GDI Engine Performance: Part B, In-Cylinder Emission Formation and Oxidation Process

Zhang, Yu; Yang, Ruomiao; Yan, Yuchao; Li, Ruijie; Fu, Jiahong; Liu, Zhentao

doi:10.3389/fenrg.2022.823386

Cited by 2 publications

(2 citation statements)

References 68 publications

(66 reference statements)

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“…The prediction performance of DOC can be seen in Figure 17. DOC has an impact on emissions and performance [71,72]. On the one hand, if DOC is too short, the conversion rate of chemical energy to heat energy decreases.…”

Section: Engine Map Predictionmentioning

confidence: 99%

The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

Wang

Yang

Sun³

et al. 2022

Processes

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While traditional one-dimensional and three-dimensional numerical simulation techniques require a lot of tests and time, emerging Machine Learning (ML) methods can use fewer data to obtain more information to assist in engine development. Combustion phasing is an important parameter of the spark-ignition (SI) engine, which determines the emission and power performance of the engine. In the engine calibration process, it is necessary to determine the maximum brake torque timing (MBT) for different operating conditions to obtain the best engine dynamics performance. Additionally, the determination of the combustion phasing enables the Wiebe function to predict the combustion process. Existing studies have unacceptable errors in the prediction of combustion phasing parameters. This study aimed to find a solution to reduce prediction errors, which will help to improve the calibration accuracy of the engine. In this paper, we used Support Vector Regression (SVR) to reconstruct the mapping relationship between engine inputs and responses, with the hyperparametric optimization method Gray Wolf Optimization (GWO) algorithm. We chose the engine speed, load, and spark timing as engine inputs. Combustion phasing parameters were selected as engine responses. After machine learning training, we found that the prediction accuracy of the SVR model was high, and the R2 of CA10−ST, CA50, CA90, and DOC were all close to 1. The RMSE of these indicators were close to 0. Consequently, SVR can be applied to the prediction of combustion phasing in SI gasoline engines and can provide some reference for combustion phasing control.

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Section: Engine Map Predictionmentioning

confidence: 99%

The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

Wang

Yang

Sun³

et al. 2022

Processes

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“…Nowadays, ML models can play an important role in different fields, such as building [5,6], medicine [7], geography [8], energy [9,10], vehicles [11], and so on. If combing the engine research and the machine learning modeling approaches, it can help to calibrate the engine, locate the efficient region, and reduce the number of experiments/three-dimensional (3D) simulations [12,13]. Moreover, there are two significant reasons for applying machine learning in internal combustion engines.…”

Section: Introductionmentioning

confidence: 99%

The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines

2022

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The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R2) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

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Numerical Investigations of Injection Timing Effects on a GDI Engine Performance: Part B, In-Cylinder Emission Formation and Oxidation Process

Cited by 2 publications

References 68 publications

The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines

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