Abstract
With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustion phasing) together, including 50% combustion crank angle (CA50), carbon monoxide (CO), unburned hydrocarbons (UHC), nitrogen oxides (NOx), indicated mean effective pressure (IMEP), and indicated thermal efficiency (ITE). The goal of this work was to verify that only one machine learning model can combine power, emissions, and phase metrics together for prediction. The predicted results showed that all coefficients of determination (R2) were larger than 0.97 with a relatively small RMSE, indicating that it is possible to build a predictive model with three types of parameters (power, emissions, phase) as outputs based on only one ANN model. Most importantly, when optimizing the powertrain control strategy of a hybrid vehicle, only a surrogate model can help establish the relationship between the input and output parameters of the whole engine, which is the need of the future research. Overall, this study demonstrated that it is feasible to integrate three types of combustion-related parameters in a single machine learning model.
Highlights
The rapid development of the automobile industry has greatly contributed to the economic development and modernization of China but at the same time has brought about energy supply tension and environmental pollution problems [1,2]
It can be found that all the black points are close to the red dashed line at 45 degrees, which indicates that the trained artificial neural network (ANN) model has good prediction performance and the predicted results agree with the actual values
The R2 values based on the ANN model for the parameters CA50, carbon monoxide (CO), unburned hydrocarbons (UHC), nitrogen oxides (NOx), indicated mean effective pressure (IMEP), and indicated thermal efficiency (ITE) are 0.9977, 0.9828, 0.9936, 0.9899, 0.9892, and 0.9914, respectively, and the root mean square error (RMSE) values are 0.8353 1.0507, 0.4659, 1.3946, 0.3060, and 0.3225
Summary
The rapid development of the automobile industry has greatly contributed to the economic development and modernization of China but at the same time has brought about energy supply tension and environmental pollution problems [1,2]. The development of efficient and low-pollution advanced combustion technology has become the main goal of the internal combustion engine industry and researchers [3,4]. The decarbonized energy revolution requires innovation in various powertrains such as gas turbine combustor [5,6] and advanced engine technologies such as in-cylinder thermal barrier coatings [7,8]. It needs different kinds of alternative fuels (i.e., biofuel [9,10], natural gas [11,12], and ethanol [13,14]). Engine research and development are mainly based on 3D CFD simulations and bench tests, but they are time consuming or References [40] [32] [41] [42] [43] [44] [45] [46]
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