Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Th...
详细信息
Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Therefore, this paper proposes a physics-informeddata-driven volumetric efficiency modeling method (PDM). Firstly, this paper constructs a model based on the simplified first law of physics to capture the main trends of volumetric efficiency changes. To improve the accuracy of the estimation, a PDM is proposed. This method includes a physical loss term and a data loss term. These loss terms are fused into a single fusion loss to train the neural network parameters, effectively merging the physical model with the neural network. The high correlation coefficient (R-2 = 0.958) between the PDM's volumetric efficiency estimates and the measured data demonstrates the robustness of the method. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Th...
详细信息
Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Therefore, this paper proposes a physics-informeddata-driven volumetric efficiency modeling method (PDM). Firstly, this paper constructs a model based on the simplified first law of physics to capture the main trends of volumetric efficiency changes. To improve the accuracy of the estimation, a PDM is proposed. This method includes a physical loss term and a data loss term. These loss terms are fused into a single fusion loss to train the neural network parameters, effectively merging the physical model with the neural network. The high correlation coefficient (R 2 = 0.958) between the PDM's volumetric efficiency estimates and the measured data demonstrates the robustness of the method.
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