Li, Qian (Tianjin University), Guo, Fan (Tianjin university), Song, Kang (TianJin University), Xie, Hui (Tianjin University), Zhou, Shengkai (Guangxi Yuchai Machinery Co., Ltd), Sang, Hailang (Guangxi Yuchai Machinery Co., Ltd)

Physics-Informed Data-Driven Modeling for Engine Volumetric Efficiency Estimation

Scheduled for presentation during the Regular session "Powertrain Control II" (FrB3), Friday, November 1, 2024, 11:10−11:30, Room T3

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

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-informed data- 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 (R2 = 0.958) between the PDM’s volumetric efficiency estimates and the measured data demonstrates the robustness of the method.