An Advanced High Dimensional Model Representation Approach for Internal Combustion Engine Modeling and Optimization

Jianhong Lei, Jing Li,Shaohua Wu,Haoxing Li,Gehan A. J. Amaratunga,Xu Han,Wenming Yang

ENERGY（2024）

Dalian Univ Technol | Nanjing Tech Univ | Univ Cambridge | Natl Univ Singapore

Cited 0|Views3

Abstract

This work introduces an efficient data-driven approach for engine performance modeling and optimization. A highly accurate and robust high-dimensional model representation (HDMR) surrogate model is developed based on the dataset generated from a coupled KIVA4-CHEMKIN II software. The HDMR model is integrated with a multi-objective optimization algorithm, enabling the automatic identification of optimal combustion strategies. Results suggest that the constructed HDMR model is able to accurately predict the engine performance and exhaust gas emissions with negligible CPU costs. Moreover, the model effectively identifies optimal engine operating conditions, leading to enhanced fuel efficiency.

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Key words

High dimension model representation (HDMR),Data driven,Multi-objective optimization,NSGA-III

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