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 and Wenming Yang

Energy, 2024, vol. 311, issue C

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.

Keywords: High dimension model representation (HDMR); Data driven; Multi-objective optimization; NSGA-Ⅲ (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031852

DOI: 10.1016/j.energy.2024.133409

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