 Expertise-guided NOx emission modeling of hybrid vehicle engines via peak-valley-enhanced Gaussian process regressionChengqing Wen, Ji Li, Bo Wang, Guoxiang Lu and Hongming Xu Energy, 2025, vol. 322, issue C Abstract: In order to improve the characterization accuracy of NOx emission of hybrid vehicle engines, this paper proposes an expertise-guided NOx emissions modeling method of peak–valley-enhanced Gaussian process regression (PV-GPR) to precisely capture its mapping features. A K-nearest neighbors model is applied first to classify data based on engine operating conditions defined by the experts. Customized Gaussian process regression models are then developed for NOx emission under each condition. Each GPR model features a customized kernel function with identified peak and valley positions. All data was collected from an experimental test bench with a BYD gasoline engine for hybrid vehicles. The results show that using the proposed PV-GPR method achieves a lower RMSE (0.49), significantly outperforming the feedforward neural network (0.84) and cascade neural network (1.01). Gaussian kernel functions applied to NOx modeling in hybrid vehicle engines are designed, further extending the method’s applicability. Keywords: NOx emission modeling; Gaussian process regression; Kernel function design; Hybrid vehicle engine (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225008084 DOI: 10.1016/j.energy.2025.135166 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.