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Expertise-guided NOx emission modeling of hybrid vehicle engines via peak-valley-enhanced Gaussian process regression

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  • Wen, Chengqing
  • Li, Ji
  • Wang, Bo
  • Lu, Guoxiang
  • Xu, Hongming

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.

Suggested Citation

  • Wen, Chengqing & Li, Ji & Wang, Bo & Lu, Guoxiang & Xu, Hongming, 2025. "Expertise-guided NOx emission modeling of hybrid vehicle engines via peak-valley-enhanced Gaussian process regression," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225008084
    DOI: 10.1016/j.energy.2025.135166
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    References listed on IDEAS

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    1. Wang, Zhihong & Luo, Kangwei & Yu, Hongsen & Feng, Kai & Ding, Hang, 2024. "NOx Emission prediction of heavy-duty diesel vehicles based on Bayesian optimization -Gated Recurrent Unit algorithm," Energy, Elsevier, vol. 292(C).
    2. Li, Ji & Zhou, Quan & Williams, Huw & Xu, Pu & Xu, Hongming & Lu, Guoxiang, 2022. "Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines," Applied Energy, Elsevier, vol. 310(C).
    3. Novella, Ricardo & Gomez-Soriano, Josep & González-Domínguez, David & Olaciregui, Orlando, 2024. "Optimizing hydrogen spark-ignition engine performance and pollutants by combining VVT and EGR strategies through numerical simulation," Applied Energy, Elsevier, vol. 376(PB).
    4. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
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