Work

WD-PSTALSTM: a data-driven hybrid model for prediction of diesel vehicle NOx emissions

Year: 2025

Type: article

Abstract: Accurate prediction of transient nitrogen oxides (NOx) emissions from diesel vehicles is essential for precise emission inventories and effective pollution control but challenged by data nonlinearity and dynamic operating conditions. This study develops the Wavelet Decomposition (WD)-Parallel Spatio-Temporal Attention-based Long Short-Term Memory (PSTALSTM) model, using real-world Portable Emission Measurement System (PEMS) and On-Board Diagnostics (OBD) data. WD preprocessing reduces emission data non-stationarity, generating more stable inputs. The PSTALSTM architecture, built upon Bidirectional Long Short-Term Memory (Bi-LSTM), incorporates a parallel attention mechanism to adaptively weight features and temporal segments, effectively capturing spatio-temporal correlations within the emission data. Validation with on-road test data demonstrates WD-PSTALSTM's superior performance over existing models. It achieves reductions exceeding 20% in mean absolute error (MAE) and 15% in root mean square error (RMSE), significantly enhancing prediction accuracy. These results establish WD-PSTALSTM as an effective approach for forecasting transient diesel engine NOx emissions. The research provides valuable methodologies for emission prediction based on vehicle operational data, contributing to environmental pollution mitigation efforts.

Source: Energy and AI

DOI: 10.1016/j.egyai.2025.100578

Authors: Ling Liu, Jihui Zhuang, Yuelei Wang, Pei Li, Dongping Guo, Xiaoming Cheng

Institutions: Hainan Vocational University of Science and Technology, Hainan University

Language: English

Topic: Vehicle emissions and performance

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,630