Cloud-assisted high-Sulfur fuel monitoring for connected heavy-duty vehicles based on transformer neural network

Jiawei Liu, Yongxin Li, Ning Wang, Yao Sun, Tingting Wang, Yunfeng Hu, Hong Chen and Xun Gong

Energy, 2025, vol. 335, issue C

Abstract: High-sulfur diesel usage in heavy-duty vehicles (HDVs) worsens harmful emissions, harming public health, and hindering progress in clean energy and low-emission technologies. Toward this end, this study investigates the early-stage impact of high-sulfur diesel on HDV after-treatment systems and introduces HS-FuelFormer, a cloud-assisted framework for real-time diesel quality monitoring using connected HDV sensor data. To reduce reliance on data integrity, it employs a transformer neural network (TNN) with a sliding-window technique for instant diesel type estimation. Multiple instant results are then integrated to enhance the evolving likelihood estimation of high-sulfur diesel usage. A credibility assessment method is also introduced to enhance framework transparency by interpreting the TNN’s decision-making process, fostering trust in the framework by drivers and regulators. A case study with real vehicular data demonstrates HS-FuelFormer’s ability to fill existing gap in online diesel quality monitoring. Experimental results also highlight its effectiveness in early detection, low-frequency operation to reduce cloud load, and reliable performance in unstable communication environments.

Keywords: High-sulfur diesel; Online monitoring; Heavy-duty vehicle; After-treatment system; Transformer neural network; Explainable machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225038320

DOI: 10.1016/j.energy.2025.138190

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