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A seq2seq learning method for microscopic emission estimation of on-road vehicles

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Abstract

Microscopic emission estimation based on driving states plays a crucial role in controlling the pollution of on-road vehicles. Existing research has evolved from fitting nonlinear models of driving cycles and emission factors to utilizing neural networks to exploit driving patterns and construct a nonlinear mapping between driving states and emission values. However, due to the small percentage of driving-cycle related to high emissions, it is still challenging to capture the vehicle emission peaks, which lead to the most noteworthy high-emission characteristics being regarded as abnormal disturbances instead. To address the issue, this paper proposes a peak-sensitive microscopic emission estimation framework characterized by sequence-to-sequence learning for on-road vehicles. Sequence-to-sequence learning emphasizes serial pattern mapping from driving sequences to emission sequences to provide more statistical constraints and reduce the estimation uncertainty brought by unstable driving behaviors. Specifically, the framework aims at capturing context features related to high emissions from sequences dynamically in an adaptive and self-learning way and is composed of a driving states embedding module and a dynamic aggregation module. Particularly, an incremental tracking loss (ITL) is proposed to adjust the incremental emissions at adjacent time steps by supervising the differences of the generated sequences, enabling the model to track sudden changes in emissions. Extensive experiments are conducted on the on-board diagnostics (OBD) dataset with 12628 sampling records collected from a heavy-duty diesel vehicle. The results show that the estimation accuracy of our proposed method is significantly better than state-of-the-art methods, and it can effectively capture high-emission peaks.

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Data availability

The data that support the findings of this study are available from Department of Ecology and Environment of Anhui Province but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of Department of Ecology and Environment of Anhui Province.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62033012, 61725304, 62103124), Major Special Science and Technology Project of Anhui, China (201903a07020012, 202003a07020009), and Postdoctoral Science Foundation of China (2021M703119).

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230026, China

    Zhenyi Zhao, Yang Cao, Zhenyi Xu & Yu Kang

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

    Zhenyi Xu & Yu Kang

  3. Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230088, China

    Yu Kang

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  1. Zhenyi Zhao
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Correspondence to Yang Cao or Yu Kang.

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Zhao, Z., Cao, Y., Xu, Z. et al. A seq2seq learning method for microscopic emission estimation of on-road vehicles. Neural Comput & Applic 36, 8565–8576 (2024). https://doi.org/10.1007/s00521-024-09512-5

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