High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations. Overall, these results indicate that ANN models could be used for a variety of research applications due to their economic and computational benefits such as derivation of vehicle control strategies to reduce FC and emissions in modern vehicles.


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    Titel :

    Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Galang, Abril A. (Autor:in) / Johnston, Brian (Autor:in) / Jathar, Shantanu (Autor:in) / Bradley, Thomas H. (Autor:in) / Briggs, Will (Autor:in) / Asher, Zachary D. (Autor:in)

    Kongress:

    WCX World Congress Experience ; 2018



    Erscheinungsdatum :

    2018-04-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network

    Asher, Zachary D. / Galang, Abril A. / Briggs, Will et al. | British Library Conference Proceedings | 2018



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    Moyer, D. F. / Stadler, H. L. / Auiler, J. E. et al. | SAE Technical Papers | 1976


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    Lamberson, D. M. / Anderson, S. R. / Blohm, T. J. et al. | SAE Technical Papers | 2005


    Hybrid Vehicle for Fuel Economy

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