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International Workshop on Soft Computing Models in Industrial and Environmental Applications

International Conference on EUropean Transnational Education

Computational Intelligence in Security for Information Systems Conference

SOCO 2017, ICEUTE 2017, CISIS 2017: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding pp 47–56Cite as

Health Assessment of Automotive Batteries Through Computational Intelligence-Based Soft Sensors: An Empirical Study

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 649))

Abstract

An empirical comparison of different intelligent soft sensors for obtaining the state of health of automotive rechargeable batteries is presented. Data streamed from on-vehicle sensors of current, voltage and temperature is processed through a selection of model-based observers of the state of health, including data-driven statistical models, first principle-based models, fuzzy observers and recurrent neural networks with different topologies. It is concluded that certain types of recurrent neural networks can outperform well established first-principle models and provide the supervisor with a prompt reading of the State of Health. The algorithms have been validated with automotive Li-FePO\(_4\) cells.

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Acknowledgements

This work was supported in part by the Science and Innovation Spanish Ministry and FEDER under the Projects TIN2014-56967-R, DPI2013-046541-R, TEC2016-80700-R (AEI/FEDER, UE), and by the Principality of Asturias Government under Project FC-15-GRUPIN14-073.

Author information

Authors and Affiliations

  1. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain

    Eva Almansa

  2. Departamento de Ingeniería Eléctrica, Electrónica, de Computadores y de Sistemas, Universidad de Oviedo, Oviedo, Spain

    David Anseán

  3. Departamento de Estadística, Universidad de Oviedo, Oviedo, Spain

    Inés Couso

  4. Departamento de Informática, Universidad de Oviedo, Oviedo, Spain

    Luciano Sánchez

Authors
  1. Eva Almansa
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  2. David Anseán
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  3. Inés Couso
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  4. Luciano Sánchez
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Corresponding author

Correspondence to Luciano Sánchez .

Editor information

Editors and Affiliations

  1. University of Leon, Leon, Spain

    Hilde Pérez García

  2. University of Leon, Leon, Spain

    Javier Alfonso-Cendón

  3. University of Leon, Leon, Spain

    Lidia Sánchez González

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Almansa, E., Anseán, D., Couso, I., Sánchez, L. (2018). Health Assessment of Automotive Batteries Through Computational Intelligence-Based Soft Sensors: An Empirical Study. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67179-6

  • Online ISBN: 978-3-319-67180-2

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