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Modelling of State of Charge Recognition: Use of a Bayesian Approach to Formulate Hidden State Perceptions and Emotions

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Abstract

State of charge (SOC) management is necessary for electric vehicle (EV) driving safety and comfort. However, the SOC estimated by a human driver may differ from the actual SOC, inducing negative emotions and false charging behaviors. Currently, a model for explaining or estimating driver perceptions and emotions regarding SOC is lacking. Therefore, this paper proposes a Bayesian framework that explains and estimates human SOC perception and related emotions and experimentally verifies the model predictions. The results suggest that the deviations between actual SOC variations and driver expectations are necessary to determine the emotional experiences of drivers caused by managing SOC noise.

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References

  1. Chao, D., DeBlock, R., Lai, C.H., Wei, Q., Dunn, B., Fan, H.J.: Amorphous VO2: A pseudocapacitive platform for high-rate symmetric batteries. Adv. Mater. 33, e2103736 (2021)

    Article  Google Scholar 

  2. Wang, T., Chen, S., Ren, H., Zhao, Y.: Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation. Int. J. Energy Res. 42, 1603–1614 (2018)

    Article  Google Scholar 

  3. Huang, C., Wang, Z., Zhao, Z., Wang, L., Lai, C.S., Wang, D.: Robustness evaluation of extended and unscented Kalman filter for battery state of charge estimation. IEEE Access 6, 27617–27628 (2018)

    Article  Google Scholar 

  4. Ernst, M.O., Bülthoff, H.H.: Merging the senses into a robust percept. Trends Cogn. Sci. 8, 162–169 (2004)

    Article  Google Scholar 

  5. Friston, K.: The free-energy principle: A unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)

    Article  Google Scholar 

  6. Körding, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004)

    Article  Google Scholar 

  7. Knill, D.C., Pouget, A.: The Bayesian brain: The role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004)

    Article  Google Scholar 

  8. Kersten, D., Mamassian, P., Yuille, A.: Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004)

    Article  Google Scholar 

  9. Wei, X.X., Stocker, A.A.: A Bayesian observer model constrained by efficient coding can explain ‘anti-Bayesian’ percepts. Nat. Neurosci. 18, 1509–1517 (2015)

    Article  Google Scholar 

  10. Yanagisawa, H.: A computational model of perceptual expectation effect based on neural coding principles. J. Sens. Stud. 31, 430–439 (2016)

    Article  Google Scholar 

  11. Wolpert, D.M., Ghahramani, Z., Jordan, M.I.: An internal model for sensorimotor integration. Science 269, 1880–1882 (1995)

    Article  Google Scholar 

  12. Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002)

    Article  Google Scholar 

  13. Pentland, A., Liu, A.: Modeling and prediction of human behavior. Neural Comput. 11, 229–242 (1999)

    Article  Google Scholar 

  14. Joffily, M., Coricelli, G.: Emotional valence and the free-energy principle. PLOS Comput. Biol. 9, e1003094 (2013)

    Article  Google Scholar 

  15. Yanagisawa, H.: Free-energy model of emotion potential: Modeling arousal potential as information content induced by complexity and novelty. Front. Comput. Neurosci. 15, 698252 (2021)

    Article  Google Scholar 

  16. Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., Penny, W.: Variational free energy and the Laplace approximation. Neuroimage 34, 220–234 (2007)

    Article  Google Scholar 

  17. Buckley, C.L., Kim, C.S., McGregor, S., Seth, A.K.: The free energy principle for action and perception: A mathematical review. J. Math. Psychol. 81, 55–79 (2017)

    Article  MATH  MathSciNet  Google Scholar 

  18. Hashimoto, T., Yanagisawa, H.: Modeling individual differences in risk feeling of autonomous driving behavior with a prediction error. JAMDSM 14, JAMDSM0078–JAMDSM0078 (2020)

    Article  Google Scholar 

  19. Friston, K., Kilner, J., Harrison, L.: A free energy principle for the brain. J. Physiol. Paris 100, 70–87 (2006)

    Article  Google Scholar 

  20. Yanagisawa, H., Kawamata, O., Ueda, K.: Modeling emotions associated with novelty at variable uncertainty levels: A Bayesian approach. Front. Comput. Neurosci. 13, 2 (2019)

    Article  Google Scholar 

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Acknowledgements

This work was conducted in collaboration with Toyota Motor Corporation. The theoretical part of this study was supported by KAKEN No. 21H03528. We thank Mr. Hironori Miki and Mr. Kenji Tsuchiya of Toyota Motor Co. Ltd. for their cooperation. We also thank Prof. Tamotsu Murakami and the members of the Design Engineering Lab for supporting this project.

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Correspondence to Hideyoshi Yanagisawa.

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Yanagisawa, H., Miyamoto, M. & Arima, S. Modelling of State of Charge Recognition: Use of a Bayesian Approach to Formulate Hidden State Perceptions and Emotions. Int. J. ITS Res. 20, 612–622 (2022). https://doi.org/10.1007/s13177-022-00313-5

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  • DOI: https://doi.org/10.1007/s13177-022-00313-5

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