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Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree

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  • Qi Wang
  • Yinsheng Luo
  • Xiaoxin Han

Abstract

In order to achieve the accurate estimation of state of charge (SOC) of the battery in a hybrid electric vehicle (HEV), this paper proposed a new estimation model based on the classification and regression tree (CART) which belongs to a kind of decision tree. The basic principle and modelling process of the CART decision tree were introduced in detail in this paper, and we used the voltage, current, and temperature of the battery in an HEV to estimate the value of SOC under the driving cycle. Meanwhile, we took the energy feedback of the HEV under the regenerative braking into consideration. The simulation data and experimental data were used to test the effectiveness of the estimation model of CART, and the results indicate that the proposed estimation model has high accuracy, the relative error of simulation is within 0.035, while the relative error of experiment is less than 0.05.

Suggested Citation

  • Qi Wang & Yinsheng Luo & Xiaoxin Han, 2019. "Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 25(4), pages 376-396, July.
    • Handle: RePEc:taf:nmcmxx:v:25:y:2019:i:4:p:376-396
    DOI: 10.1080/13873954.2019.1655654
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    Cited by:

    1. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.

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