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Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries

Author

Listed:
  • Donghun Wang

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Jihwan Hwang

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Jonghyun Lee

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Minchan Kim

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Insoo Lee

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

Abstract

Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops because of their environmentally friendly nature, high energy density, and long lifespan. Despite these advantages, lithium-ion batteries may experience overcharging or discharging if they are not continuously monitored, leading to fire and explosion risks, in cases of overcharging, and decreased capacity and lifespan, in cases of overdischarging. Another factor that can decrease the capacity of these batteries is their internal resistance, which varies with temperature. This study proposes an estimation method for the state of charge (SOC) using a neural network (NN) model that is highly applicable to the external temperatures of batteries. Data from a vehicle-driving simulator were used to collect battery data at temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, including voltage, current, temperature, and time data. These data were used as inputs to generate the NN models. The NNs used to generate the model included the multilayer neural network (MNN), long short-term memory (LSTM), gated recurrent unit (GRU), and gradient boosting machine (GBM). The SOC of the battery was estimated using the model generated with a suitable temperature parameter and another model generated using all the data, regardless of the temperature parameter. The performance of the proposed method was confirmed, and the SOC-estimation results demonstrated that the average absolute errors of the proposed method were superior to those of the conventional technique. In the estimation of the battery’s state of charge in real time using a Jetson Nano device, an average error of 2.26% was obtained when using the GRU-based model. This method can optimize battery performance, extend battery life, and maintain a high level of safety. It is expected to have a considerable impact on multiple environments and industries, such as electric vehicles, mobile phones, and laptops, by taking advantage of the lightweight and miniaturized form of the Jetson Nano device.

Suggested Citation

  • Donghun Wang & Jihwan Hwang & Jonghyun Lee & Minchan Kim & Insoo Lee, 2023. "Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries," Energies, MDPI, vol. 16(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2639-:d:1094121
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    References listed on IDEAS

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    1. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    2. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(C).
    3. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    4. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
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