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The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

Author

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  • Shih-Wei Tan

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Sheng-Wei Huang

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Yi-Zeng Hsieh

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Shih-Syun Lin

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

Abstract

This study uses deep learning to model the discharge characteristic curve of the lithium-ion battery. The battery measurement instrument was used to charge and discharge the battery to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve and was improved by MLP (multilayer perceptron), RNN (recurrent neural network), LSTM (long short-term memory), and GRU (gated recurrent unit). The results obtained by these methods were graphs. We used genetic algorithm (GA) to obtain the parameters of the discharge characteristic curve equation.

Suggested Citation

  • Shih-Wei Tan & Sheng-Wei Huang & Yi-Zeng Hsieh & Shih-Syun Lin, 2021. "The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm," Energies, MDPI, vol. 14(15), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4423-:d:599329
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    References listed on IDEAS

    as
    1. Zhihao Yu & Ruituo Huai & Linjing Xiao, 2015. "State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization," Energies, MDPI, vol. 8(8), pages 1-20, July.
    2. Yi-Zeng Hsieh & Shih-Syun Lin & Yu-Cin Luo & Yu-Lin Jeng & Shih-Wei Tan & Chao-Rong Chen & Pei-Ying Chiang, 2020. "ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
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