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

Shih-Wei Tan, Sheng-Wei Huang, Yi-Zeng Hsieh and Shih-Syun Lin
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Shih-Wei Tan: Department of Electrical 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
Shih-Syun Lin: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan

Energies, 2021, vol. 14, issue 15, 1-21

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.

Keywords: deep learning; MLP (multilayer perceptron); RNN (recurrent neural network); LSTM (long short-term memory); GRU (gated recurrent unit); genetic algorithm (GA) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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