Next Article in Journal
Numerical Analysis on the Flue Gas Temperature Maintenance System of a Solid Fuel-Fired Boiler Operating at Minimum Loads
Next Article in Special Issue
Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result
Previous Article in Journal
Application of Scattering Parameters to DPL Time-Lag Parameter Estimation at Nanoscale in Modern Integration Circuit Structures
Previous Article in Special Issue
Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide
Article

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

1
Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
2
Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
3
Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202301, Taiwan
4
Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editor: Domenico Di Domenico
Energies 2021, 14(15), 4423; https://doi.org/10.3390/en14154423
Received: 31 May 2021 / Revised: 12 July 2021 / Accepted: 15 July 2021 / Published: 22 July 2021
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. View Full-Text
Keywords: deep learning; MLP (multilayer perceptron); RNN (recurrent neural network); LSTM (long short-term memory); GRU (gated recurrent unit); genetic algorithm (GA) deep learning; MLP (multilayer perceptron); RNN (recurrent neural network); LSTM (long short-term memory); GRU (gated recurrent unit); genetic algorithm (GA)
Show Figures

Figure 1

MDPI and ACS Style

Tan, S.-W.; Huang, S.-W.; Hsieh, Y.-Z.; Lin, S.-S. The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm. Energies 2021, 14, 4423. https://doi.org/10.3390/en14154423

AMA Style

Tan S-W, Huang S-W, Hsieh Y-Z, Lin S-S. The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm. Energies. 2021; 14(15):4423. https://doi.org/10.3390/en14154423

Chicago/Turabian Style

Tan, Shih-Wei, Sheng-Wei Huang, Yi-Zeng Hsieh, and Shih-Syun Lin. 2021. "The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm" Energies 14, no. 15: 4423. https://doi.org/10.3390/en14154423

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop