SOC Estimation of Large Capacity Lithium Batteries Based on LWOA-LSTM
Accurate prediction of the state of charge (SOC) of lithium batteries is crucial for their safe operation, and analyzing the SOC in different power grid modes is the basis for the comprehensive promotion of lithium batteries. This paper proposes a whale optimization algorithm based on Levy flight (L...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2024-06-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202312106 |
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| Summary: | Accurate prediction of the state of charge (SOC) of lithium batteries is crucial for their safe operation, and analyzing the SOC in different power grid modes is the basis for the comprehensive promotion of lithium batteries. This paper proposes a whale optimization algorithm based on Levy flight (LWOA) to optimize long short-term memory neural network (LSTM) for estimating the SOC of large capacity lithium-ion batteries in frequency modulation mode. Firstly, the LSTM neural network and LWOA algorithm are analyzed, and the LWOA-LSTM model is constructed to optimize the parameters. Then, the experimental data of the large capacity lithium-ion battery pack in frequency modulation mode are selected for data preprocessing and model training. Finally, SOC estimation of lithium batteries in frequency modulation mode is achieved. The experimental results show that the constructed model can accurately predict the SOC of lithium batteries. Compared with the WOA-LSTM model, the evaluation indicators RMSE and MAE are reduced by 25.55% and 28.71%, respectively, while R2 increases by 0.76%. |
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| ISSN: | 1004-9649 |