期刊
ENERGIES
卷 14, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/en14154506
关键词
lithium battery; state of charge; state of health; multilayer neural network; long short-term memory; estimation
资金
- BK21 FOUR project - Ministry of Education in Korea [4199990113966]
- National Research Foundation of Korea (NRF) - Ministry of Education [2020R1I1A3A04036615]
- National Research Foundation of Korea [2020R1I1A3A04036615] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study proposes a lithium battery health monitoring method and state of charge estimation algorithm based on neural network models, to prevent accidents such as performance degradation and explosion by diagnosing health status and estimating charge level.
Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods.
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