Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result

Jong-Hyun Lee and In-Soo Lee
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Jong-Hyun Lee: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
In-Soo Lee: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea

Energies, 2021, vol. 14, issue 15, 1-16

Abstract: 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.

Keywords: lithium battery; state of charge; state of health; multilayer neural network; long short-term memory; estimation (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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