Battery State-of-Charge Estimation Using ANN and ANFIS for Photovoltaic System

Cho, Tae-Hyun; Hwang, Hye-Rin; Lee, Hyun; Lee, In Su

doi:10.14801/jkiit.2020.18.5.55

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In the OCV method, SOC estimation is performed based on the fact that the remaining capacity of the battery naturally decreases in proportion to its energy usage. However, real-time SOC estimation, using this method is difficult, as it requires a large amount of time, requires the battery to be in an equilibrium state, and requires the measurement of the terminal voltage [17].…”

Section: Traditional Soc Estimation Methodsmentioning

confidence: 99%

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Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks

et al. 2020

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With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used in numerous applications, from everyday items, such as smartphones, wireless vacuum cleaners, and wireless power tools, to transportation means, such as electric vehicles and bicycles. In this paper, the state of charge (SOC) of each cell of the lithium battery pack was estimated in real time using two types of neural networks: Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM). To determine the difference in the SOC estimation performance under various conditions, the input values were compared using 2, 6, and 8 input values, and the difference according to the use of temperature variable data was compared, and finally, the MNN and LSTM. The differences were compared. Real-time SOC was estimated using the method with the lowest error rate.

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Section: Traditional Soc Estimation Methodsmentioning

confidence: 99%

“…We compared the advantages and disadvantages of the conventional OCV method, Kalman filter method, neural network method, which are frequently used for SOC estimation, and the proposed method in Table 1 [16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Traditional Soc Estimation Methodsmentioning

confidence: 99%