A Multi-Frequency Electrical Impedance Spectroscopy Technique of Artificial Neural Network-Based for the Static State of Charge

• Main Author: Yi-Feng Luo
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Energies
• Subjects: artificial neural network (ANN); multi-frequency electrical impedance spectroscopy (EIS); lithium-ion (Li-ion) battery; static state of charge (<i>SOC</i>); potentiostats; galvanostats
• Online Access: https://www.mdpi.com/1996-1073/14/9/2526

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (<i>SOC</i>) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static <i>SOC</i>. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate <i>SOC</i>. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of <i>SOC</i> estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed ANN-based multi-frequency EIS technique can be utilized to measure the static <i>SOC</i> of random discharge of Li-ion batteries.