State of Charge Estimation and State of Health Diagnostic Method Using Multilayer Neural Networks

2021 ◽  
Author(s):  
Jong-Hyun Lee ◽  
Hyun-Sil Kim ◽  
In-Soo Lee
Keyword(s):  
Neural Networks ◽  
Diagnostic Method ◽  
State Of Charge ◽  
State Of Health ◽  
Multilayer Neural Networks ◽  
State Of Charge Estimation

scholarly journals SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

10.29007/m89x ◽  
2020 ◽  
Author(s):  
Jong Hyun Lee ◽  
Hyun Sil Kim ◽  
In Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Discharge Voltage ◽  
State Of Charge ◽  
State Of Health ◽  
Soc Estimation ◽  
Multilayer Neural Networks

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

scholarly journals Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7206
Author(s):  
Sungwoo Jo ◽  
Sunkyu Jung ◽  
Taemoon Roh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Domain ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Charge ◽  
Training Data ◽  
State Of Health ◽  
Relative State ◽  
Time Domain Data

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.

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