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

2020 ◽  
Vol 10 (23) ◽  
pp. 8644
Author(s):  
JaeHyung Park ◽  
JongHyun Lee ◽  
SiJin Kim ◽  
InSoo Lee
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Alternative Energy ◽  
Lithium Battery ◽  
Short Term Memory ◽  
Discharge Rate ◽  
State Of Charge ◽  
Battery Pack ◽  
Storage Device ◽  
The Difference

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.

scholarly journals Real-Time Guitar Amplifier Emulation with Deep Learning

2020 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Alec Wright ◽  
Eero-Pekka Damskägg ◽  
Lauri Juvela ◽  
Vesa Välimäki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Reference Signal ◽  
Network Models ◽  
Neural Network Models ◽  
Desktop Computer ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
The Difference

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

scholarly journals Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

Crystals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 138
Author(s):  
Natasha Dropka ◽  
Stefan Ecklebe ◽  
Martin Holena
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Network Performance ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Growth Dynamics ◽  
Vertical Gradient ◽  
Performance Criteria ◽  
Interface Position ◽  
Vertical Gradient Freeze

The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks.

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.

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 Towards real-time respiratory motion prediction based on long short-term memory neural networks

2019 ◽  
Vol 64 (8) ◽  
pp. 085010 ◽  
Author(s):  
Hui Lin ◽  
Chengyu Shi ◽  
Brian Wang ◽  
Maria F Chan ◽  
Xiaoli Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Respiratory Motion ◽  
Short Term Memory ◽  
Motion Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1813 ◽  
Author(s):  
Alexander L. Bowler ◽  
Serafim Bakalis ◽  
Nicholas J. Watson
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Short Term Memory ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Models ◽  
Ultrasonic Sensors ◽  
Multisensor Data Fusion ◽  
Machine Learning Models

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

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