Contactless Li-Ion Battery Voltage Detection by Using Walabot and Machine Learning

2019 ◽  
Author(s):  
Yanan Wang ◽  
Haoyu Niu ◽  
Tiebiao Zhao ◽  
Xiaozhong Liao ◽  
Lei Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Principal Component ◽  
Stochastic Gradient Descent ◽  
Li Ion Battery ◽  
Linear Discriminant ◽  
Li Ion

Abstract This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

scholarly journals In situ fabrication of a graphene-coated three-dimensional nickel oxide anode for high-capacity lithium-ion batteries

RSC Advances ◽  
2018 ◽  
Vol 8 (14) ◽  
pp. 7414-7421 ◽  
Author(s):  
Chiwon Kang ◽  
Eunho Cha ◽  
Sang Hyub Lee ◽  
Wonbong Choi
Keyword(s):  
Nickel Oxide ◽  
Lithium Ion Batteries ◽  
Three Dimensional ◽  
High Capacity ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
In Situ Fabrication ◽  
Li Ion ◽  
Oxide Anode

The processing of graphene coated NiO–Ni anode using one CVD system delivered high Li-ion battery performance.

Direct planting of ultrafine MoO2+δ nanoparticles in carbon nanofibers by electrospinning: self-supported mats as binder-free and long-life anodes for lithium-ion batteries

2016 ◽  
Vol 18 (29) ◽  
pp. 19832-19837 ◽  
Author(s):  
Xiaoxiao Liu ◽  
Henghui Xu ◽  
Yunhui Huang ◽  
Xianluo Hu
Keyword(s):  
Lithium Ion Batteries ◽  
Carbon Nanofibers ◽  
High Performance ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Binder Free ◽  
Li Ion ◽  
Long Life

Three-dimensional interconnected carbon nanofibrous mats containing well-dispersed MoO2+δ nanocrystals are fabricated through electrospinning for high-performance Li-ion battery anodes.

scholarly journals Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

2021 ◽  
Vol 2 (02) ◽  
pp. 77-82
Author(s):  
Prismahardi Aji Riyantoko ◽  
Tresna Maulana Fahrudin ◽  
Kartika Maulida Hindrayani ◽  
Mohammad Idhom
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Gradient Descent ◽  
Exploratory Data Analysis ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Machine Learning Algorithm ◽  
Exploratory Data

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.        

Enhancing Co/Co2VO4 Li-ion Battery Anode Performances via 2D-2D Heterostructure Engineering

Nanoscale ◽  
2021 ◽  
Author(s):  
Kun Wang ◽  
Yongyuan Hu ◽  
Jian Pei ◽  
Fengyang Jing ◽  
Zhongzheng Qin ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Anode Material ◽  
Output Voltage ◽  
High Capacity ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Anode

High capacity Co2VO4 becomes a potential anode material for lithium ion batteries (LIBs) benefiting from its lower output voltage during cycling than other cobalt vanadates. However, the application of this...

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

scholarly journals Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

2021 ◽  
Vol 11 (13) ◽  
pp. 6237
Author(s):  
Azharul Islam ◽  
KyungHi Chang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Random Forest ◽  
Support System ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Unstructured Data ◽  
Stochastic Gradient Descent ◽  
Decision Making Support

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

Effects of SiO2 particles in copper current collector on diffusion induced stresses in layered Li-ion battery electrodes

2021 ◽  
pp. 095440622110036
Author(s):  
Roozbeh Pouyanmehr ◽  
Morteza Pakseresht ◽  
Reza Ansari ◽  
Mohammad Kazem Hassanzadeh-Aghdam
Keyword(s):  
Volume Fraction ◽  
Lithium Ion ◽  
Current Collector ◽  
Limiting Factors ◽  
Li Ion Battery ◽  
Current Collectors ◽  
Effect Of Particle Size ◽  
Li Ion ◽  
Induced Stresses ◽  
Copper Composite

One of the limiting factors in the life of lithium-ion batteries is the diffusion-induced stresses on their electrodes that cause cracking and consequently, failure. Therefore, improving the structure of these electrodes to be able to withstand these stresses is one of the ways that can extend the life of the batteries as well as improve their safety. In this study, the effects of adding graphene nanoplatelets and microparticles into the active plate and current collectors, respectively, on the diffusion induced stresses in both layered and bilayered electrodes are numerically investigated. The micromechanical models are employed to predict the mechanical properties of both graphene nanoplatelet-reinforced Sn-based nanocomposite active plate and silica microparticle-reinforced copper composite current collector. The effect of particle size and volume fraction in the current collector on diffusion induced stresses has been studied. The results show that in electrodes with a higher volume fraction of particles and smaller particle radii, decreased diffusion induced stresses in both the active plate and the current collector are observed. These additions will also result in a significant decrease in the bending of the electrode.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

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