scholarly journals A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2524 ◽  
Author(s):  
Jia ◽  
Guan ◽  
Wu
Keyword(s):  
Lithium Batteries ◽  
Lithium Ion ◽  
Degradation Process ◽  
State Of Health ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Set Up

As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data sets. Firstly, according to importance sampling (IS), valid features are extracted from cycles of charging voltage in both the source and target battery. Secondly, transfer component analysis (TCA) is used to map the source data set to the target data set. Moreover, an extreme learning machine (ELM) algorithm is employed to train a single hidden layer feed forward neural network (SLFN) for its fast training speed and facile to set up. Finally, validation experiments and the comparisons on the results are conducted. The results showed that the proposed framework has a good capability of predicting the SOH of lithium batteries.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

Lithium-ion battery state of health estimation based on improved deep extreme learning machine

2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Wanwan Zeng ◽  
Chun Chang ◽  
Qiyue Wang ◽  
Si Xu
Keyword(s):  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Time Interval ◽  
State Of Health ◽  
Discharge Data ◽  
Ergodic Method ◽  
Discharge Temperature ◽  
Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

scholarly journals Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zhike Zhao ◽  
Xiaoguang Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Performance Test ◽  
Research Problem ◽  
Wire Rope ◽  
Nondestructive Method ◽  
Experimental Results ◽  
Feed Forward Neural Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Nodes

An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

Assessment of Products Future Reusability Based on Consumers Usage Behavior: Implications for Lithium-Ion Laptop Batteries

2015 ◽  
Author(s):  
Mostafa Sabbaghi ◽  
Behzad Esmaeilian ◽  
Ardeshir Raihanian Mashhadi ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
High Reliability ◽  
Lithium Ion ◽  
Degradation Process ◽  
Rechargeable Batteries ◽  
Data Set ◽  
Utilization Behavior ◽  
Product Reuse ◽  
Number Of Cycles ◽  
Stability Time ◽  
Product Durability

Product reuse is a recommended action toward sustainability. However, the profitable reusability of End-of-Use or End-of-Life (EoU/L) products depends on how consumers have used them over the initial lifecycles and what are their EoU conditions. In addition to consumers’ behavior, product design features such as product durability has an impact on the future reusability. In this paper, a data set of Lithium-ion laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the lifetime of these batteries and improving the recycling process is becoming important. In this paper, the reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their lifetimes. After capturing the utilization behavior, the performance-based stability time of batteries is approximately derived. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.

scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression

2015 ◽  
Vol 24 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Omer F. Alcin ◽  
Abdulkadir Sengur ◽  
Jiang Qian ◽  
Melih C. Ince
Keyword(s):  
Extreme Learning Machine ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Machine Intelligence ◽  
Orthogonal Matching Pursuit ◽  
Training Data ◽  
Singularity Problem ◽  
Data Set ◽  
Learning Machine ◽  
Hidden Layer

AbstractExtreme learning machine (ELM) is a recent scheme for single hidden layer feed forward networks (SLFNs). It has attracted much interest in the machine intelligence and pattern recognition fields with numerous real-world applications. The ELM structure has several advantages, such as its adaptability to various problems with a rapid learning rate and low computational cost. However, it has shortcomings in the following aspects. First, it suffers from the irrelevant variables in the input data set. Second, choosing the optimal number of neurons in the hidden layer is not well defined. In case the hidden nodes are greater than the training data, the ELM may encounter the singularity problem, and its solution may become unstable. To overcome these limitations, several methods have been proposed within the regularization framework. In this article, we considered a greedy method for sparse approximation of the output weight vector of the ELM network. More specifically, the orthogonal matching pursuit (OMP) algorithm is embedded to the ELM. This new technique is named OMP-ELM. OMP-ELM has several advantages over regularized ELM methods, such as lower complexity and immunity to the singularity problem. Experimental works on nine commonly used regression problems indicate that the investigated OMP-ELM method confirms these advantages. Moreover, OMP-ELM is compared with the ELM method, the regularized ELM scheme, and artificial neural networks.

scholarly journals Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xinyi Yang ◽  
Shan Pang ◽  
Wei Shen ◽  
Xuesen Lin ◽  
Keyi Jiang ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Validation Data ◽  
Data Set ◽  
Swarm Optimization ◽  
Turbine Engine ◽  
Optimal Network ◽  
Aero Engine ◽  
Learning Machine ◽  
Hidden Layer

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

Reusability Assessment of Lithium-Ion Laptop Batteries Based on Consumers Actual Usage Behavior

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Mostafa Sabbaghi ◽  
Behzad Esmaeilian ◽  
Ardeshir Raihanian Mashhadi ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
Life Cycle ◽  
High Reliability ◽  
Lithium Ion ◽  
Degradation Process ◽  
Rechargeable Batteries ◽  
Significant Drop ◽  
Data Set ◽  
Actual Usage ◽  
The Stability ◽  
Stability Time

In this paper, a data set of Lithium-ion (Li-ion) laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the life cycle of these batteries and improving the recycling process is becoming increasingly important. The reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their life cycle. After capturing the utilization behavior, the stability time of batteries is approximately derived. The stability time represents the interval that a battery works normally without any significant drop in performance. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.

scholarly journals Specialios struktūros daugiasluoksnis perceptronas daugiamačiams duomenims vizualizuoti

2009 ◽  
Vol 50 ◽  
pp. 358-364
Author(s):  
Laura Ringienė ◽  
Gintautas Dzemyda
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Data Clustering ◽  
Multidimensional Data ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Multidimensional Data Visualization ◽  
Radial Basis ◽  
Hidden Layer

Pasiūlytas ir ištirtas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono junginys daugiamačiams duomenis vizualizuoti. Siūlomas vizualizavimo būdas apima daugiamačių duomenų matmenų mažinimą naudojant radialines bazines funkcijas, daugiamačių duomenų suskirstymą į klasterius, klasterį charakterizuojančių skaitinių reikšmių nustatymą ir daugiamačių duomenų vizualizavimą dirbtinio neuroninio tinklo paskutiniame paslėptajame sluoksnyje.Special Multilayer Perceptron for Multidimensional Data VisualizationLaura Ringienė, Gintautas Dzemyda SummaryIn this paper a special feed forward neural network, consisting of the radial basis function layer and a multilayer perceptron is presented. The multilayer perceptron has been proposed and investigated for multidimensional data visualization. The roposedvisualization approach includes data clustering, determining the parameters of the radial basis function and forming the data set to train the multilayer perceptron. The outputs of the last hidden layer are assigned as coordinates of the visualized points.

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