Efficient estimating and clustering Lithium-ion batteries with a …
Efficient estimating and clustering Lithium-ion batteries with . a deep-learning approach. Zhongxian Sun 1, Weilin He 1, Dingquan Li 2, Xin Geng 3, Dongchen Y ang 4, Hailong .
Efficient estimating and clustering Lithium-ion batteries with . a deep-learning approach. Zhongxian Sun 1, Weilin He 1, Dingquan Li 2, Xin Geng 3, Dongchen Y ang 4, Hailong .
In this study, an unsupervised learning technique, clustering, is utilized to identify potential compounds suitable for serving as solid-state electrolytes in lithium-based …
Energy-storage systems such as battery modules for new energy vehicles (NEVs) are gaining extensive attention [1], [2] as a means of replacing traditional gas (petrol/diesel)-operated vehicles and thereby promoting a cleaner environment. The performance parameters of lithium (Li)-ion battery modules include energy density, capacity, and specific power.
In the case of defect detection in point cloud data of lithium batteries, the features used for clustering can include the location, size, shape, and type of defects. Once the clustering algorithm has identified the different clusters of defects, each cluster can be visualized as a different color or shape in the 3D point cloud model of the ...
Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is …
spatial clustering of applications with noise (DBSCAN) algorithm for battery clustering. Finding the optimal clustering center is a crucial aspect of battery classification algorithms ... Figure1a illustrates the Nyquist diagram of lithium-ion batteries, presenting approxi-mate representations of high-, medium-, and low-frequency bands. ...
This data‐driven clustering modeling with fast pulse test is a promising approach for clustering lithium‐ion batteries, which is demonstrated with a home‐built and high throughput ...
This paper has adopted traditional K-means and improved K-means algorithms to solve the problem of retired lithium-ion battery clustering. The clustering process for batteries shows that the improved K-means algorithm can effectively and stably reach the global optimal result. To achieve real-time battery clustering, new features are derived by ...
The traditional linear clustering methods, such as K-means, hierarchical clustering, are the hard-clustering methods (de Carvalho et al., 2012; Ferreira et al., 2016), which means each sample is only assigned to a specific cluster. ... Lithium-ion batteries (LIBs) are the ideal energy storage device for electric vehicles, and their ...
With the extensive use of lithium-ion batteries, the world is facing the problem of large-scale retirement of batteries. The second-use of retired lithium-ion batteries can effectively extend the service life of the batteries, so the second-use has received the key attention of the industry. However, most of the existing battery screening methods in the second-use process are …
In the context of solid-state electrolytes for batteries, ambient temperature ionic conductivity stands as a pivotal attribute. This investigation presents a compilation of potential candidates for solid-state electrolytes in lithium-ion batteries, employing clustering—an unsupervised machine-learning technique. To achieve this, a fusion of data from two distinct …
Efficient estimating and clustering Lithium-ion batteries with a deep-learning approach Zhongxian Sun1, Weilin He1, Dingquan Li2, Xin Geng3, Dongchen Yang4, Hailong Wang3, Linyu Hu5, Haiyan Tu1, Xin He1,3* 1 College of Electrical Engineering, Sichuan University, Chengdu, 610065, China 2 Peng Cheng Laboratory, Shenzhen, 518055, China 3School of Chemical Engineering, …
Graphite is the popular anode material of current lithium-ion batteries (LIBs). However, its low specific capacity and poor lithium intercalation potential hinder its use for high-power and large-scale energy storage. To meet the demand for energy storage, novel anode materials with high capacity, fast chargeable capability, and long cycle life are of great interest. …
With the growing electrification of various sectors, including transportation, there is a rising demand for Lithium-ion (Li-ion) batteries. This was reflected by the International Energy Association''s 2023 report which documented a 65 % increase in Li-ion battery demand within the automotive sector in 2022 compared to the previous year [1].This surge is a result to the …
Olivine-structured LiFePO4 is one of the most popular cathode materials in lithium-ion batteries (LIBs) for sustainable applications. Significant attention has been paid to investigating the dynamics of the lithiation/delithiation process in LixFePO4 (0 ≤ x ≤ 1), which is crucial for the development of high-performance LiFePO4 material. Various macroscopic models based on …
The rapid deployment of lithium-ion batteries in clean energy and electric vehicle applications will also increase the volume of retired batteries in the coming years. ... Different clustering algorithms can be used to regroup retired batteries. Available clustering algorithms for grouping battery include the k-means method, affinity ...
Soft clustering of retired lithium-ion batteries for the secondary utilization using Gaussian mixture model based on electrochemical impedance spectroscopy. J. Clean. Prod., 339 (2022), Article 130786. View PDF View article View in Scopus Google Scholar [21] J. Meng, J. Peng, L. Cai, Z. Song.
The sorting into groups is a critical step in the cascade utilization process of retired power lithium-ion batteries. In order to enhance the consistency performance of grouped batteries in cascade utilization, the various static and dynamic features based on battery charge and discharge experimental data are extracted in this paper, A two-tier sorting architecture is …
Clustering analysis based on self-organizing map (SOM) neural networks is applied on the measured data to form clusters of battery packs that have higher electrochemical performance than randomly selected ones. With the increase of production of electrical vehicles (EVs) and battery packs, lithium ion batteries inconsistency problem has drawn much attention.
The battery echelon utilization is to sort and reuse the retired lithium-ion batteries with poor consistency, which puts forward higher requirements on how to guarantee their comprehensive consistencies after sorting. ... Q. Wang, X. Cheng, J. Wang, A new algorithm for a fast testing and sorting system applied to battery clustering, in: 2017 ...
Molybdenum pentachloride (MoCl 5) was introduced to the pristine electrolyte, leading to the coordination of lithium polysulfides and the suppression of their intrinsic clustering to facilitate Li 2 S deposition and activation. Sulfur conversion reaction was accordingly promoted to demonstrate a high available capacity of Li-S batteries even under cryogenic environment.
This data‐driven clustering modeling with fast pulse test is a promising approach for clustering lithium‐ion batteries, which is demonstrated with a home‐built and high throughput intelligent clustering machine. In general, the technology opens a new generation of battery clustering, improving the efficiency and accuracy over the past ...
Jiang et al. [21] employed k-means clustering model to group weeded-out lithium batteries using three characteristic indicies namely capacity, ohmic resistance, and polarization
These so-called post-lithium batteries have the potential to store more energy, be safer, and offer a more cost-effective, long-term option for mass applications such as stationary and mobile electrochemical storage. ... as the only German Cluster of Excellence for battery research. POLiS is funded with 47 million euros over seven years.
In lithium-ion battery manufacturing, due to the variations of raw materials, manual operation and equipment, batteries performance differently from each other, which inevitably lead to a reduction in the available capacity and premature failure of a battery pack with multiple cells configured in series, parallel, and series-parallel. So it is important to screen inconsistent ones from ...
State of charge (SOC) estimation of power battery pack is critical for electric vehicles (EVs). During the service life of electric vehicles, the consistency of power pack changes due to manufacturing errors and different usage environments, which in turn affects the accuracy of battery pack SOC estimation. To improve the accuracy of pack SOC estimation while …
DOI: 10.1016/j.apenergy.2023.120841 Corpus ID: 257016653; Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering
A method to construct eigenvectors based on global feature extraction and cluster analysis for the k-means algorithm and self-organizing neural network is proposed. In this paper, we propose a method to construct eigenvectors based on global feature extraction and cluster analysis. Mainly for the k-means algorithm and self-organizing neural network has been …
16 new fast lithium conductors with conductivities ranging from 10-4 to 10-1 Scm-1. Here, it is proposed to cluster lithium-containing oxides, sulfides, selenides, and tellurides to identify the potential candidates. The analysis focuses on identifying conductivity trends among lithium ions within each cluster. Potential solid-state lithium-ion
This paper has adopted traditional K-means and improved K-means algorithms to solve the problem of retired lithium-ion battery clustering. The clustering process for …
Request PDF | On May 1, 2018, Zhiwei He and others published Feature time series clustering for lithium battery based on SOM neural network | Find, read and cite all the research you need on ...
This data-driven clustering modeling with fast pulse test is a promising approach for clustering lithium-ion batteries, which is demonstrated with a home-built and high throughput intelligent clustering machine.
Surface defects of lithium batteries seriously aect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and …
DOI: 10.1016/j.est.2024.111422 Corpus ID: 268808016; An enhanced sorting method for retired battery with feature selection and multiple clustering @article{Liu2024AnES, title={An enhanced sorting method for retired battery with feature selection and multiple clustering}, author={Tianqi Liu and Xi Chen and Qiao Peng and Jichang Peng and Jinhao Meng}, journal={Journal of Energy …
DOI: 10.1016/j.jclepro.2022.130786 Corpus ID: 246496429; Soft clustering of retired lithium-ion batteries for the secondary utilization using Gaussian mixture model based on electrochemical impedance spectroscopy
In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation.
All‐solid‐state polymer electrolyte‐based rechargeable batteries paired with high‐voltage cathodes and lithium anodes hold promising prospects to increase the energy density and the safety of lithium metal batteries (LMBs). To improve the stability of the polymer electrolytes at both the positive and negative electrodes, fluorinated polymer electrolytes have …
The parameter values from the performance assessment test reflect the current state of the batteries to varying degrees. As the lithium-ion battery is an integrated electrochemical system, there will be correlations between the performance data. Considering each parameter for clustering of batteries, would lead to very high computational ...
Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists …
Secondary utilization of retired lithium-ion batteries (LIBs) from electric vehicles could provide significant economic benefits. Herein, based on a short pulse test, we propose a two-step machine leaning method, which …
The capacity degradation behavior of lithium-ion batteries is the key object that the battery life management system needs to monitor in real time. Estimating the remaining service time of the battery through battery parameters such as capacity is one of the main tasks of the battery management system. Due to the complex chemical mechanism that causes the …
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