Capacity estimation of large-scale retired li-ion batteries for second use based on support vector machine

2014 ◽  
Author(s):  
Zheng Fangdan ◽  
Jiang Jiuchun ◽  
Zhang Weige ◽  
Sun Bingxiang ◽  
Zhang Caiping ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Support Vector ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Li Ion ◽  
Second Use

scholarly journals Charge/discharge profiles of LiFePO4 repurposing batteries based on UL 1974

2020 ◽  
Author(s):  
Hsien-Ching Chung
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Li Ion Batteries ◽  
Test Procedures ◽  
Safety Certification ◽  
Li Ion ◽  
And Performance ◽  
Discharge Profile ◽  
Charge Discharge ◽  
Second Use

Owing to the popularization of electric vehicles worldwide and the development of renewable energy supply, the Li-ion batteries are widely used from small-scale personal mobile products to large-scale energy storage systems. Recently, the number of retired power batteries has largely increased, causing environmental protection threats and waste of resources. Since most of the retired power batteries still possess about 80% initial capacity, the second use of them becomes a possible route to solve the emergency problem. The safety and performance are important in using these second-use repurposing batteries. Underwriters Laboratories (UL), a global safety certification company, published the standard for evaluating the safety and performance of repurposing batteries, i.e., UL 1974. In this work, the test procedures are designed according to UL 1974 and the charge/discharge profile dataset of the LiFePO4 repurposing batteries provided. Researchers/engineers can use the characteristic curves in estimating the repurposing batteries under UL 1974. Furthermore, the profile dataset can be applied in the model-based engineering of repurposing batteries, e.g., fitting the variables of an empirical model or validating the results of a theoretical model.

scholarly journals A sparse least squares support vector machine used for SOC estimation of Li-ion Batteries

2019 ◽  
Vol 52 (11) ◽  
pp. 256-261 ◽  
Author(s):  
Li Zhang ◽  
Kang Li ◽  
Dajun Du ◽  
Chunbo Zhu ◽  
Min Zheng
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Li Ion Batteries ◽  
Soc Estimation ◽  
Li Ion

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Energy ◽  
2015 ◽  
Vol 86 ◽  
pp. 638-648 ◽  
Author(s):  
Junfu Li ◽  
Lixin Wang ◽  
Chao Lyu ◽  
Liqiang Zhang ◽  
Han Wang
Keyword(s):  
Particle Filter ◽  
Discharge Capacity ◽  
Operating Conditions ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Li Ion

scholarly journals Impact of Surface Chemistry of Silicon Nanoparticles on the Structural and Electrochemical Properties of Si/Ni3.4Sn4 Composite Anode for Li-Ion Batteries

Nanomaterials ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Tahar Azib ◽  
Claire Thaury ◽  
Fermin Cuevas ◽  
Eric Leroy ◽  
Christian Jordy ◽  
...  
Keyword(s):  
Surface Chemistry ◽  
Electrochemical Properties ◽  
Large Scale ◽  
Silicon Nanoparticles ◽  
Lithium Ion ◽  
Li Ion Batteries ◽  
Chemical Homogeneity ◽  
Electrochemical Behaviors ◽  
Pure Silicon ◽  
Li Ion

Embedding silicon nanoparticles in an intermetallic matrix is a promising strategy to produce remarkable bulk anode materials for lithium-ion (Li-ion) batteries with low potential, high electrochemical capacity and good cycling stability. These composite materials can be synthetized at a large scale using mechanical milling. However, for Si-Ni3Sn4 composites, milling also induces a chemical reaction between the two components leading to the formation of free Sn and NiSi2, which is detrimental to the performance of the electrode. To prevent this reaction, a modification of the surface chemistry of the silicon has been undertaken. Si nanoparticles coated with a surface layer of either carbon or oxide were used instead of pure silicon. The influence of the coating on the composition, (micro)structure and electrochemical properties of Si-Ni3Sn4 composites is studied and compared with that of pure Si. Si coating strongly reduces the reaction between Si and Ni3Sn4 during milling. Moreover, contrary to pure silicon, Si-coated composites have a plate-like morphology in which the surface-modified silicon particles are surrounded by a nanostructured, Ni3Sn4-based matrix leading to smooth potential profiles during electrochemical cycling. The chemical homogeneity of the matrix is more uniform for carbon-coated than for oxygen-coated silicon. As a consequence, different electrochemical behaviors are obtained depending on the surface chemistry, with better lithiation properties for the carbon-covered silicon able to deliver over 500 mAh/g for at least 400 cycles.

Ternary Cu2SnS3 cabbage-like nanostructures: large-scale synthesis and their application in Li-ion batteries with superior reversible capacity

Nanoscale ◽  
2011 ◽  
Vol 3 (10) ◽  
pp. 4389 ◽  
Author(s):  
Baihua Qu ◽  
Hongxing Li ◽  
Ming Zhang ◽  
Lin Mei ◽  
Libao Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Reversible Capacity ◽  
Li Ion Batteries ◽  
Li Ion ◽  
Large Scale Synthesis

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine

2019 ◽  
Vol 68 (9) ◽  
pp. 8583-8592 ◽  
Author(s):  
Xuning Feng ◽  
Caihao Weng ◽  
Xiangming He ◽  
Xuebing Han ◽  
Languang Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
State Of Health ◽  
Li Ion Battery ◽  
Li Ion
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