Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

Li, Ran; Xu, Shihui; Zhou, Yongqin; Li, Sibo; Yao, Jie; Zhou, Kai; Liu, Xianzhong

doi:10.1109/access.2019.2962835

Cited by 9 publications

(6 citation statements)

References 30 publications

(21 reference statements)

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“…Therefore, based on the data assigned to construct the model, as well as data that did not influence the model (new data), the performance and efficiency of the model should be examined. [ 54,63 ]…”

Section: Methodsmentioning

confidence: 99%

“…The relationships between these factors for successive iterations at time ( t ) and ( t + 1 ) are presented in Equations (10 and 11), where r represents a random number in the range of 0 and 1. The C p and C g control in this algorithm results in the exploitation of optimal values in the objective function [ 46,51–56 ]

X_{i} (t + 1) = X_{i} (t) + V_{i} (t + 1)

\begin{matrix} true \\ right V_{i} ((), t + 1) & = & left w \times V_{i} ((), t) + C_{p} \times r \times ((), X_{i}^{p b e s t} - X_{i} ((), t)) \\ left + C_{g} \times r \times ((), X_{i}^{g b e s t} - X_{i} ((), t)) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

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Numerous studies have been performed to modify ferrites to achieve the desired magnetic properties for the intended applications. Many variables with interactions between themselves are involved in modifying these properties. This study aims to provide an accurate and effective method for predicting the magnetic properties of M‐type ferrites under the influence of involved variables. For this purpose, ferrites are synthesized by doping 17 different ions and the results of the analysis of samples form the experimental data. The support vector regression (SVR) model is selected for prediction and in order to optimize hyper parameters, genetic, particle swarm optimization, and sequential minimal optimization techniques are used. Based on the comparison between the outcomes of these algorithms, the model with the best performance is used to predict coercivity and residual magnetism in M‐type magnetic ferrites. Furthermore, with the cross validation technique, the accuracy and robustness of the model designed for new samples are evaluated. The results show that the selected SVR model, with an average absolute relative error of 2% and 0.7%, is able to predict coercivity and residual magnetism, respectively. Consequently, the prediction method presented has the capability to accelerate and develop the modification of magnetic properties for different applications.

show abstract

Section: Methodsmentioning

confidence: 99%

X_{i} (t + 1) = X_{i} (t) + V_{i} (t + 1)

\begin{matrix} true \\ right V_{i} ((), t + 1) & = & left w \times V_{i} ((), t) + C_{p} \times r \times ((), X_{i}^{p b e s t} - X_{i} ((), t)) \\ left + C_{g} \times r \times ((), X_{i}^{g b e s t} - X_{i} ((), t)) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…[1,2] Moreover, benefited from the advantages of stable discharge potential, high voltage accuracy and reliable safety, ZnÀ Ag batteries show great potential in energy storage market. [3][4][5] However, the manufacturing cost of ZnÀ Ag batteries is extraordinarily high because of the high price of Ag resource. Hence, current ZnÀ Ag batteries is mainly applied in military and aerospace fields ( e. g., torpedo, missile, and space vehicle).…”

Section: Statusmentioning

confidence: 99%

“…Zinc‐silver (Zn−Ag) battery is one of the widely studied alkaline battery with high theoretical energy density of 350 Wh kg −1 and 750 Wh L −1 [1,2] . Moreover, benefited from the advantages of stable discharge potential, high voltage accuracy and reliable safety, Zn−Ag batteries show great potential in energy storage market [3–5] . However, the manufacturing cost of Zn−Ag batteries is extraordinarily high because of the high price of Ag resource.…”

Section: Zinc‐silver Batteriesmentioning

confidence: 99%

2020 Roadmap on Zinc Metal Batteries

Zhang

et al. 2020

Chemistry An Asian Journal

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Metallic zinc (Zn) is considered to be a safe and low-cost anode for rechargeable batteries. Thus, several zinc metal batteries (ZMBs) have been well developed, i. e., zinc silver batteries, ZnÀ MnO 2 batteries, nickel-zinc batteries, zincair batteries and other kinds of zinc ion batteries. ZMBs are strongly correlated with electrochemistry, electrodes, electrolytes and battery structures. To support the development of this field, we herein invite some famous research experts to write this roadmap for giving some guidance in future research. The roadmap will include their research status, challenge, opportunities, and the technology advance in their fields. We expect this roadmap will produce active influence in developing green, low-cost, safe ZMBs for our bright life in future.

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“…The method uses the clustering validity function to determine the optimal class number, two times of FCM sorting algorithm in optimal results, using an extrusion algorithm. Reference [98] uses a combination of FCM and particle swarm optimization algorithm-least squares support vector machine (PSO-LSSVM) for battery sorting; the method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, and enhances model training speed and accuracy. Reference [99] clusters charge-discharge characteristic curves to finish sorting.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

Self Cite

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To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.

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Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

Cited by 9 publications

References 30 publications

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

2020 Roadmap on Zinc Metal Batteries

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

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