A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Yang, Yuxiang; Gao, Mingyu; He, Zhiwei; Wang, Caisheng

doi:10.3390/en10071035

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…e K-means algorithm is one of the easiest ways to teach without a teacher. It automatically identifies clusters and central points without the need for tags [29].…”

Section: E Principle Of K-means Algorithmmentioning

confidence: 99%

Lithium Battery Allocation Decision-Making Scheme Based on K-Means Algorithm

Qin

2022

Mathematical Problems in Engineering

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Lithium-ion batteries, the core components of electric vehicles, have received unprecedented attention and undergone development in the era of huge energy demand. The traditional clustering algorithm cannot meet the requirement of the consistency of lithium battery distribution. In this study, we provide an improved K-means algorithm to meet the battery distribution needs of enterprises and combine it with reality. This model includes an early data processing model and a battery comparison method based on the new K-means algorithm. In the battery data processing model, the preprocessing process approach and actual production standards preclude problematic batteries. In the battery comparison algorithm, the number of batteries in each cluster becomes equal after the battery comparison. The algorithm can ensure the internal characteristics of lithium-ion power batteries, and, at the same time, after the matching is completed, the number of lithium batteries in each cluster is equal.

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“…e K-means algorithm is one of the easiest ways to teach without a teacher. It automatically identifies clusters and central points without the need for tags [29].…”

Section: E Principle Of K-means Algorithmmentioning

confidence: 99%

Lithium Battery Allocation Decision-Making Scheme Based on K-Means Algorithm

Qin

2022

Mathematical Problems in Engineering

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“…Reference [75] selects three algorithms to compare, including principal component analysis, kernel principal component analysis, and random forest, and ultimately verifies that random forest (RF) has the best sorting effect. References [76][77][78][79][80][81] adopt K-means, take a variety of factors into account, and demonstrate that it has high reliability. Reference [82] uses k-means to judge the condition of charging facilities.…”

Section: Clustering Algorithmmentioning

confidence: 99%

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

Zhang

et al. 2020

WEVJ

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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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A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Chen

Peng

et al. 2019

Engineering

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A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Cited by 9 publications

References 25 publications

Lithium Battery Allocation Decision-Making Scheme Based on K-Means Algorithm

Lithium Battery Allocation Decision-Making Scheme Based on K-Means Algorithm

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

A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

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