Research Papers

Lithium-Ion Battery Packs Formation With Improved Electrochemical Performance for Electric Vehicles: Experimental and Clustering Analysis

[+] Author and Article Information
Liu Yun, Jian Zhang

Intelligent Manufacturing Key Laboratory of
Ministry of Education,
Shantou University,
Guangdong, China

Jayne Sandoval

Intelligent Manufacturing Key Laboratory of
Ministry of Education,
Shantou University,
Guangdong, China;
Department of Mechanical Engineering,
Northern Arizona University,
Flagstaff, AZ 86011

Liang Gao

State Key Lab of Digital Manufacturing
Equipment and Technology,
School of Mechanical Science and Engineering,
Huazhong University of Science and Technology,
Wuhan 430074, China
e-mail: gaoliang@mail.hust.edu.cn

Akhil Garg

Intelligent Manufacturing Key Laboratory
of Ministry of Education,
Shantou University,
Guangdong, China

Chin-Tsan Wang

Department of Mechanical
and Electro-Mechanical Engineering,
National ILan University,
ILan, Taiwan

1Corresponding author.

Manuscript received May 18, 2018; final manuscript received November 17, 2018; published online January 18, 2019. Assoc. Editor: Bengt Sunden.

J. Electrochem. En. Conv. Stor. 16(2), 021011 (Jan 18, 2019) (11 pages) Paper No: JEECS-18-1045; doi: 10.1115/1.4042093 History: Received May 18, 2018; Revised November 17, 2018

With the increase of production of electrical vehicles (EVs) and battery packs, lithium ion batteries inconsistency problem has drawn much attention. Lithium ion battery imbalance phenomenon exists during three different stages of life cycle. First stage is premanufacturing of battery pack i.e., during the design, the cells of similar performance need to be clustered to improve the performance of pack. Second is during the use of battery pack in EVs, batteries equalization is necessary. In the third stage, clustering of spent lithium ion batteries for reuse is also an important problem because of the great recycling challenge of lithium batteries. In this work, several clustering and equalization methods are compared and summarized for different stages. The methods are divided into the traditional methods and intelligent methods. The work also proposes experimental combined clustering analysis for new lithium-ion battery packs formation with improved electrochemical performance for electric vehicles. Experiments were conducted by dismantling of pack and measurement of capacity, voltage, and internal resistance data. Clustering analysis based on self-organizing map (SOM) neural networks is then applied on the measured data to form clusters of battery packs. The validation results conclude that the battery packs formed from the clustering analysis have higher electrochemical performance than randomly selected ones. In addition, a comprehensive discussion was carried out.

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Fig. 1

Battery inconsistency problem

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Fig. 2

Active equalization and passive equalization methods

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Fig. 3

Second use process for battery packs

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Fig. 4

Second use screening process for battery packs

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Fig. 5

Comprehensive methodology based on experiments and SOM neural network

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Fig. 6

Process of forming new battery pack and experimental validation

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Fig. 7

Capacity of different battery packs during performance validation experiment

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Fig. 8

Temperature performance difference between pack 1, pack 2 and pack 3



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