Cycle Life Prediction Method of Lithium Batteries Based on Morphology and Properties

doi:10.3969/j.issn.1000-565X.2018.04.001

Journal of South China University of Technology (Natural Science Edition) ›› 2018, Vol. 46 ›› Issue (4): 1-7.doi: 10.3969/j.issn.1000-565X.2018.04.001

• Traffic & Transportation Engineering • Next Articles

Cycle Life Prediction Method of Lithium Batteries Based on Morphology and Properties

LI Lifu ZHANG Dongyu

School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China

Received:2017-08-18 Revised:2017-12-20 Online:2018-04-25 Published:2018-03-01
Contact: 李礼夫( 1955) ，男，教授，博士，主要从事光机电一体化研究 E-mail:pmlfli@scut.edu.cn
About author:李礼夫( 1955) ，男，教授，博士，主要从事光机电一体化研究
Supported by:
Supported by the Special Funds for Public Welfare Ｒesearch and Capacity Building of Guangdong Province ( 2014B010106004)

Abstract

Abstract: The conventional prediction model for cycle life of lithium power batteries may generate prediction error， because it ignores the impact of the structural changes caused by electrochemical reactions． To solve this problem， the lithium iron phosphate batteries are regarded as the research objects for the study of the relationship between the internal structure and the cycle life of lithium power battery． The prediction method based on morphology and electrochemical properties is proposed． And a prediction model for cycle life of power battery ( BLPC model) is established on the basis of the morphology and electrochemical properties． The corresponding power battery cycle life test system ( BLPC system) is designed to carry out the cycle life experiment of the lithium iron phosphate batteries and verify the validity of BLPC model． The experimental results show that the new prediction method is more reliable and practical than those conventional ones．

Key words:

lithium-ion batteries, cycle life, prediction, computerized tomography

CLC Number:

U469.72

LI Lifu ZHANG Dongyu .

Cycle Life Prediction Method of Lithium Batteries Based on Morphology and Properties

[J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(4): 1-7.

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Cycle Life Prediction Method of Lithium Batteries Based on Morphology and Properties

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