Battery Cycle Life Prediction with Coupled Chemical Degradation and Fatigue Mechanics

2012 ◽  
Vol 159 (10) ◽  
pp. A1730-A1738 ◽  
Author(s):  
Rutooj Deshpande ◽  
Mark Verbrugge ◽  
Yang-Tse Cheng ◽  
John Wang ◽  
Ping Liu
Keyword(s):  
Life Prediction ◽  
Cycle Life ◽  
Chemical Degradation

scholarly journals Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4671
Author(s):  
Danpeng Cheng ◽  
Wuxin Sha ◽  
Linna Wang ◽  
Shun Tang ◽  
Aijun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Life Prediction ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Cycle Life ◽  
Quantitative Prediction ◽  
Test Accuracy ◽  
Machine Learning Algorithm ◽  
Discharge Data

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

Cycle life prediction and match detection in retired electric vehicle batteries

2013 ◽  
Vol 23 (10) ◽  
pp. 3040-3045 ◽  
Author(s):  
Xiang-yang ZHOU ◽  
You-lan ZOU ◽  
Guang-jin ZHAO ◽  
Juan YANG
Keyword(s):  
Electric Vehicle ◽  
Life Prediction ◽  
Cycle Life
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