Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

2021 ◽  
pp. 2101474
Author(s):  
Chade Lv ◽  
Xin Zhou ◽  
Lixiang Zhong ◽  
Chunshuang Yan ◽  
Madhavi Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Materials Development ◽  
State Prediction

scholarly journals Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 723
Author(s):  
Saurabh Saxena ◽  
Darius Roman ◽  
Valentin Robu ◽  
David Flynn ◽  
Michael Pecht
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Consumer Electronics ◽  
Stress Factors ◽  
Test Time ◽  
Capacity Fade ◽  
Maximum Acceleration ◽  
Accelerated Degradation ◽  
Qualification Testing

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 122185
Author(s):  
Tao Sun ◽  
Shaoqing Wang ◽  
Sheng Jiang ◽  
Bowen Xu ◽  
Xuebing Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Dynamic Weight ◽  
Weight Allocation

Estimation of Potentials in Lithium-Ion Batteries Using Machine Learning Models

2021 ◽  
pp. 1-16
Author(s):  
Weihan Li ◽  
Damas W. Limoge ◽  
Jiawei Zhang ◽  
Dirk Uwe Sauer ◽  
Anuradha M. Annaswamy
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Learning Models ◽  
Machine Learning Models
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