| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
Cycle Life Prediction of Lithium Ion Batteries Based on Data Driven Methods |
| Author(s) |
Laisuo Su, Mengchen Wu, B. Reeja Jayan |
| On-Site Speaker (Planned) |
Laisuo Su |
| Abstract Scope |
Machine learning algorithms are much better to learn hidden features for complex, nonlinear systems than human expects. Those hidden features are crucial for many applications, like mode identification and performance prediction. In this study, we compare the ability between human experts and machine learning algorithms for capturing features to predict lifetime of lithium ion batteries (LIBs). We generate a comprehensive dataset with 104 commercial LiNi0.8Co0.15Al0.05O2/graphite 18650-series batteries under wide range of test conditions. Based on charge and discharge curves, we capture 20 different features that relate to the lifetime of LIBs. The best prediction error is around 50% based on those human captured features using linear regression method and neural network model. In comparison, a convolution neural network (CNN) that captures hidden features can predict cycle life with less than 10% error. This study demonstrates the advantages of applying machine learning algorithm for capturing hidden features for complex, nonlinear systems. |