State‐of‐health estimation for the lithium‐ion battery based on gradient boosting decision tree with autonomous selection of excellent features

2021 ◽  
Author(s):  
Zhiqi Zhang ◽  
Li Li ◽  
Xi Li ◽  
Yuchen Hu ◽  
Kai Huang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Gradient Boosting ◽  
State Of Health ◽  
Selection Of

scholarly journals State-of-health prediction of lithium-ion battery based on improved gate recurrent unit

2021 ◽  
Vol 2010 (1) ◽  
pp. 012142
Author(s):  
Jingdong Lin ◽  
Chang Wang ◽  
Guansong Yan
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Health

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

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