State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method

Energy ◽  
2018 ◽  
Vol 144 ◽  
pp. 647-656 ◽  
Author(s):  
Yingzhi Cui ◽  
Pengjian Zuo ◽  
Chunyu Du ◽  
Yunzhi Gao ◽  
Jie Yang ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Parameters Identification ◽  
State Of Health ◽  
Identification Method ◽  
Diagnosis Model ◽  
Health Diagnosis

A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

2021 ◽  
Vol 45 (6) ◽  
pp. 9502-9517
Author(s):  
Heng Miao ◽  
Jiajun Chen ◽  
Ling Mao ◽  
Keqing Qu ◽  
Jinbin Zhao ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Parameters Identification ◽  
Model Parameters ◽  
Identification Method

Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Yuhao Huang ◽  
Yan Su ◽  
Akhil Garg
Keyword(s):  
Lithium Ion Batteries ◽  
Open Circuit Voltage ◽  
Prediction Method ◽  
Lithium Ion ◽  
Open Circuit ◽  
Constant Current ◽  
New Process ◽  
Artificial Neural Network Ann ◽  
Cv Model ◽  
Charge Discharge

Abstract A new process decomposed calculation method is developed to compare the cycle based charge, discharge, net, and overall energy efficiencies of lithium-ion batteries. Multi-cycle measurements for both constant current (CC) and constant current to constant voltage (CC-CV) charge models have been performed. Unlike most conventional efficiency calculation methods with one mean open-circuit voltage (OCV) curve, two OCV curves are calculated separately for the charge and discharge processes. These two OCV curves help to clarify the intra-cycle charge, discharge, net, and overall energy efficiencies. The relationships of efficiencies versus state of charge, state of quantity, and scaled stresses are demonstrated. Efficiency degradation patterns versus cycle numbers and scaled stresses are also illustrated with the artificial neural network (ANN) prediction method. The decaying ratios of the overall efficiencies are about 2% and 0.3% in the first 30 cycles, for CC and CC-CV, respectively. Hence, efficiencies of the CC-CV model are more stable compared with the CC model, which are shown by both experimental and ANN prediction results.

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