scholarly journals Analysis of equivalent circuit models in lithium-ion batteries

AIP Advances ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 125101 ◽  
Author(s):  
Hyun Woo You ◽  
Jun Ik Bae ◽  
So Jeong Cho ◽  
Jong Moon Lee ◽  
Se-Hun Kim
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Lithium Ion ◽  
Equivalent Circuit Models

scholarly journals Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

Energies ◽  
2016 ◽  
Vol 9 (5) ◽  
pp. 360 ◽  
Author(s):  
Alexandros Nikolian ◽  
Yousef Firouz ◽  
Rahul Gopalakrishnan ◽  
Jean-Marc Timmermans ◽  
Noshin Omar ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Lithium Ion ◽  
Electrical Equivalent Circuit ◽  
Nickel Manganese ◽  
Equivalent Circuit Models

scholarly journals Bridging physics-based and equivalent circuit models for lithium-ion batteries

2021 ◽  
Vol 372 ◽  
pp. 137829
Author(s):  
Zeyang Geng ◽  
Siyang Wang ◽  
Matthew J. Lacey ◽  
Daniel Brandell ◽  
Torbjörn Thiringer
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Lithium Ion ◽  
Equivalent Circuit Models

scholarly journals Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

2017 ◽  
Vol 7 (10) ◽  
pp. 1002 ◽  
Author(s):  
Lijun Zhang ◽  
Hui Peng ◽  
Zhansheng Ning ◽  
Zhongqiang Mu ◽  
Changyan Sun
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Electric Vehicles ◽  
Comparative Research ◽  
Lithium Ion ◽  
Equivalent Circuit Models

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

2021 ◽  
pp. 1-28
Author(s):  
Yanbo Che ◽  
Yibin Cai ◽  
Hongfeng Li ◽  
Yushu Liu ◽  
Mingda Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Variable Order ◽  
Nonlinear Correlation ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

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