Prognostics for lithium-ion batteries using a two-phase gamma degradation process model

Author(s):  
Chun Pang Lin ◽  
Man Ho Ling ◽  
Javier Cabrera ◽  
Fangfang Yang ◽  
Denis Yau Wai Yu ◽  
...  
2017 ◽  
Vol 66 (4) ◽  
pp. 1345-1360 ◽  
Author(s):  
Dejing Kong ◽  
Narayanaswamy Balakrishnan ◽  
Lirong Cui

Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 405-410 ◽  
Author(s):  
Thomas Knoche ◽  
Florian Surek ◽  
Gunter Reinhart

2013 ◽  
Vol 242 ◽  
pp. 736-741 ◽  
Author(s):  
Jie Xiao ◽  
Xiqian Yu ◽  
Jianming Zheng ◽  
Yungang Zhou ◽  
Fei Gao ◽  
...  

2017 ◽  
Vol 169 ◽  
pp. 245-252 ◽  
Author(s):  
Daniela da Silveira Leite ◽  
Pablo Luis Gutierrez Carvalho ◽  
Leandro Rodrigues de Lemos ◽  
Aparecida Barbosa Mageste ◽  
Guilherme Dias Rodrigues

2020 ◽  
Author(s):  
Salim Erol

In this study, a simulation of an electrochemical impedance spectroscopy for lithium-ion batteries was proposed. The electrochemical process was developed from battery electrode kinetics and mass transfer of mobile Li+ ion through negative and positive electrodes and electrolyte. The phenomena used in this process were represented by an equivalent electrical circuit. A mathematical model was designed using the equivalent circuit and its elements which are in fact battery parameters. The parameter values were presented as compared with real experimental impedance result.


Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5000
Author(s):  
Haipeng Pan ◽  
Chengte Chen ◽  
Minming Gu

Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.


2020 ◽  
Vol 173 ◽  
pp. 115213 ◽  
Author(s):  
Seong Ho Hong ◽  
Dong Soo Jang ◽  
Seonggi Park ◽  
Sungho Yun ◽  
Yongchan Kim

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