State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model... State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Power Sources Elsevier

State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0378-7753
D.O.I.
10.1016/j.jpowsour.2018.02.058
Publisher site
See Article on Publisher Site

Abstract

State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence.

Journal

Journal of Power SourcesElsevier

Published: Apr 15, 2018

References

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