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A novel state-of-charge estimation method of lithium-ion batteries combining the Grey model and Genetic Algorithms

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journal contribution
posted on 2018-01-19, 10:26 authored by Lin Chen, Zhengzheng Wang, Zhiqiang Lü, Junzi Li, Bing Ji, Haiyan Wei, Haihong Pan
In order to guarantee safe and reliable operation of battery in electric vehicles and utilizing capacity at the greatest extent, it is indispensable to estimate the state-of-charge (SoC) of battery. This study aimed to develop such a novel estimation approach based on the Grey model and Genetic Algorithms method without the need of a high computation cost and high-fidelity battery model. A SoC analytical model was established using the Grey System theory based on a limited amount of incomplete data compared to conventional methods. The model was further improved by applying the sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the Genetic Algorithms were introduced to achieve an optimal adjustment coefficient, < formula > < tex > $\lambda$ < /tex > < /formula > , in the traditional Grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of Lithium-ion batteries were used as the device-under-test, and the accuracy and repeatability of the proposed modeling method were verified under a range of battery discharging conditions. The results indicate that the proposed modeling approach features a higher accuracy for such systems compared to the benchmarking GM method that is illustrated using typical passenger car driving cycles.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No.51267002, Grant No.51667006), Guangxi Natural Science Foundation (Grant No.2015GXNSFAA139287), Innovation Project of Guangxi Graduate Education (Grant No.YCSW2017038), and Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (Grant No.15-140-30S002).

History

Citation

IEEE Transactions on Power Electronics, 2017, PP(99)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Power Electronics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

0885-8993

eissn

1941-0107

Acceptance date

2017-11-27

Copyright date

2017

Available date

2018-01-19

Publisher version

http://ieeexplore.ieee.org/document/8187676/

Language

en

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