State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter
State-of-charge estimation of rechargeable battery is vital to maximize the battery performance and ensure the safe operating condition. This paper presents state-of-charge estimation method for lithium-ion battery using adaptive unscented Kalman Filter. In this aspect, Busse's adaptive rule is...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73416/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964327219&doi=10.1109%2fCENCON.2015.7409544&partnerID=40&md5=88970ec4547b9b2a79bf1f7717854a93 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | State-of-charge estimation of rechargeable battery is vital to maximize the battery performance and ensure the safe operating condition. This paper presents state-of-charge estimation method for lithium-ion battery using adaptive unscented Kalman Filter. In this aspect, Busse's adaptive rule is implemented to update the process noise covariance of the Kalman filter. Compared with the existing adaptive rules, Busse's rule is relatively simpler and it doesn't require huge memory capacity for storing the voltage residual. The accuracy of the proposed method is verified through experimental studies. A comparison with the unscented Kalman filter algorithms is made to compare the accuracy of each algorithm. |
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