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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Institute of Electrical and Electronics Engineers Inc.
2016
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my.utm.734162017-11-20T08:43:00Z http://eprints.utm.my/id/eprint/73416/ State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter Yao, L. W. Aziz, J. A. Idris, N. R. N. TK Electrical engineering. Electronics Nuclear engineering 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. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item PeerReviewed Yao, L. W. and Aziz, J. A. and Idris, N. R. N. (2016) State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter. In: 2015 IEEE Conference on Energy Conversion, CENCON 2015, 19 - 20 Okt 2015, Johor Bahru, Malaysia. 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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TK Electrical engineering. Electronics Nuclear engineering Yao, L. W. Aziz, J. A. Idris, N. R. N. State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
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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. |
| format |
Conference or Workshop Item |
| author |
Yao, L. W. Aziz, J. A. Idris, N. R. N. |
| author_facet |
Yao, L. W. Aziz, J. A. Idris, N. R. N. |
| author_sort |
Yao, L. W. |
| title |
State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
| title_short |
State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
| title_full |
State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
| title_fullStr |
State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
| title_full_unstemmed |
State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter |
| title_sort |
state-of-charge estimation for lithium-ion battery using busse's adaptive unscented kalman filter |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2016 |
| url |
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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