MARC表示: Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application

Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application

To build up a proficient battery management system, it is required to accurately estimate the state of charge (SOC) of the electric vehicle (EV) battery. Generally, the accuracy of the conventional extended Kalman Filter (CEKF) algorithm is exceptionally affected by the method used to update the noi...

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主要な著者:	Shrivastava, Prashant, Soon, Tey Kok, Bin Idris, Mohd Yamani Idna, Mekhilef, Saad
フォーマット:	Conference or Workshop Item
出版事項:	IEEE 2020
主題:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
オンライン･アクセス:	http://eprints.um.edu.my/37178/ https://ieeexplore.ieee.org/document/9367743
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id	my.um.eprints.37178
record_format	eprints
spelling	my.um.eprints.371782023-04-14T03:52:43Z http://eprints.um.edu.my/37178/ Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application Shrivastava, Prashant Soon, Tey Kok Bin Idris, Mohd Yamani Idna Mekhilef, Saad QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering To build up a proficient battery management system, it is required to accurately estimate the state of charge (SOC) of the electric vehicle (EV) battery. Generally, the accuracy of the conventional extended Kalman Filter (CEKF) algorithm is exceptionally affected by the method used to update the noise covariance matrices under running conditions. In this work, the new adaptive extended Kalman filter (AEKF) algorithm is designed for the SOC estimation. Methods such as forgetting factor method and moving window are used for estimation of measurement noise and sensor noise covariance matrix respectively. Pulse discharge and customized dynamic stress tests are conducted to check the robustness of the proposed algorithm. Experimental results indicated that proposed AEKF has superior performance than CEKF under dynamic load conditions. IEEE 2020 Conference or Workshop Item PeerReviewed Shrivastava, Prashant and Soon, Tey Kok and Bin Idris, Mohd Yamani Idna and Mekhilef, Saad (2020) Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application. In: 2020 IEEE 9th International Power Electronics and Motion Control Conference IPEMC2020-ECCE ASIA), 29 November - 02 December 2020, Nanjing, China. https://ieeexplore.ieee.org/document/9367743
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Shrivastava, Prashant Soon, Tey Kok Bin Idris, Mohd Yamani Idna Mekhilef, Saad Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
description	To build up a proficient battery management system, it is required to accurately estimate the state of charge (SOC) of the electric vehicle (EV) battery. Generally, the accuracy of the conventional extended Kalman Filter (CEKF) algorithm is exceptionally affected by the method used to update the noise covariance matrices under running conditions. In this work, the new adaptive extended Kalman filter (AEKF) algorithm is designed for the SOC estimation. Methods such as forgetting factor method and moving window are used for estimation of measurement noise and sensor noise covariance matrix respectively. Pulse discharge and customized dynamic stress tests are conducted to check the robustness of the proposed algorithm. Experimental results indicated that proposed AEKF has superior performance than CEKF under dynamic load conditions.
format	Conference or Workshop Item
author	Shrivastava, Prashant Soon, Tey Kok Bin Idris, Mohd Yamani Idna Mekhilef, Saad
author_facet	Shrivastava, Prashant Soon, Tey Kok Bin Idris, Mohd Yamani Idna Mekhilef, Saad
author_sort	Shrivastava, Prashant
title	Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
title_short	Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
title_full	Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
title_fullStr	Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
title_full_unstemmed	Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
title_sort	battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
publisher	IEEE
publishDate	2020
url	http://eprints.um.edu.my/37178/ https://ieeexplore.ieee.org/document/9367743
_version_	1764222944731463680
score	13.251813

Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application

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