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.
Full text not available from this repository.Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | undamental Research Grant Scheme under UM grant (FRGS/1/2018/TK07/UM/02/4), undamental Research Grant Scheme under UM grant (FP095-2018A) |
| Additional Information: | IEEE 9th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Nanjing, Peoples R China, Nov 29-Dec 02, 2020 |
| Uncontrolled Keywords: | Lithium-ion battery; State of charge; Kalman filter; Battery management System; Electric vehicle |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 14 Apr 2023 03:52 |
| Last Modified: | 14 Apr 2023 03:52 |
| URI: | http://eprints.um.edu.my/id/eprint/37178 |
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