Shrivastava, Prashant and Tey, Kok Soon and Idris, Mohd Yamani Idna and Mekhilef, Saad and Syed Adnan, Syed Bahari Ramadzan (2022) Comprehensive co-estimation of lithium-ion battery state of charge, state of energy, state of power, maximum available capacity, and maximum available energy. Journal of Energy Storage, 56 (B). ISSN 2352-152X, DOI https://doi.org/10.1016/j.est.2022.106049.
Full text not available from this repository.Abstract
In developing an efficient battery management system (BMS), accurate battery state estimation is always required. However, the trade-off between computational efficiency and accuracy of state estimation is hard to maintain. This work proposes the comprehensive co-estimation method for battery states, maximum available capacity, and maximum available energy estimation. The existing correlation between different battery states is effectively utilized to achieve high accuracy and reduce the computational burden. A combined state of charge (SOC) and state of energy (SOE) estimation using the dual forgetting factor adaptive extended Kalman filter (DFFAEKF) algorithm and experimental quantitative relations between SOC and SOE are utilized to estimate the SOC and SOE. Due to low computational cost and simplicity, the multiple constraints model-based SOP esti-mation using the Rint model is employed. The maximum available capacity and maximum available energy estimation are performed using a new sliding window-approximate weighted total least square (SW-AWTLS) algorithm. The performance of the proposed co-estimation method is validated by two different chemistry battery cells under dynamic load profiles at different operating temperatures. Moreover, the comparison with other existing co-estimation methods is also conducted, whose results indicate the superior accuracy of the proposed comprehensive co-estimation method.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia (Grant No: LRGS/1/2019/UKM/01/6/3), University of Malaya for UM Matching (Grant No; PV018-2022) |
| Uncontrolled Keywords: | Electric vehicle; Lithium-ion battery; State estimation; Battery modeling; Extended Kalman filter |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Centre for Foundation Studies in Science Faculty of Computer Science & Information Technology > Department of Computer System & Technology Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 23 Nov 2023 02:54 |
| Last Modified: | 23 Nov 2023 02:54 |
| URI: | http://eprints.um.edu.my/id/eprint/40334 |
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