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Lotfivand, A, Yu, D and Gomm, B (2022) State Of Charge estimation using Extended Kalman Filter in Electric Vehicles. In: 2022 27th International Conference on Automation and Computing (ICAC) . (2022 27th International Conference on Automation and Computing (ICAC), 1st - 3rd September 2022, Bristol, United Kingdom).
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Abstract
Lithium-ion batteries are in the top priority among other batteries because of being safe, quick charging, and long-life cycle and are widely used in electric vehicles for having precise battery model, it is essential to decide factors like state of health and state of charge. In this paper using LA92 drive cycle experiment data is used for the state of charge estimation algorithm Firstly, for imitating the lithium-ion batterie's behavior in accurate way, a mathematical model of analogous battery is required. The 2RC branches are included in Thevenin model and Hybrid Pulse Power Characterization (HPPC) test data achieved at 40°C, 25°C, 10°C, 0°C, and -10°C is used to calculate the SOC 3-dimensional curve as a function of SOC and T. Coulomb counting (CC) and extended Kalman Filter method were observed for estimating the state of charge. The results show that EKF is more precise than CC (Coulomb Counting). The mistake of estimation with EKF is less than 1% that shows the reliability of the algorithm.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Divisions: | Engineering |
| Publisher: | IEEE |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 29 Aug 2023 12:49 |
| Last Modified: | 27 Sep 2023 09:53 |
| DOI or ID number: | 10.1109/ICAC55051.2022.9911137 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/20919 |
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