Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical Engineering | en_US |
| dc.contributor.advisor | Lam, Kwok-ho (EE) | en_US |
| dc.contributor.advisor | Shi, Dongliang (EE) | en_US |
| dc.creator | Yang, Fan | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11864 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Modified extended kalman filtering method on state-of-charge estimation of batteries | en_US |
| dcterms.abstract | The estimation of the State-of-Charge (SOC) on battery has become a global research hotspot with the rapid development of electric vehicles and electric-vehicle related technologies. The estimation of the SOC on batteries is an important part of the power battery management system (BMS) of the electric vehicles. To solve the problems of low accuracy and poor stability of SOC estimation on batteries, the key techniques of extended Kalman filtering (EKF) and ampere-hour (Ah) integration are studied and combined effectively in this study. The error of the EKF method is reduced by investigating the noises. Besides, this study improves the accuracy of the estimated voltage in the EKF. The lithium-ion polymer battery of Lede technical corporation in Shenzhen is employed as the target. The research focuses on the study of rechargeable battery characteristics, the establishment of the equivalent model of the battery, the parameter identification of equivalent model of battery, and the estimation of SOC on battery. Finally, Matlab software is used to verify the reliability of the algorithm. | en_US |
| dcterms.abstract | Firstly, the characteristics of lithium-ion batteries are studied in depth. The calculation method and characteristics of the relation curves between the open-circuit voltage (OCV) and SOC are explored. The effects of discharge rate and temperature on lithium-ion batteries are also studied. | en_US |
| dcterms.abstract | The equivalent battery model is established according to the characteristics of lithium-ion batteries. The parameter identification method adopts the off-line parameter identification. The accuracy of the EKF method is found to be improved by investigating the relationship between the white noise and processing noise. The error of the aforementioned method can be within 5%. | en_US |
| dcterms.abstract | The proposed EKF method is validated in practical applications with actual working current. However, there are errors in estimated voltages when the value of the current changes. As the modification of Kalman gains during the change of current is found to influence the accuracy of the voltage, the error between the estimated voltage and actual voltage can be reduced by the adjustment of the noise and Kalman gains. Finally, the method is proved to be feasible in actual working current. | en_US |
| dcterms.extent | vii, 58 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2020 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | lithium-ion batteries | en_US |
| dcterms.LCSH | Electric vehicles--Batteries. | en_US |
| dcterms.LCSH | Electric batteries | en_US |
| dcterms.LCSH | Kalman filtering | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6352.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.29 MB | Adobe PDF | View/Open |
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