| Autor: |
Ning Ding, Krishnamachar Prasad, Tek Tjing Lie, Jinhui Cui |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Inventions, Vol 4, Iss 4, p 66 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2411-5134 |
| DOI: |
10.3390/inventions4040066 |
| Popis: |
The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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