A comparative study based on the least square parameter identification method for state of charge estimation of a lifepo4 battery pack using three model-based algorithms for electric vehicles
文献类型：期刊论文
| 作者 | Zahid, Taimoor1,2,3; Li, Weimin1,2,3 |
| 刊名 | Energies
 |
| 出版日期 | 2016-09-01 |
| 卷号 | 9期号:9页码:16 |
| 关键词 | Battery management system Lithium ion batteries State of charge (soc) estimation Extended kalman filter (ekf) Unscented kalman filter (ukf) Particle filter (pf) |
| ISSN号 | 1996-1073 |
| DOI | 10.3390/en9090720 |
| 通讯作者 | Li, weimin(wm.li@siat.ac.cn) |
| 英文摘要 | Battery energy storage management for electric vehicles (ev) and hybrid ev is the most critical and enabling technology since the dawn of electric vehicle commercialization. a battery system is a complex electrochemical phenomenon whose performance degrades with age and the existence of varying material design. moreover, it is very tedious and computationally very complex to monitor and control the internal state of a battery's electrochemical systems. for thevenin battery model we established a state-space model which had the advantage of simplicity and could be easily implemented and then applied the least square method to identify the battery model parameters. however, accurate state of charge (soc) estimation of a battery, which depends not only on the battery model but also on highly accurate and efficient algorithms, is considered one of the most vital and critical issue for the energy management and power distribution control of ev. in this paper three different estimation methods, i.e., extended kalman filter (ekf), particle filter (pf) and unscented kalman filter (ukf), are presented to estimate the soc of lifepo4 batteries for an electric vehicle. battery's experimental data, current and voltage, are analyzed to identify the thevenin equivalent model parameters. using different open circuit voltages the soc is estimated and compared with respect to the estimation accuracy and initialization error recovery. the experimental results showed that these online soc estimation methods in combination with different open circuit voltage-state of charge (ocv-soc) curves can effectively limit the error, thus guaranteeing the accuracy and robustness. |
| WOS关键词 | LITHIUM-ION BATTERIES ; MANAGEMENT-SYSTEMS ; OF-CHARGE ; POLYMER BATTERY ; KALMAN FILTER ; ROBUST STATE ; IMPEDANCE ; VOLTAGE ; CELLS |
| WOS研究方向 | Energy & Fuels |
| WOS类目 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000383547900057 |
| 出版者 | MDPI AG |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2375949 |
| 专题 | 中国科学院大学 |
| 通讯作者 | Li, Weimin |
| 作者单位 | 1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 2.Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Jining Inst Adv Technol, Jining 272000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zahid, Taimoor,Li, Weimin. A comparative study based on the least square parameter identification method for state of charge estimation of a lifepo4 battery pack using three model-based algorithms for electric vehicles[J]. Energies,2016,9(9):16. |
| APA | Zahid, Taimoor,&Li, Weimin.(2016).A comparative study based on the least square parameter identification method for state of charge estimation of a lifepo4 battery pack using three model-based algorithms for electric vehicles.Energies,9(9),16. |
| MLA | Zahid, Taimoor,et al."A comparative study based on the least square parameter identification method for state of charge estimation of a lifepo4 battery pack using three model-based algorithms for electric vehicles".Energies 9.9(2016):16. |
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来源：中国科学院大学
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