[1]邓青,刘成武.电池管理系统关键技术SOC估算的研究[J].福建工程学院学报,2017,15(06):519-522.[doi:10.3969/j.issn.1672-4348.2017.06.003]
 Deng Qing,Liu Chengwu.Estimation of SOC as the key technology in battery management system[J].Journal of FuJian University of Technology,2017,15(06):519-522.[doi:10.3969/j.issn.1672-4348.2017.06.003]
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电池管理系统关键技术SOC估算的研究()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第15卷
期数:
2017年06期
页码:
519-522
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
Estimation of SOC as the key technology in battery management system
作者:
邓青刘成武
福建工程学院机械与汽车工程学院
Author(s):
Deng Qing Liu Chengwu
School of Mechanical & Automotive Engineering, Fujian University of Technology
关键词:
荷电状态 安时积分法 修正
Keywords:
SOC(state of charge) ampere-hour integral method correction
分类号:
TM 911
DOI:
10.3969/j.issn.1672-4348.2017.06.003
文献标志码:
A
摘要:
在大量先验数据的基础上,根据电池管理系统的采样数据,结合安时积分法和开路电压法,对电池SOC进行估算,同时考虑温度及单体电池的不一致性对电池性能的影响,根据不同温度与电池SOC之间的修正数据关系对上述算法进行高低端修正,实验结果表明此方法能够降低安时积分法估计电池SOC的累积误差,准确估算电池SOC,且具有较强的实用性和可靠性。
Abstract:
On the basis of a large number of prior data, the battery SOC was estimated according to the sampling data of the battery management system, with the combination of the ampere-hour integral method and the open-circuit voltage method. Meanwhile, considering the effects of temperature and difference of single batteries on the performance of the battery, a high-and-low-end correction of the above algorithm was based on the correction data relationship between different temperatures and battery SOC. Experimental results show that the method could reduce the cumulative error of ampere-hour integral method in estimating the battery SOC, so it can accurately estimate the SOC of the battery. In addition, this method has strong applicability and reliability.

参考文献/References:

[1] 周翔,赵韩,江昊.基于EKF算法的磷酸铁锂电池在线SOC估算[J]. 合肥工业大学学报,2013,36(4):385-388.
[2] 李洪宇,张晓强,张卫平.大容量锂离子电池SOC估算原理及应用[J].电源技术,2015,39(5):1100-1102.
[3] Chen X K, Sun D. Modeling and state of charge estimation of lithium-ion battery[J].Advances in Manufacturing,2015,3(3):202-211.
[4] Weng C, Sun J, Peng H. A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring[J]. Journal of Power Sources,2014,258:228-237.
[5] Sun Y, Ma Z, Tang G, et al. Estimation method of state-of-charge for lithium-i-on battery used in hybrid electric vehicles based on variable structure extended kalman filter[J]. Chinese Journal of Mechanical Engineering,2016,29(4):717-726.
[6] Gregory L P. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs:Part 3. State and parameter estimation[J]. Journal of Power Sources,2004,134(2):277-292.
[7] Jayasinghe J, Nadishan K. Neural network based state of charge (SOC) estimation of electric vehicle batteries[J].University of Moratuwa,2014,4(12):1-4.
[8] 鲍慧,于洋.基于安时积分法的电池SOC估算误差校正[J].计算机仿真,2013,30(11):148-151.
[9] Liu X, Wu J, Zhang C. et al. A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures[J]. Journal of Power Sources,2014,270:151-157.
[10] 李哲,卢兰光,欧阳明高.提高安时积分法估算电池SOC精度的方法比较[J].清华大学学报,2010,50(8):1293-1296.
[11] 赵昂,成勇,杨晓军,等.一种电池 SOC 的检测方法及装置:106154176A[P].2016-11-23.

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更新日期/Last Update: 2018-01-18