• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2016, Vol. 37 ›› Issue (9): 50-.doi: 10.3969/j.issn.1000-7229.2016.09.007

• 储能技术 • 上一篇    下一篇

基于改进无迹卡尔曼滤波法的大容量电池储能系统SOC预测

赵泽昆1,张喜林2,张斌3,韩晓娟1   

  1. 1.华北电力大学控制与计算机工程学院,北京市102206;2.国网吉林省电力有限公司 长春供电公司,长春市 130000;3.国网冀北三河市供电有限公司,河北省三河市065200
  • 出版日期:2016-09-01
  • 作者简介:赵泽昆(1992),男,硕士研究生,主要从事新能源发电控制技术、储能技术、故障诊断等方面的研究工作; 张喜林(1957),男,硕士,正高级工程师,主要从事电力系统自动化等方面的研究工作; 张斌(1989),男,硕士,主要从事新能源发电控制技术和储能技术等方面的研究工作; 韩晓娟(1970),女,博士,教授,主要从事新能源发电控制技术、故障诊断、信息融合和检测技术等方面的研究工作。
  • 基金资助:
    国家自然科学基金项目(51577065);国家电网公司科技项目(KY-SG-2016-204-JLDKY)

State-of-Charge Estimation of Large Scale Battery Energy Storage System Based on Improved Unscented Kalman Filter

ZHAO Zekun1, ZHANG Xilin2, ZHANG Bin3, HAN Xiaojuan1   

  1. 1 School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; 2. Changchun Power Supply Company, State Grid Jilin Electric Power Company, Changchun 130000, China; 3. Sanhe Electric Power Supply Company, State Grid Jibei Electric Power Company, Sanhe 065200, Hebei Province, China
  • Online:2016-09-01
  • Supported by:
    Project supported by National Natural Science Foundation of China (51577065)

摘要: 大容量电池储能系统的荷电状态(state of charge,SOC)是电池管理系统(battery management system,BMS)的重要参数,必须准确预测,由于电池单体存在较强的差异性,传统的SOC预测技术很难达到准确预测的效果。针对上述问题,提出基于改进无迹卡尔曼滤波法(unscented Kalman filter, UKF)的大容量电池储能系统SOC预测方法,利用遗传算法(genetic algorithm, GA)优化无迹卡尔曼滤波的滤波参数,进一步提高SOC的预测精度。在设定工况下对串联型电池储能系统进行仿真实验,仿真结果表明该文提出的改进无迹卡尔曼滤波方法可以获得有效可靠的SOC预测结果,具有良好的工程应用前景。

关键词: 遗传算法, 无迹卡尔曼滤波(UKF), 荷电状态预测, 等效电路

Abstract: Accurate and reliable state of charge (SOC) estimation for large capacity battery energy storage system (LCBESS) is necessary for the battery management system (BMS). Since the difference between batteries, it is difficult to obtain ideal prediction results by traditional methods. To solve the above problem, this paper proposes the prediction method of SOC for LCBESS based on improved unscented Kalman filter (UKF), which adopts the genetic algorithm (GA) method to optimize the filter coefficients of UKF, in order to further improve the prediction accuracy of SOC. This paper simulates the series battery energy storage system under setting conditions. The simulation results show that the proposed improved unscented Kalman filter method can obtain the effective and reliable prediction results of SOC, which has a broad prospect of engineering application.

Key words: genetic algorithm, unscented Kalman filter (UKF), prediction of state of charge, equivalent circuit

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