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| 锂电池SOC参数识别、优化及监测研究 |
| Study on SOC Parameter Identification Optimization and Monitoring of Lithium Battery |
| 投稿时间：2017-10-16 |
| DOI：10.16018/j.cnki.cn32-1650/n.201801003 |
| 中文关键词: SOC 递推最小二乘法 扩展卡尔曼滤波算法 MATLAB/Simulink |
| 英文关键词: SOC recursive least squares method extended Kalman filter algorithm MATLAB/Simulink |
| 基金项目:江苏省科技厅科技支撑计划项目(BY2015057-07) |
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| 中文摘要: |
| 为了延长微网系统中储能装置的使用寿命，保证微网系统的稳定可靠运行，需对锂电池荷电状态(SOC)进行实时准确的监测。提出一种基于递推最小二乘法(RLS)和扩展卡尔曼滤波算法(EKF)的电池SOC参数识别优化及检测方法，并在MATLAB/Simulink仿真环境下进行可行性验证。结果表明，该方法能够在极小误差范围内实现对锂电池SOC的实时监测。 |
| 英文摘要: |
| With the increasing energy crisis and environmental pollution, microgrid systems based on distributed power generation are widely used. As an important part of the microgrid system, the hybrid energy storage device usually plays an important role in recovering the energy of the system and stabilizing the fluctuation of the load power. Lithium battery plays a very important role as the main form of energy storage device. However, in order to prolong the service life of the energy storage device and ensure the stable and reliable operation of the micro-grid system, it is necessary to accurately monitor the state of charge(SOC)of the lithium battery in real time. In this paper, an optimization and detection method for SOC parameter identification based on recursive least squares(RLS)and extended Calman filter algorithm(EKF)is proposed. And the feasibility of the method is verified in the MATLAB/Simulink simulation environment. The results show that this method can realize real-time monitoring of lithium battery SOC within a very small error range. |
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