首页> 外文会议>International Conference on Electrical Drives and Power Electronics >Estimation of VRLA battery states and parameters using Sigma-point Kalman filter
【24h】

Estimation of VRLA battery states and parameters using Sigma-point Kalman filter

机译:使用Sigma-Point Kalman滤波器估计VRLA电池状态和参数

获取原文

摘要

This paper describes a hybrid electrical model of valve-regulated lead-acid battery (VRLA) and its application in Matlab/Simulink environment. Based on the charge/discharge test characteristics of the telecommunication battery stack, all parameters of the hybrid electrical models are derived. After implementing the charge/discharge simulations of hybrid electrical model and comparisons with actual tests of battery stack, joint estimation of model states and parameters is carried out using Sigma-point Kalman filter (SPKF). Results of performed joint estimation correspond to model simulations and it is shown that the SPKF algorithm is good for estimation of model states and parameters. After validation of the hybrid electrical model and validation of SPKF algorithm, joint estimation of battery states and parameters is performed to charge/discharge test of VRLA battery stack using Unscented Kalman Filter (UKF) method.
机译:本文介绍了阀调节铅酸蓄电池(VRLA)的混合电气模型及其在MATLAB / SIMULINK环境中的应用。基于电信电池堆的充电/放电测试特性,衍生混合动力电模型的所有参数。在实现混合电气模型的充电/放电模拟和使用电池堆的实际测试的比较之后,使用Sigma-Point Kalman滤波器(SPKF)进行模型状态和参数的联合估计。执行关节估计的结果对应于模型模拟,并显示SPKF算法良好的模型状态和参数估计。在验证混合电气模型和SPKF算法的验证之后,使用Unscented Kalman滤波器(UKF)方法对电池状态和参数的联合估计和参数进行充电/放电测试。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号