• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Electrochemical Science >A Novel Prior Noise Correction - Adaptive Extended Kalman Filtering Method for the Full Parameter and State-of-energy co- estimation of the Lithium-ion Batteries
【24h】

A Novel Prior Noise Correction - Adaptive Extended Kalman Filtering Method for the Full Parameter and State-of-energy co- estimation of the Lithium-ion Batteries

机译：一种新的现有噪声校正自适应扩展卡尔曼滤波方法，用于全参数和锂离子电池的能量共同估算

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the battery management system, the state-of-energy is an important state to represent the remaining energy of the battery. The equivalent circuit model is the key to predicate this state of the lithium-ion battery. Therefore, the modeling and parameter identification of the battery model is crucial. This paper proposes a full parameter identification algorithm based on the forgetting factor recursive extended leastsquare algorithm, which is leveraged to calculate parameters including the open-circuit voltage of the equivalent circuit model. Besides, the prior noise correction adaptive extended Kalman filtering algorithm is derived to predict the state-of-energy with the proposed full parameters identification algorithm. The prior noise correction is an efficient method to reduce the estimation error of the extended Kalman filtering algorithm, which predicts the noise at the next moment by current noise. Comparing with the extended Kalman filtering algorithm, the noise of prior noise correction adaptive extended Kalman filtering algorithm can be corrected efficiently. In this way, the maximum error of the forgetting factor recursive extended least-square algorithm to estimate open-circuit-voltage is 0.41% under different complex working conditions comparing with actual values. The modeling accuracy by full parameters identification is higher than 99.31%. For verification of state-of-energy, two different complexes working conditions are conducted to calculated state-of-energy, the error of state-of-energy estimation is less than 1.49%. The results demonstrate that the proposed algorithm can perfect the state estimation.
机译：在电池管理系统中，能量状态是表示电池剩余能量的重要状态。等效电路模型是谓式锂离子电池状态的键。因此，电池模型的建模和参数识别至关重要。本文提出了一种基于遗忘因子递归延期算法的全参数识别算法，其利用来计算包括等效电路模型的开路电压的参数。此外，导出了现有噪声校正自适应扩展卡尔曼滤波算法以预测所提出的完整参数识别算法的能量状态。现有噪声校正是减少扩展卡尔曼滤波算法的估计误差的有效方法，其通过当前噪声预测下一刻的噪声。与扩展卡尔曼滤波算法进行比较，可以有效地校正先前噪声校正自适应扩展卡尔曼滤波算法的噪声。以这种方式，在与实际值相比，在不同的复杂工作条件下，遗忘因子递归延长最小二乘算法的最大误差为估计开路电压为0.41％。通过完整参数识别的建模精度高于99.31％。为了验证能量状态，进行两种不同的配合物工作条件以计算能量状态，能源状态估计的误差小于1.49％。结果表明，所提出的算法可以完善状态估计。

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