首页> 外文会议>WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems >Local Model Network based Dynamic Battery Cell Model Identification
【24h】

Local Model Network based Dynamic Battery Cell Model Identification

机译:基于本地模型网络的动态电池单元模型识别

获取原文

摘要

In this paper the local model network (LMN) based dynamic battery cell model identification is presented. Such a model describes the nonlinear dynamic behaviour of the cell terminal voltage in dependance of the charge/discharge current and can be used for the state of charge (SoC) estimation in hybrid electrical vehicles. For that purpose, the model must be accurate at high C-rates in combination with a highly dynamic excitation. The LMN construction, related SoC observer structures and the appropriate experiment design are discussed in the present paper. The proposed concepts and the performance of the LMN is validated by means of real measurement data from a Lithium Ion power cell.
机译:本文介绍了本地模型网络(LMN)的动态电池单元模型识别。这种模型描述了依赖于充电/放电电流的单元端电压的非线性动态行为,并且可以用于混合动力电动车辆中的电荷状态(SOC)估计。为此目的,该模型必须以高C率准确,与高动态励磁组合。本文讨论了LMN结构,相关SOC观察者结构和适当的实验设计。所提出的概念和LMN的性能通过来自锂离子电池的真实测量数据验证。

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