首页> 外文期刊>Journal of ambient intelligence and humanized computing >An approach to predicpt discharge voltage of lithium-ion batteries under dynamic loading conditions
【24h】

An approach to predicpt discharge voltage of lithium-ion batteries under dynamic loading conditions

机译:一种动态负载条件下锂离子电池放电电压的预测方法

获取原文
获取原文并翻译 | 示例
           

摘要

Terminal voltage is an important indicator to alarm end-of-discharge of lithium-ion batteries. Therefore, predicting the terminal voltage is helpful in preventing issues that caused by running out of power. However, the loading condition of battery is usually dynamic in real practice which greatly increases the difficulty of prediction. In this paper, we propose a novel approach to predict the terminal voltage under dynamic loading condition. This approach transforms the problem of predicting the terminal voltage into the problem of predicting the state-of-charge (SOC) of battery, using equivalent circuit model and a polynomial function. In the prediction of SOC, an accurate value of capacity is required, but it is not practical to be measured in each discharge process. Therefore, we develop an adaptive capacity method based on feature extraction in charging profile and k-nearest neighbor algorithm to timely update batterys SOC after each charge process. The whole prediction approach is tested on an open dataset, and comparison experiments demonstrate that it outperforms traditional approaches.
机译:端子电压是报警锂离子电池放电终止的重要指示器。因此,预测端子电压有助于防止因电量耗尽而引起的问题。然而,在实际应用中电池的负载情况通常是动态的,这大大增加了预测的难度。在本文中,我们提出了一种新颖的方法来预测动态负载条件下的端电压。这种方法使用等效电路模型和多项式函数将预测端子电压的问题转换为预测电池充电状态(SOC)的问题。在SOC的预测中,需要准确的容量值,但是在每个放电过程中进行测量是不实际的。因此,我们基于充电曲线中的特征提取和k最近邻算法开发了一种自适应容量方法,以便在每次充电后及时更新电池SOC。整个预测方法在一个开放的数据集上进行了测试,比较实验表明它优于传统方法。

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