• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Energy Storage >Model-based thermal anomaly detection for lithium-ion batteries using multiple-model residual generation
【24h】

Model-based thermal anomaly detection for lithium-ion batteries using multiple-model residual generation

机译：使用多模型残留生成的基于模型的热异常检测锂离子电池

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The continuously increasing energy and power density of lithium-ion batteries will aggravate the safety and reliability concerns of advanced battery management systems (BMSs). To ensure the safety and reliability of lithium-ion batteries, the BMS must implement anomaly detection algorithms that are capable of capturing abnormal behaviors. Thermal anomalies are one of the most critical anomalies that can be potentially catastrophic. Motivated by this, a model-based strategy of anomaly detection of thermal parameters for lithium ion-batteries is presented in this paper. The algorithm is based on a multiple-model adaptive estimation framework. Firstly, an equivalent-circuit-model-based electrothermal model is proposed to describe battery dynamic behaviors. Then, a combination of the recursive-least-square method and Kalman-filter is employed to generate residual signals for thermal anomaly detection. Furthermore, the probability of the signature anomaly is evaluated through the multiple-model adaptive estimation technique. Distinguished from existing threshold-based methods, the proposed method can determine particular anomalies according to the value of the generated conditional probability, without a manually determined threshold. Simulations are developed to simulate different faults and generate data for algorithm validation. The results show signature thermal anomaly can be detected accurately.
机译：锂离子电池的不断增加的能量和功率密度将加剧先进电池管理系统（BMS）的安全性和可靠性问题。为确保锂离子电池的安全性和可靠性，BMS必须实现能够捕获异常行为的异常检测算法。热异常是可能灾难性的最关键的异常之一。由此，本文提出了一种基于模型的异常检测的异常检测锂离子电池的热参数。该算法基于多模型自适应估计框架。首先，提出了一种基于等效电路模型的电热模型来描述电池动态行为。然后，采用递归 - 最小二乘法和卡尔曼滤光器的组合来产生热异常检测的残余信号。此外，通过多模型自适应估计技术评估签名异常的概率。区分基于阈值的方法，所提出的方法可以根据产生的条件概率的值确定特定的异常，而没有手动确定的阈值。开发模拟以模拟不同的故障并生成算法验证数据。结果显示可准确检测签名热异常。

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