基于自适应神经网络的故障检测用于电池电池的热过程
IEEE transactions on cybernetics
|February 18, 2026
概括
本研究介绍了一种适应性神经网络框架,用于检测离子电池中的热故障. 该方法使用减少顺序建模和神经观察准确识别异常,增强电池安全性.
科学领域:
- 电池热管理 电池热管理
- 故障检测系统的故障检测系统
- 在工程领域的人工智能.
背景情况:
- 离子电池是关键的储能设备.
- 热失控带来了重大的安全风险.
- 准确的热量监测对于安全运行至关重要.
研究的目的:
- 开发一个基于自适应神经网络 (AdNN) 的故障检测框架.
- 为了应对未知的非线性热量产生和有限的传感器数据的挑战.
- 为了能够可靠地检测离子电池中的热异常.
主要方法:
- 一个两阶段的方法,结合了减少顺序的建模和自适应的神经观察.
- 用光谱近似技术创建一个可计算的可处理的小序模型.
- 一个自适应的神经观察器,从表面温度数据中估计电池状态和未知的动态.
- 一个混合故障检测方案,集成基于模型的剩余和数据驱动的值生成.
主要成果:
- 成功估计了电池状态和未知的非线性动态.
- 使用混合故障检测方案有效检测到热异常.
- 在袋式电池上的实验验证证证了该方法的可靠性.
结论:
- 拟议的基于AdNN的框架可靠地检测离子电池中的热异常.
- 减少顺序建模和自适应神经观测的结合是有效的.
- 这种方法提高了电池热管理系统的安全性和可靠性.
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