在线离子电池智能感知用于热故障检测和定位
Luyu Tian1, Chaoyu Dong2, Yunfei Mu1
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Heliyon
|February 19, 2024
概括
这项研究引入了针对离子电池热故障诊断的优化深度学习模型. 改进的模型使用热成像准确地识别和定位电池问题,提高安全性和可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 离子电池对于现代技术至关重要,但由于热失控,会带来安全风险.
- 有效的热故障诊断对于确保电池安全和运行可靠性至关重要.
研究的目的:
- 开发和验证一个优化的深度学习模型,用于精确的离子电池热故障诊断.
- 提高电池系统中热异常的识别和定位.
主要方法:
- 开发了一个优化的Mask R-CNN模型 (LBIP-V2),根据电池数据集改进参数和结构.
- 该模型处理热图像以提取特征,分类故障,并使用骨干网络,RPN和Mask分支 pinpoint故障区域.
- 在单细胞和各种电池组配置上评估了性能.
主要成果:
- 在不同的电池数据集中,LBIP模型实现了超过95%的识别准确度.
- 实时故障诊断模拟以超过98%的信心证明了LBIP的有效性.
- 与LBIP-V1.1相比,优化的LBIP-V2模型显示性能有所改善.
结论:
- 开发的LBIP模型为离子电池的实时热故障诊断提供了强大的解决方案.
- 优化的深度学习方法通过防止热失控,显著提高了电池的安全性.
- LBIP提供了一种可靠的方法来识别和定位不同电池配置中的故障.
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