基于热成像的锂离子电池智能故障定位技术

From Time-series to Vision:Lithium-ion Battery Intelligent Perception(LBIP)for Thermal Fault Location

随着新能源发电、电动汽车等的发展,目前对储能技术的要求不断提高。其中,锂离子电池因其环境友好、能量密度高、使用寿命长等优点被广泛应用于各类储能系统中。为锂离子电池配备合理的热故障诊断可以避免热失控现象的发生,确保电池安全可靠运行。该研究提出了锂离子电池智能感知(lithium-ionbatteryintelligent perception,LBIP)来建立锂离子电池的热故障诊断模型。LBIP包括特征提取网络、区域提交网络(regionproposal network,RPN)、感兴趣区域对齐(region of interest align,ROIAlign)以及Mask分支。选择Ansys Fluent软件进行锂离子电池的有限元仿真。LBIP处理电池表面的热成像图像,对问题电池进行识别,并进行问题电池的定位与分割。结果表明,故障电池的识别准确率可达95%。

With the development of new energy power generation,electric vehicles,etc.,the requirements for energy storage are constantly increasing.Lithium-ion batteries are widely used in various energy storage systems due to their advantages of environmental friendliness,high energy density,and long lifespan.Providing reasonable thermal fault diagnosis for lithium-ion batteries can avoid thermal runaway and ensure safe and reliable operation of the batteries.This study proposes lithium-ion battery intelligent perception(LBIP)to build a thermal fault diagnosis model for lithium-ion batteries.LBIP includes the backbone for feature extraction,region proposal network(RPN)for proposals generation,and fine-grained localization.The Ansys Fluent software is selected for finite element simulation of lithium-ion batteries.The model processes the thermal imaging image of the battery surface,identifies the problematic battery,and localizes the problematic battery.Results shows that the recognition accuracy of the faulty battery can reach 95%.