基于电阻层析成像的汽车动力电池内部温度监测

Monitoring of internal temperature of vehicle power battery based on electrical resistance tomography

为了直观地在线检测动力电池的温度,以确保动力电池的安全,提出了一种基于电阻层析成像的动力电池内部温度监测新方法。引入温纳-施伦贝尔装置模型分析电池内部温度检测机理,推导了电池内部温度与电阻率的数学关系;应用抑制平滑度最小平方法将多电极检测大数据进行图像反演重建,实现了对动力电池内部温度异变区的动态监测,并搭建了实验平台。文中通过建立相交区域试验模型,实验分析动力电池内部的电阻率静态分布,并验证方法可靠性。然后,实验分析动力电池内部电阻率的动态分布,得到45.5℃时反演图像色带差值增大了2.5倍,且坐标(0.047,0.000 5)内色带变化明显。最后,动态跟踪异变区临界温度。结果显示,在0.173Ω·m处突变骤升,电池内部产生异变。得到的结果表明,提出的方法可直观可靠地监测动力电池内部温度动态变化;可为动力电池的安全特性、寿命预测、负载控制等提供新的研究途径。

A novel method to monitoring the internal temperature of a power battery was proposed based on the Electrical Resistance Tomography（ERT） to complement the temperature measurement in online and to insure the safety of the power battery. Through introducing the Wenner-Schlumberger model in the ERT, the relationships between the internal temperatures and the resistivity of power battery were deduced. And by using the smoothness-constrained least-square method to invert the images of multi-electrode tomographic detection data, the internal abnormal temperature regions were monitored dynamically and a test platform was established. To validate the reliability of this new method, an intersection region model was established to analyze the static distribution of internal re-sistivity of the power battery. Then, dynamic resistivity distribution was analyzed in five different temperatures. The analysis shows that the color ribbon difference in the inversion image increases by 2.5 times in 45. 5 ^（2 as compared to those of other four tests, especially at coordinates （0. 047, 0. 000 5）, which changes significantly. Finally, dynamic tracking critical temperature in mutational area shows that the resistivity rebounds suddenly at 0. 173 Ω·m, which indicates that a mutation occurs in the battery. These results demonstrate that the proposed method not only offers a visual and reliable monitoring result of internal temperature changes for the power battery, but also can provide a new research pathways for the power battery, such as safety characteristics, life prediction, and load control.