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基于射频同轴探针的电解质度的神经网络预测方法的研究

Jiangbo Qian^1,2, Zhiwei Peng¹, Shilong Liu¹

¹Department of Power Engineering, North China Electric Power University, Baoding 071003, People's Republic of China.

The Review of scientific instruments

|December 8, 2025

概括

这项研究引入了一种新的电解质度预测方法,使用射频同轴探头和莱文伯格-马奎特逆向传播神经网络. 该技术为实时电解质度测量提供了高度准确和可靠的解决方案.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
传感器技术传感器技术

背景情况:

传统的电解质度测量方法存在高成本,低准确性和对电池质量的潜在损害.
需要精确,非破坏性和具有成本效益的方法来实时监测电解质度.

研究的目的:

开发和验证一种新的电解质度预测方法.
为了解决现有的电解质测量技术的局限性.
为了实时监测电解质度.

主要方法:

使用射频同轴探头构建神经网络预测系统.
执行神经网络算法Levenberg-Marquardt反向传播 (L-MBP) 的执行.
对贝叶斯规范化和定量联梯度优化的反向传播 (BP) 神经网络进行比较分析.
实验验证使用三甲硫酸 [Zn(CF3SO3) 2] 电解质.

主要成果:

拟议的射频同轴探头和L-MBP神经网络方法与其他BP网络优化方法相比,显示出更高的预测准确性.
对三甲硫酸盐电解质度的实验测量显示,最大相对误差为4.42%,最大绝对误差为4.94%.
该方法在实时电解质度测量方面被证明是有效的.

结论:

射频同轴探头与L-MBP神经网络相结合,为测量基于水的电解质度提供了高度准确和可靠的方法.
这种预测方法适用于实时应用,克服了传统技术的缺点.
该研究验证了用于电解质分析的拟议传感器系统的可行性和有效性.

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