一种混合机器学习模型,用于使用平板电容传感器动态水平检测酸电池电解质
Shuai Huang1, Weikang Zhang2, Weiwei Zhang3
1School of Energy and Environment, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种混合深度学习模型,Poly-LSTM,用于准确测量酸电池中的电解质水平. 它克服了残留液体薄膜的错误,提高了非破坏性测试的准确性.
科学领域:
- 电池技术 电池技术
- 非破坏性测试 不破坏性测试
- 机器学习 机器学习
背景情况:
- 酸电池的故障可能源于异常的电解质水平.
- 容量化方法提供非侵入性的电解质水平检测,但由于在水平快速下降时,管壁上的残留液体薄膜导致动态错误.
研究的目的:
- 开发一种用于准确测量酸电池中的电解质水平的新方法.
- 为了减轻在电容传感中由残留液体薄膜粘附引起的动态测量误差.
主要方法:
- 开发了一种混合深度学习模型,Poly-LSTM,将多项式特征生成与长短期内存 (LSTM) 网络相结合.
- 多项式特征被生成以捕捉非线性传感器输入效应.
- LSTM处理了这些特征,以建模时间依赖性,以准确地预测液体水平.
主要成果:
- 与其他模型相比,Poly-LSTM模型显示出高于其他模型的液体水平估计准确度.
- 在0.12mm/s的快速下降速度下,该模型实现了0.5319mm的平均绝对误差 (MAE).
- 根平均平方误差 (RMSE) 为0.7180毫米,平均绝对百分比误差 (MAPE) 为0.1320%.
结论:
- 拟议的Poly-LSTM模型有效地消除了电容液位检测中的动态测量错误.
- 这种混合深度学习方法提高了酸电池电解质水平的非破坏性测试的可靠性.
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