上海电力大学自动化工程学院
纸质出版:2025
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何云, 盛文娟. 结合光纤光栅传感器与特征筛选的锂电池荷电状态估计[J]. 电器与能效管理技术, 2025,(10):72-83.
何云, 盛文娟. 结合光纤光栅传感器与特征筛选的锂电池荷电状态估计[J]. 2025, (10): 72-83.
何云, 盛文娟. 结合光纤光栅传感器与特征筛选的锂电池荷电状态估计[J]. 电器与能效管理技术, 2025,(10):72-83. DOI: 10.16628/j.cnki.2095-8188.2025.10.010.
何云, 盛文娟. 结合光纤光栅传感器与特征筛选的锂电池荷电状态估计[J]. 2025, (10): 72-83. DOI: 10.16628/j.cnki.2095-8188.2025.10.010.
光纤布拉格光栅(FBG)传感器正逐步成为提升锂电池荷电状态(SOC)估计精度的创新解决方案,而锂电池表面应变的位置和数量均会影响SOC的估计精度,提出一种基于序列前向选择(SFS)算法的多应变特征筛选方法。针对SFS算法在特征选择过程中存在的模型泛化能力波动问题,引入弹性网络(Elastic Net)与留一交叉验证(LOOCV)机制以提升特征筛选稳定性与模型的泛化性能,再利用遗传算法优化反向传播(GA-BP)神经网络对锂电池SOC进行估计。实验结果表明,经特征筛选后,锂电池SOC估计的均方根误差(RMSE)降低至0.635%
平均绝对误差(MAE)为0.419%
拟合优度(R
2
)达0.999。所提方法为多位置FBG应变信号的特征筛选问题提出了解决思路,对基于光纤传感器的锂电池SOC高精度估计提供了技术参考。
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0000 0001 2341 2786, grid.116068.8, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000 0001 2341 2786, grid.116068.8, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,Toyota Research Institute, Los Altos, CA, USA,Toyota Research Institute, Los Altos, CA, USA,0000 0001 2341 2786, grid.116068.8, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA,0000 0001 2341 2786, grid.116068.8, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000 0001 2231 4551, grid.184769.5, Materials Science Division, Lawrence Berkeley National Lab, Berkeley, CA, USA,0000000419368956, grid.168010.e, Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA,0000 0001 2341 2786, grid.116068.8, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.Data-driven prediction of battery cycle life before capacity degradation[J].Nature Energy,2019.
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