论文标题
使用深色前向神经网络对锂离子电池的最先进估计
State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks
论文作者
论文摘要
本文分别使用K折交叉验证方法在给定的温度下，分别使用两个Panasonic 18650pf锂离子（Li-Ion）电池的Panasonic 18650pf锂离子（Li-Ion）电池的驱动周期进行建模，以估算细胞的电荷状态（SOC）。计算驱动周期电源轮廓，该电动卡车具有35kWh电池组，该电池组的缩放为一个18650pf单元。我们提出了一个机器学习工作流程，在开发深度学习模型以进行SOC估计时，它可以与过度拟合。这项工作的贡献是提出一种为锂离子电池构建深度向前网络及其性能评估的方法，该网络遵循机器学习的最佳实践。
This article presents two Deep Forward Networks with two and four hidden layers, respectively, that model the drive cycle of a Panasonic 18650PF lithium-ion (Li-ion) battery at a given temperature using the K-fold cross-validation method, in order to estimate the State of Charge (SOC) of the cell. The drive cycle power profile is calculated for an electric truck with a 35kWh battery pack scaled for a single 18650PF cell. We propose a machine learning workflow which is able to fight overfitting when developing deep learning models for SOC estimation. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment, which follows the best practices in machine learning.
