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.

中文翻译：

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使用深度前向神经网络估计锂离子电池的充电状态

本文介绍了两个分别具有两个和四个隐藏层的深度前向网络，它们使用 K 折交叉验证方法对给定温度下 Panasonic 18650PF 锂离子 (Li-ion) 电池的驱动循环进行建模，以便估计电池的荷电状态 (SOC)。驱动循环功率曲线是针对带有 35kWh 电池组的电动卡车计算的，该电池组按单个 18650PF 电池的比例进行调整。我们提出了一种机器学习工作流程，它能够在开发用于 SOC 估计的深度学习模型时对抗过度拟合。这项工作的贡献是提出了一种为锂离子电池构建深度前向网络及其性能评估的方法，该方法遵循机器学习的最佳实践。