Panasonic 18650PF Li-ion Battery Data and Example FNN and LSTM Neural Network SOC Estimator Training Script

The included example script was created by Dr. Carlos Vidal and Dr. Phillip Kollmeyer at McMaster University in Hamilton, Ontario, Canada. If this script and the included dataset is utilized for any purpose, the following paper should be referenced along with this Mendeley dataset:

Carlos Vidal, Pawel Malysz, Mina Naguib, Ali Emadi, Phillip J. Kollmeyer, “Estimating battery state of charge using recurrent and non-recurrent neural networks,” Journal of Energy Storage, 2021 (see https://www.sciencedirect.com/ for complete citation information).

The example script is configured to train two different types of machine learning state of charge estimation algorithms - a feedforward neural network with filtered input values and a long short term memory (LSTM) recurrent neural network. These algorithms are described in detail in the above reference. The script trains the SOC estimator for normalized data for a Panasonic 18650PF battery dataset which can be found here: https://data.mendeley.com/datasets/wykht8y7tg/1

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-Instructions for Downloading and Running the Script:
1-Select download all files from the Mendeley Data page
2-The files will be downloaded as a zip file. Unzip the file to a folder, do not modify the folder structure.
3-Open and run "LSTMvsFNN_Script_Nov_2021_V2.mlx"
4-Further instructions are included in the comments and text in the script (for better experience use the "Matlab Live Code File" with extension *.mlx).

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Important - Additional notes regarding the script:
When changing the type of neural network go to line 101 and follow the instructions below.

To select Neural Network Type (line 101):

Select "1" on the drop down for LSTM
Select "2" on the drop down for FNN

Also go to line 237 and make the same selection of Neural Network Type.

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Description of included files:

-All data is normalized, see "Normalization" folder for example showing how to denormalize data

-Data for -20, -10, 0, 10, and 25degC is included (in the paper, the -20degC data is not used)

-The "X" data has 7 rows, where the data in each row is as follows: { V, I, T, V_0.5mHz, I_0.5mHz, V_5mHz, I_5mHz}, where V is voltage, I is current, T is temperature, and the _0.5mHz and _5mHz data is filtered with a 1st order low pass Butterworth filter

-The "Y" data is state of charge calculated via coulomb counting

-The data is split into Train, Test, and Validation and is saved in the respective folders (see the Journal of Energy Storage paper for a description of how the data is split). Mix 1 to 4 and US06 are used for training, LA92 -10degC for validation, and LA92, NN, and UDDS for testing

-The training data is split into ten separate files, this allows up to 10 mini batches.

所附示例脚本由加拿大安大略省汉密尔顿市的麦克马斯特大学的卡洛斯·维达尔博士和菲利普·科尔梅耶博士编制。若此脚本及所附数据集被用于任何目的，应引用以下论文，同时结合本Mendeley数据集：

卡洛斯·维达尔，帕维尔·马利什，米娜·纳吉布，阿里·埃马迪，菲利普·J·科尔梅耶，“利用循环和非循环神经网络估计电池荷电状态，”《能源存储杂志》，2021年（完整引用信息请参见https://www.sciencedirect.com/）。

该示例脚本配置了两种不同类型的机器学习荷电状态估计算法的训练——一种具有滤波输入值的前馈神经网络和一种长短期记忆（LSTM）循环神经网络。这些算法在上述参考文献中有详细描述。脚本训练了用于标准化数据的松下18650PF电池数据集的SOC估计器，该数据集可在以下链接找到：https://data.mendeley.com/datasets/wykht8y7tg/1

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-脚本下载和运行说明：
1-从Mendeley数据页面选择下载所有文件
2-文件将作为zip文件下载。解压文件到一个文件夹中，切勿修改文件夹结构。
3-打开并运行“LSTMvsFNN_Script_Nov_2021_V2.mlx”
4-脚本中的注释和文本包含进一步说明（为获得更好的体验，请使用带有*.mlx扩展名的“Matlab Live Code File”）。

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重要 - 关于脚本的补充说明：
当更改神经网络类型时，请前往第101行并遵循以下说明。

选择神经网络类型（第101行）：

选择下拉菜单中的“1”以选择LSTM
选择下拉菜单中的“2”以选择FNN

同时，请前往第237行并做出相同的神经网络类型选择。

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所附文件描述：

-所有数据均已标准化，参见“标准化”文件夹中的示例，展示如何反标准化数据

-包含-20，-10，0，10和25摄氏度的数据（在论文中，-20摄氏度的数据未使用）

-X数据有7行，其中每行的数据如下：{ V, I, T, V_0.5mHz, I_0.5mHz, V_5mHz, I_5mHz}，其中V为电压，I为电流，T为温度，_0.5mHz和_5mHz数据经过一阶低通巴特沃斯滤波器滤波

-Y数据是通过库仑计数计算出的荷电状态

数据分为训练、测试和验证集，并分别保存在相应的文件夹中（请参阅《能源存储杂志》中关于数据分割的描述）。Mix 1至4和US06用于训练，LA92 -10摄氏度用于验证，LA92，NN和UDDS用于测试

训练数据分为十个单独的文件，这允许最多10个迷你批次。

搜集汇总

背景与挑战

背景概述

该数据集包含Panasonic 18650PF锂离子电池的标准化数据，用于训练FNN和LSTM神经网络以估计电池的充电状态（SOC）。数据涵盖多种温度条件，并提供了详细的脚本和文件结构说明，适合用于机器学习研究和电池状态估计。