Panasonic 18650PF Li-ion Battery Data and Example FNN and LSTM Neural Network SOC Estimator Training Script
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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 %*****************************************************************************************% -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). %*****************************************************************************************% 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. %*****************************************************************************************% 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.
本附带示例脚本由加拿大安大略省汉密尔顿市麦克马斯特大学（McMaster University）的卡洛斯·维达尔（Carlos Vidal）博士与菲利普·科勒迈尔（Phillip Kollmeyer）博士开发。若将本脚本及附带数据集用于任何用途，需同时引用下述论文与本Mendeley数据集：Carlos Vidal、Pawel Malysz、Mina Naguib、Ali Emadi、Phillip J. Kollmeyer，《基于循环与非循环神经网络的电池荷电状态（State of Charge，SOC）估算》，《储能期刊（Journal of Energy Storage）》，2021年（完整引用信息请见https://www.sciencedirect.com/）。 本示例脚本用于训练两类不同的机器学习荷电状态估算算法：一类为带滤波输入的前馈神经网络（feedforward neural network），另一类为长短期记忆（LSTM）循环神经网络。上述两类算法的详细说明见前述引用论文。 本脚本针对松下18650PF（Panasonic 18650PF）电池归一化数据集训练荷电状态估算器，该数据集可通过以下链接获取：https://data.mendeley.com/datasets/wykht8y7tg/1 %*****************************************************************************************% - 脚本下载与运行说明： 1. 从Mendeley数据页面选择下载全部文件 2. 文件将以压缩包形式下载，将其解压至文件夹中，请勿修改文件夹结构 3. 打开并运行「LSTMvsFNN_Script_Nov_2021_V2.mlx」 4. 脚本内的注释与文本已包含进一步操作说明（若要获得更佳使用体验，请使用扩展名为*.mlx的Matlab实时脚本文件） %*****************************************************************************************% %*****************************************************************************************% 重要提示：关于本脚本的补充说明 如需修改神经网络类型，请跳转至第101行并遵循下述操作步骤。选择神经网络类型（第101行）： - 若需选择长短期记忆网络（LSTM），请在下拉菜单中选择「1」 - 若需选择前馈神经网络（FNN），请在下拉菜单中选择「2」 同时请跳转至第237行，再次选择相同的神经网络类型。 %*****************************************************************************************% %*****************************************************************************************% 附带文件说明： 1. 所有数据均已完成归一化处理，如需查看数据反归一化的示例，请参阅「Normalization」文件夹 2. 数据集包含-20℃、-10℃、0℃、10℃与25℃下的实验数据（论文中未使用-20℃数据集） 3. 「X」数据包含7个维度，各维度依次为：{电压V、电流I、温度T、0.5mHz滤波电压V_0.5mHz、0.5mHz滤波电流I_0.5mHz、5mHz滤波电压V_5mHz、5mHz滤波电流I_5mHz}，其中带_0.5mHz与_5mHz后缀的数据均经过一阶低通巴特沃斯（Butterworth）滤波处理 4. 「Y」数据为通过库仑计数法计算得到的电池荷电状态（SOC） 5. 数据集已划分为训练集、测试集与验证集，并分别保存至对应文件夹（数据划分的详细规则请参阅《储能期刊》刊发的论文）。其中Mix 1至4与US06用于训练，LA92 -10℃数据用于验证，LA92、NN与UDDS用于测试 6. 训练数据被拆分为10个独立文件，最多支持设置10个小批量（mini batch）训练
