LG 18650HG2 Li-ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script

The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer ([e-mail]). If this data is utilized for any purpose, it should be appropriately referenced.
-A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script.

-Instructions for Downloading and Running the Script:
1-Select download all files from the Mendeley Data page (https://data.mendeley.com/datasets/cp3473x7xv/2).
2-The files will be downloaded as a zip file. Unzip the file to a folder, do not modify the folder structure.
3-Navigate to the folder with "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx"
4-Open and run "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx"
5-The matlab script should run without any modification, if there is an issue it's likely due to the testing and training data not being in the expected place.
6-The script is set by default to train for 50 epochs and to repeat the training 3 times. This should take 5-10 minutes to execute.
7-To recreate the results in the paper, set number of epochs to 5500 and number of repetitions to 10.

-The test data, or similar data, has been used for some publications, including:
[1] C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020
[2] C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.

本数据集的测试工作由加拿大安大略省汉密尔顿市麦克马斯特大学的菲利普·科勒梅耶博士（[e-mail]）负责执行。若本数据集被用于任何研究目的，必须进行适当的引用。
- 在本次测试中，对一款全新的3Ah LG HG2电池进行了测试，测试环境为一座8立方英尺的热室，并使用了一款75安培、5伏特的Digatron通用电池测试仪通道，其电压和电流的准确度为满量程的0.1%。这些数据被用于采用深度前馈神经网络（FNN）方法设计的SOC估算器的设计过程中。数据集还包括了数据采集、数据准备以及FNN示例脚本的开发描述。
- 以下是下载和运行脚本的操作指南：
1. 从Mendeley数据页面（https://data.mendeley.com/datasets/cp3473x7xv/2）选择下载所有文件。
2. 文件将以zip文件形式下载。解压文件至一个文件夹中，请勿修改文件夹结构。
3. 导航至包含“FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx”的文件夹。
4. 打开并运行“FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx”。
5. Matlab脚本应在未经修改的情况下运行无误，若出现问题，很可能是因为测试和训练数据未放置在预期位置。
6. 默认情况下，脚本设置为训练50个周期，并重复训练3次。该过程预计需要5至10分钟。
7. 若要重现论文中的结果，请将周期数设置为5500，重复次数设置为10。
- 测试数据或类似数据已被用于一些出版物中，包括：
[1] C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “基于深度神经网络的鲁棒xEV电池SOC估算器设计”，在WCX SAE世界大会体验中，底特律，密歇根州，2020年4月。
[2] C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, “使用长短期记忆递归神经网络与迁移学习进行锂离子电池SOC估计”，2019年IEEE交通电气化大会和展览（ITEC），底特律，密歇根州，美国，2019年，第1-6页。