Panasonic 18650PF Li-ion Battery Data

The included tests were performed at the University of Wisconsin-Madison by Dr. Phillip Kollmeyer ([e-mail]). If this data is utilized for any purpose, it should be appropriately referenced. The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. The test data, or similar data, has been used for numerous publications, including: E. Chemali, P. J. Kollmeyer, M. Preindl, R. Ahmed and A. Emadi, "Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries," in IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6730-6739, Aug. 2018. doi: 10.1109/TIE.2017.2787586 R. Zhao, P. J. Kollmeyer, R. D. Lorenz and T. M. Jahns, "A compact unified methodology via a recurrent neural network for accurate modeling of lithium-ion battery voltage and state-of-charge," 2017 IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, 2017, pp. 5234-5241. doi: 10.1109/ECCE.2017.8096879 P.J. Kollmeyer, A. Hackl and A. Emadi, "Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization," 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago, IL, 2017, pp. 486-492. doi: 10.1109/ITEC.2017.7993319 P. J. Kollmeyer, Development and Implementation of a Battery-Electric Light-Duty Class 2a Truck including Hybrid Energy Storage, PhD Dissertation, Dept. Elect. Eng., University of Wisconsin-Madison, 2015. For the tests, a brand new 2.9Ah Panasonic 18650PF cell was tested in an 8 cu.ft. thermal chamber with a 25 amp, 18 volt Digatron Firing Circuits Universal Battery Tester channel. A series of tests, including HPPC, drive cycles, and impedance spectroscopy, were performed at five different temperatures. The battery was charged after each test at a 1C rate to 4.2V, 50mA cut off, with battery temperature 12degC or greater). See the readme file for additional details.

本数据集包含的测试由美国威斯康星大学麦迪逊分校的Phillip J. Kollmeyer博士（电子邮箱：[e-mail]）完成。若将本数据用于任何用途，均需进行恰当引用。本测试数据可用于测试神经网络与卡尔曼滤波荷电状态（State of Charge, SOC）算法，或用于构建电池模型，旨在作为基准数据集供研究人员对比其算法与模型在标准数据集上的性能表现。该测试数据或同类数据已被多篇学术论文采用，包括：E. Chemali、P. J. Kollmeyer、M. Preindl、R. Ahmed与A. Emadi，"用于精确估算锂离子电池荷电状态的长短期记忆网络"，发表于《IEEE工业电子学汇刊》（IEEE Transactions on Industrial Electronics），第65卷第8期，第6730-6739页，2018年8月。DOI: 10.1109/TIE.2017.2787586；R. Zhao、P. J. Kollmeyer、R. D. Lorenz与T. M. Jahns，"基于循环神经网络的紧凑统一方法：精确建模锂离子电池电压与荷电状态"，2017 IEEE能源转换大会暨展览会（ECCE），美国俄亥俄州辛辛那提，2017年，第5234-5241页。DOI: 10.1109/ECCE.2017.8096879；P.J. Kollmeyer、A. Hackl与A. Emadi，"带电流脉冲与电化学阻抗谱参数化的汽车循环工况下锂离子电池模型性能"，2017 IEEE交通运输电气化大会暨博览会（ITEC），美国伊利诺伊州芝加哥，2017年，第486-492页。DOI: 10.1109/ITEC.2017.7993319；P. J. Kollmeyer，"包含混合储能系统的纯电动轻型2a类卡车的开发与实现"，威斯康星大学麦迪逊分校电子工程系博士学位论文，2015年。本次测试采用全新2.9Ah松下（Panasonic）18650PF电芯，在容积8立方英尺的恒温箱中，通过25A、18V规格的Digatron Firing Circuits通用电池测试仪通道完成。测试涵盖混合脉冲功率特性（Hybrid Pulse Power Characterization, HPPC）测试、循环工况测试与电化学阻抗谱测试，共覆盖5种不同测试温度。每次测试结束后，以1C倍率对电池充电至4.2V，截止电流设为50mA，且要求充电时电池温度不低于12℃。更多详细信息请参阅自述文件（readme）。

搜集汇总

背景与挑战

背景概述

该数据集包含Panasonic 18650PF锂离子电池的测试数据，由威斯康星大学麦迪逊分校的Phillip Kollmeyer博士在五个不同温度下进行实验，涵盖HPPC、驱动循环和阻抗谱等多种测试。数据集主要用于开发和验证神经网络、卡尔曼滤波的荷电状态估计算法以及电池模型，为研究人员提供标准参考以比较算法性能。