Battery Test Data (LiFePO4 18650 Rechargeable Cell 3.3V 1100 mAh, Panasonic NCR18650B 3400mAh, Murata VTC6 18650 3000mAh 15A)

The included tests were performed at the University of Malaya by Dr. Prashant Shrivastava ([e-mail]). If this data is utilized for any purpose, it should be appropriately referenced. The tests can be used to test Machine Learning Algorithms, Non-Linear observers and Filters Kalman Filter based State of Charge / State of Energy 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: P. Shrivastava, T. K. Soon, M. Y. I. B. Idris, S. Mekhilef and S. B. R. S. Adnan, " Comprehensive Co-estimation of Lithium-ion Battery States (SOC, SOE, SOP), Actual Capacity and Maximum Available Energy for EV Applications” J. Energy Storage, vol. 56, p. 102704, Dec. 2022, https://doi.org/10.1016/j.est.2022.106049. P. Shrivastava, T. K. Soon, M. Y. I. B. Idris, S. Mekhilef and S. B. R. S. Adnan, “Model-based SOX estimation of Lithium-ion Battery for Electric Vehicle Applications” Int J Energy Res. 2022; 46 (8): 10704- 10723. doi:10.1002/er.7874. P. Shrivastava, T. K. Soon, M. Y. I. B. Idris, S. Mekhilef and S. B. R. S. Adnan, "Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery using Dual Forgetting Factor-based Adaptive Extended Kalman Filter for Electric Vehicle Applications," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2021.3051655. P. Shrivastava, T. K. Soon, M. Yamani Bin Idris and S. Mekhilef, "Lithium-ion Battery Model Parameter Identification Using Modified Adaptive Forgetting Factor-Based Recursive Least Square Algorithm," 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), 2021, pp. 2169-2174, doi: 10.1109/ECCE-Asia49820.2021.9479079. P. Shrivastava, T. Kok Soon, M. Yamani Bin Idris, S. Mekhilef and S. Bahari Ramadzan Syed Adnan, "Lithium-ion Battery State of Energy Estimation Using Deep Neural Network and Support Vector Regression," 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), 2021, pp. 2175-2180, doi: 10.1109/ECCE-Asia49820.2021.9479413. For the tests, brand new 18650 cells of different chemistries such as LFP, NCA, and NMC were tested under controlled temperature using ESPEC SU-241 temperature chamber with Neware BTS 4000 battery tester. A series of tests, including drive cycles including DST, FUDS, UDDS, WLTP, US06; HPPC, and pulse (dis) charge test, were performed at four different temperatures.

本数据集包含的测试由马来亚大学的普拉尚特·斯里瓦斯塔瓦（Prashant Shrivastava）博士（邮箱：[e-mail]）完成。若将本数据集用于任何用途，请进行恰当引用。

本测试可用于机器学习算法、非线性观测器以及基于卡尔曼滤波（Kalman Filter）的荷电状态（State of Charge, SOC）/能量状态（State of Energy, SOE）算法的测试，亦可用于构建电池模型，旨在作为标准数据集供研究人员对比其算法与模型的性能表现。

本测试数据或同类数据已被多篇学术论文采用，包括：
1. P. Shrivastava、T. K. Soon、M. Y. I. B. Idris、S. Mekhilef 与 S. B. R. S. Adnan，《面向电动汽车应用的锂离子电池状态（SOC、SOE、SOP）、实际容量与最大可用能量的综合协同估计》，《Journal of Energy Storage》，第56卷，第102704页，2022年12月，https://doi.org/10.1016/j.est.2022.106049。
2. P. Shrivastava、T. K. Soon、M. Y. I. B. Idris、S. Mekhilef 与 S. B. R. S. Adnan，《面向电动汽车应用的锂离子电池基于模型的SOX估计》，《International Journal of Energy Research》，2022；46(8)：10704-10723，doi:10.1002/er.7874。
3. P. Shrivastava、T. K. Soon、M. Y. I. B. Idris、S. Mekhilef 与 S. B. R. S. Adnan，《面向电动汽车应用的基于双遗忘因子自适应扩展卡尔曼滤波的锂离子电池荷电状态与能量状态联合估计》，《IEEE Transactions on Vehicular Technology》，doi: 10.1109/TVT.2021.3051655。
4. P. Shrivastava、T. K. Soon、M. Yamani Bin Idris 与 S. Mekhilef，《基于改进自适应遗忘因子递推最小二乘算法的锂离子电池模型参数辨识》，2021 IEEE第12届能量转换大会暨展览会-亚洲（ECCE-Asia），2021年，第2169-2174页，doi: 10.1109/ECCE-Asia49820.2021.9479079。
5. P. Shrivastava、T. Kok Soon、M. Yamani Bin Idris、S. Mekhilef 与 S. Bahari Ramadzan Syed Adnan，《基于深度神经网络与支持向量回归的锂离子电池能量状态估计》，2021 IEEE第12届能量转换大会暨展览会-亚洲（ECCE-Asia），2021年，第2175-2180页，doi: 10.1109/ECCE-Asia49820.2021.9479413。

本次测试采用全新的18650型锂离子电池，涵盖LFP、NCA、NMC等不同化学体系，使用ESPEC SU-241恒温箱与Neware BTS 4000电池测试仪在可控温度环境下完成。测试包含多种工况，包括DST、FUDS、UDDS、WLTP、US06等行驶循环，以及HPPC与脉冲充放电测试，共在四种不同温度下开展。

搜集汇总

背景与挑战

背景概述

该数据集包含三种18650锂离子电池（LiFePO4、Panasonic NCR18650B和Murata VTC6）的测试数据，由马来亚大学研究人员在控制温度下采集，涵盖多种驱动循环和脉冲测试，适用于机器学习算法、卡尔曼滤波器状态估计及电池模型开发。数据已用于多项学术研究，旨在为电动汽车应用提供标准参考。