Battery Test Data (LiFePO4 18650 Rechargeable Cell 3.3V 1100 mAh, Panasonic NCR18650B 3400mAh, Murata VTC6 18650 3000mAh 15A)
Description
The included tests were performed at the University of Malaya by Dr. Prashant Shrivastava (prashant.xev.ess@gmail.com). 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.