Abstract
Lithium-ion battery ageing modelling and prediction is one of the most relevant topics in the energy storage research field. The development and assessment of reliable solutions are not straightforward, because of the necessity to acquire information on the cell ageing processes by employing very time-consuming tests. During these tests the cells are subjected to different profiles, usually based on the repetition of several charge/discharge cycles, in order to reproduce the ageing effects in laboratory. This paper aims at accelerating the advancement in this research field by discussing a dataset containing three different ageing tests and making it available to be used by other research groups. The tests are accurately described and a preliminary analysis of the obtained results is carried out.
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References
S. Nejad, D. Gladwin, D. Stone, A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. J. Power Sources 316, 183–196 (2016)
B. Balasingam, G.V. Avvari, B. Pattipati, K.R. Pattipati, Y. Bar-Shalom, A robust approach to battery fuel gauging, part II: real time capacity estimation. J. Power Sources 269, 949–961 (2014)
G.L. Plett, Dual and joint EKF for simultaneous SOC and SOH estimation, in Proceedings of the 21st Electric Vehicle Symposium (EVS21), Monaco (2005), pp. 1–12
E. Locorotondo, L. Pugi, L. Berzi, M. Pierini, A. Pretto, Online state of health estimation of lithium-ion batteries based on improved ampere-count method, in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), June (IEEE, New York, 2018), pp. 1–6
T. Liang, L. Song, K. Shi, On-board incremental capacity/differential voltage curves acquisition for state of health monitoring of lithium-ion batteries, in 2018 IEEE International Conference on Applied System Invention (ICASI), April (IEEE, New York, 2018), pp. 976–979
J. Tian, R. Xiong, Q. Yu, Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries. IEEE Trans. Ind. Electron. 66(2), 1576–1584 (2019)
D.-I. Stroe, E. Schaltz, SOH estimation of LMO/NMC-based electric vehicle lithium-ion batteries using the incremental capacity analysis technique, in 2018 IEEE Energy Conversion Congress and Exposition (ECCE), September (IEEE, New York, 2018), pp. 2720–2725
D. Ansean, M. Gonzalez, C. Blanco, J.C. Viera, Y. Fernandez, V.M. Garcia, Lithium-ion battery degradation indicators via incremental capacity analysis, in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), June (IEEE, New York, 2017), pp. 1–6
D. Stroe, M. Swierczynski, A.-I. Stan, R. Teodorescu, S.J. Andreasen, Experimental investigation on the internal resistance of Lithium iron phosphate battery cells during calendar ageing, in IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, November (IEEE, New York, 2013), pp. 6734–6739
CALCE Battery Research Group Datasets. Available: https://web.calce.umd.edu/batteries/data.htm
NASA Li-ion Battery Aging Datasets. Available: https://data.nasa.gov/dataset/Li-ion-Battery-Aging-Datasets/uj5r-zjdb
N. Yang, J. Feng, Q. Sun, T. Liu, D. Zhong, Online estimation of state-of-health for lithium ion batteries based on charge curves, in 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS), October (IEEE, New York, 2016), pp. 1–8
L. Li, Y. Peng, Y. Song, D. Liu, Lithium-ion battery remaining useful life prognostics using data-driven deep learning algorithm, in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), October (IEEE, New York, 2018), pp. 1094–1100
S. Wang, D. Liu, J. Zhou, B. Zhang, Y. Peng, A run-time dynamic reconfigurable computing system for lithium-ion battery prognosis. Energies 9(8), 572 (2016)
G.L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. J. Power Sources 134(2), 277–292 (2004)
H. Dai, X. Zhang, X. Wei, Z. Sun, J. Wang, F. Hu, Cell-BMS validation with a hardware-in-the-loop simulation of lithium-ion battery cells for electric vehicles. Int. J. Electr. Power Energy Syst. 52, 174–184 (2013)
R. Morello, R. Di Rienzo, R. Roncella, R. Saletti, F. Baronti, Hardware-in-the-loop platform for assessing battery state estimators in electric vehicles, in IEEE Access (2018), p. 1-1
F. Baronti, W. Zamboni, R. Roncella, R. Saletti, G. Spagnuolo, Open-circuit voltage measurement of Lithium–Iron–Phosphate batteries, in 2015 IEEE Int. Instrum. Meas. Technol. Conf. Proc. (IEEE, New York, 2015), pp. 1711–1716
F. Baronti, G. Fantechi, E. Leonardi, R. Roncella, R. Saletti, Enhanced model for Lithium-Polymer cells including temperature effects, in IECON 2010 - 36th Annu. Conf. IEEE Ind. Electron. Soc., November (IEEE, New York, 2010), pp. 2329–2333
Ageing dataset, University of Pisa. Available: https://github.com/batterylabunipi/ageing_dataset
L. Zhang, J. Jiang, W. Zhang, Capacity decay mechanism of the LCO + NMC532/graphite cells combined with post-mortem technique. Energies 10(8), 1147 (2017)
Acknowledgements
This work has partially been supported by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence) and by the University of Pisa under Grant PRA-2017-33 (Urban districts with zero environmental and energetic impact).
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Morello, R., Rienzo, R.D., Roncella, R., Saletti, R., Baronti, F. (2020). Report on Lithium-Ion Battery Ageing Tests. In: Zamboni, W., Petrone, G. (eds) ELECTRIMACS 2019. Lecture Notes in Electrical Engineering, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-030-37161-6_29
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DOI: https://doi.org/10.1007/978-3-030-37161-6_29
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