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Optimal Fast-charging Strategy for Cylindrical Li-ion Cells
SOLiTHOR, Ondernemeerslaan 5429, 3800 Sint-Truiden, Belgium
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Received: 22 May 2024 Accepted: 27 August 2024 Published: 9 September 2024
Abstract
This paper presents an innovative approach to optimize the fast-charging strategy for cylindrical Li-ion NMC 3Ah cells, with a focus on enhancing both charging efficiency and thermal safety. Leveraging the power of Model Predictive Control (MPC), we introduce a cost function that approximates the thermal safety boundary of Li-ion batteries, revealing a relationship between temperature gradient and state of charge. Our proposed approach formulates the fast and safe charging problem as an optimal output regulator problem, incorporating thermal safety margin constraints. To solve the optimization problem, we develop an MPC algorithm. Our charge control structure incorporates an equivalent circuit model coupled with a thermal equation for battery state of charge and temperature estimation. Through numerical validation with real experimental data obtained from testing an NMC 3Ah cylindrical cell, we demonstrate that our approach respects the battery’s electrical and thermal constraints throughout the charging process.
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Copyright © 2024
Jaguemont et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use and distribution provided that the original work is properly cited.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Cite this Article
Jaguemont, J., Darwiche, A., & Bardé, F. (2024). Optimal Fast-charging Strategy for Cylindrical Li-ion Cells. Highlights of Vehicles, 2(2), 24–34. https://doi.org/10.54175/hveh2020003
References
1.
Armand, M., Axmann, P., Bresser, D., Copley, M., Edström, K., Ekberg, C., et al. (2020). Lithium-ion batteries–Current state of the art and anticipated developments. Journal of Power Sources, 479, 228708. https://doi.org/10.1016/j.jpowsour.2020.228708
2.
Suarez, C., & Martinez, W. (29 September 2019–03 October 2019). Fast and ultra-fast charging for battery electric vehicles–a review. 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA. https://doi.org/10.1109/ECCE.2019.8912594
3.
LaMonaca, S., & Ryan, L. (2022). The state of play in electric vehicle charging services–A review of infrastructure provision, players, and policies. Renewable and Sustainable Energy Reviews, 154, 111733. https://doi.org/10.1016/j.rser.2021.111733
4.
Liu, S., Liu, X., Dou, R., Zhou, W., Wen, Z., & Liu, L. (2020). Experimental and simulation study on thermal characteristics of 18,650 lithium–iron–phosphate battery with and without spot–welding tabs. Applied Thermal Engineering, 166, 114648. https://doi.org/10.1016/j.applthermaleng.2019.114648
5.
Ouyang, D., Weng, J., Chen, M., & Wang, J. (2020). Impact of high-temperature environment on the optimal cycle rate of lithium-ion battery. Journal of Energy Storage, 28, 101242. https://doi.org/10.1016/j.est.2020.101242
6.
Zhang, G., Wei, X., Han, G., Dai, H., Zhu, J., Wang, X., et al. (2021). Lithium plating on the anode for lithium-ion batteries during long-term low temperature cycling. Journal of Power Sources, 484, 229312. https://doi.org/10.1016/j.jpowsour.2020.229312
7.
Ouyang, D., Weng, J., Chen, M., Wang, J., & Wang, Z. (2022). Electrochemical and thermal characteristics of aging lithium-ion cells after long-term cycling at abusive-temperature environments. Process Safety and Environmental Protection, 159, 1215–1223. https://doi.org/10.1016/j.psep.2022.01.055
8.
Inuzuka, S., Shen, T., & Kojima, T. (2020). Dynamic programming based energy management of HEV with three driving modes. In IOP Conference Series: Materials Science and Engineering (Vol. 715, No. 1, p. 012063). IOP Publishing. https://doi.org/10.1088/1757-899X/715/1/012063
9.
Padovani, T. M., Debert, M., Colin, G., & Chamaillard, Y. (2013). Optimal energy management strategy including battery health through thermal management for hybrid vehicles. IFAC Proceedings Volumes, 46(21), 384–389. https://doi.org/10.3182/20130904-4-JP-2042.00137
10.
Park, S., & Ahn, C. (2020). Computationally efficient stochastic model predictive controller for battery thermal management of electric vehicle. IEEE Transactions on Vehicular Technology, 69(8), 8407–8419. https://doi.org/10.1109/TVT.2020.2999939
11.
Choi, M. E., Lee, J. S., & Seo, S. W. (2014). Real-time optimization for power management systems of a battery/supercapacitor hybrid energy storage system in electric vehicles. IEEE Transactions on Vehicular Technology, 63(8), 3600–3611. https://doi.org/10.1109/TVT.2014.2305593
12.
Zhu, C., Lu, F., Zhang, H., & Mi, C. C. (2018). Robust predictive battery thermal management strategy for connected and automated hybrid electric vehicles based on thermoelectric parameter uncertainty. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6(4), 1796–1805. https://doi.org/10.1109/JESTPE.2018.2852218
13.
Afzal, A., Mohammed Samee, A. D., Abdul Razak, R. K., & Ramis, M. K. (2021). Thermal management of modern electric vehicle battery systems (MEVBS). Journal of Thermal Analysis and Calorimetry, 144, 1271–1285. https://doi.org/10.1007/s10973-020-09606-x
14.
Zou, C., Manzie, C., & Nešić, D. (2018). Model predictive control for lithium-ion battery optimal charging. IEEE/ASME Transactions on Mechatronics, 23(2), 947–957. https://doi.org/10.1109/TMECH.2018.2798930
15.
Zou, C., Hu, X., Wei, Z., Wik, T., & Egardt, B. (2017). Electrochemical estimation and control for lithium-ion battery health-aware fast charging. IEEE Transactions on Industrial Electronics, 65(8), 6635–6645. https://doi.org/10.1109/TIE.2017.2772154
16.
Sony. (2015). Lithium Ion Rechargeable Battery Technical Information. https://www.kronium.cz/uploads/SONY_US18650VTC6.pdf (accessed 4 September 2024).
17.
Berliner, M. D., Jiang, B., Cogswell, D. A., Bazant, M. Z., & Braatz, R. D. (2022). Fast charging of lithium-ion batteries by mathematical reformulation as mixed continuous-discrete simulation. In 2022 American Control Conference (ACC) (pp. 5265–5270). IEEE. https://doi.org/10.23919/ACC53348.2022.9867170
18.
Jaguemont, J., Omar, N., Abdel-Monem, M., Van den Bossche, P., & Van Mierlo, J. (2018). Fast-charging investigation on high-power and high-energy density pouch cells with 3D-thermal model development. Applied Thermal Engineering, 128, 1282–1296. https://doi.org/10.1016/j.applthermaleng.2017.09.068
19.
Choe, S. Y., Li, X., & Xiao, M. (2013). Fast charging method based on estimation of ion concentrations using a reduced order of Electrochemical Thermal Model for lithium ion polymer battery. In 2013 World Electric Vehicle Symposium and Exhibition (EVS27) (pp. 1–11). IEEE. https://doi.org/10.1109/EVS.2013.6914966
20.
He, H., Xiong, R., & Fan, J. (2011). Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies, 4(4), 582–598. https://doi.org/10.3390/en4040582
21.
Jaguemont, J., Boulon, L., & Dubé, Y. (2015). Characterization and modeling of a hybrid-electric-vehicle lithium-ion battery pack at low temperatures. IEEE Transactions on Vehicular Technology, 65(1), 1–14. https://doi.org/10.1109/TVT.2015.2391053
22.
White, G., Hales, A., Patel, Y., & Offer, G. (2022). Novel methods for measuring the thermal diffusivity and the thermal conductivity of a lithium-ion battery. Applied Thermal Engineering, 212, 118573. https://doi.org/10.1016/j.applthermaleng.2022.118573
23.
Wu, M. S., Liu, K. H., Wang, Y. Y., & Wan, C. C. (2002). Heat dissipation design for lithium-ion batteries. Journal of Power Sources, 109(1), 160–166. https://doi.org/10.1016/S0378-7753(02)00048-4
24.
Robinson, P. R., & Cima, D. (2017). Model-Predictive Control Fundamentals. Springer Handbook of Petroleum Technology, 833–839. https://doi.org/10.1007/978-3-319-49347-3_26
25.
Meng, J., Yue, M., & Diallo, D. (2023). Nonlinear extension of battery constrained predictive charging control with transmission of Jacobian matrix. International Journal of Electrical Power & Energy Systems, 146, 108762. https://doi.org/10.1016/j.ijepes.2022.108762
26.
Li, Y., Wik, T., Huang, Y., & Zou, C. (2023). Nonlinear model inversion-based output tracking control for battery fast charging. IEEE Transactions on Control Systems Technology, 32(1), 225–240. https://doi.org/10.1109/TCST.2023.3306240
27.
Jia, G., & Cai, X. (2022). Fast charging strategy of lithium-ion batteries with thermal safety margin based on model predictive control. In 2022 41st Chinese Control Conference (CCC) (pp. 2694–2699). IEEE. https://doi.org/10.23919/CCC55666.2022.9902174
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