Adaptive particle swarm optimization; Lithium iron phosphate; Lithium-ion battery; Optimal cell design; Reduced-order electrochemical model; Sensitivity analysis; Adaptive particle swarm optimizations; Cell design; Design optimization; Electrochemical modeling; Energy density; Half cells; Particle radii; Reduced order; Civil and Structural Engineering; Modeling and Simulation; Renewable Energy, Sustainability and the Environment; Building and Construction; Fuel Technology; Energy Engineering and Power Technology; Pollution; Mechanical Engineering; Energy (all); Management, Monitoring, Policy and Law; Industrial and Manufacturing Engineering; Electrical and Electronic Engineering; General Energy
Abstract :
[en] Model-based optimal cell design is an efficient approach to maximize the energy density of lithium-ion batteries. This maximization problem is solved in this paper for a lithium iron phosphate (LFP) cell. We consider half-cells as opposed to full-cells typically considered, which are intermediate steps during battery manufacturing for electrode characterization and they are gaining popularity by themselves as lithium-metal batteries. First, a dimensionless reduced-order electrochemical model is used instead of high-order models. Second, sensitivity equations are analyzed to determine the ranking of the design parameters according to their effect on the energy density, which is often lacking in other contributions. Three parameters, namely electrode thickness, LFP particle radius and electrode cross sectional area, are shown to have the most influential effects. Third, a novel adaptive particle swarm optimization with a specific stopping criterion is used for LIB design optimization. The proposed optimization framework is tested in simulation on a LFP half-cell battery. The results show that the design optimization yields 250 Wh kg−1 for an LFP electrode of 310μm thickness, 10 nm particle radius and 2⋅10−4 m2 cross-sectional area, which is an increase of energy density of 61 Wh kg−1 with respect to an initial design proposed in the literature.
Disciplines :
Energy
Author, co-author :
Couto, Luis. D. ; Department of Control Engineering and System Analysis, Universite libre de Bruxelles, Brussels, Belgium
Charkhgard, Mohammad; Department of Control Engineering and System Analysis, Universite libre de Bruxelles, Brussels, Belgium
Karaman, Berke ; Université de Liège - ULiège > Chemical engineering
Job, Nathalie ; Université de Liège - ULiège > Department of Chemical Engineering > Ingéniérie électrochimique : matériaux et procédés pour la transformation et le stockage d'énergie
Kinnaert, Michel ; Department of Control Engineering and System Analysis, Universite libre de Bruxelles, Brussels, Belgium
Language :
English
Title :
Lithium-ion battery design optimization based on a dimensionless reduced-order electrochemical model
This work is supported by the Fonds de la Recherche Scientifique — FNRS, Belgium under grant № T.0142.20 . The first author would also like to thank the Wiener-Anspach Foundation for its financial support.
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Bibliography
Georgious, R., Refaat, R., Garcia, J., Daoud, A.A., Review on energy storage systems in microgrids. Electronics, 10(17), 2021, 2134.
González, I., Calderón, A.J., Folgado, F.J., IoT real time system for monitoring lithium-ion battery long-term operation in microgrids. J Energy Storage, 51, 2022, 104596.
Cao, W., Xiao, J.-W., Cui, S.-C., Liu, X.-K., An efficient and economical storage and energy sharing model for multiple multi-energy microgrids. Energy, 244, 2022, 123124.
Chaturvedi, N.A., Klein, R., Christensen, J., Ahmed, J., Kojic, A., Algorithms for advanced battery-management systems. IEEE Control Syst 30:3 (2010), 49–68.
Xue, N., Du, W., Gupta, A., Shyy, W., Marie Sastry, A., Martins, J.R.R.A., Optimization of a single lithium-ion battery cell with a gradient-based algorithm. J Electrochem Soc 160:8 (2013), A1071–A1078.
Wang, S., Jin, S., Bai, D., Fan, Y., Shi, H., Fernandez, C., A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep 7 (2021), 5562–5574.
Wang, S., Takyi-Aninakwa, P., Jin, S., Yu, C., Fernandez, C., Stroe, D.-I., An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation. Energy, 254, 2022, 124224.
Hu, X., Li, S., Peng, H., A comparative study of equivalent circuit models for Li-ion batteries. J Power Sources 198 (2012), 359–367.
Wang, Y., Fang, H., Zhou, L., Wada, T., Revisiting the state-of-charge estimation for lithium-ion batteries: A methodical investigation of the extended Kalman filter approach. IEEE Control Syst Mag 37:4 (2017), 73–96.
Zou, C., Manzie, C., Nešić, D., A framework for simplification of PDE-based lithium-ion battery models. IEEE Trans Control Syst Technol 24:5 (2016), 1594–1609.
Zhang, D., Dey, S., Couto, L.D., Moura, S.J., Battery adaptive observer for a single-particle model with intercalation-induced stress. IEEE Trans Control Syst Technol, 2019, 1–15.
Doyle, M., Fuller, T., Newman, J., Modeling of galvanostatic charge and discharge of the lithium/ polymer/insertion cell. J Electrochem Soc 140:6 (1993), 1526–1533.
Fuller, T.F., Doyle, M., Newman, J., Simulation and optimization of the dual lithium ion insertion cell. J Electrochem Soc 141:1 (1994), 1–10.
Thomas-Alyea, K.E., Newman, J., Darling, R.M., Mathematical modeling of lithium batteries. van Schalkwijk, W.A., Scrosati, B., (eds.) Advances in lithium-ion batteries, 1st ed, 2002, Springer US, New York, 345–392.
Newman, J., Thomas-Alyea, K.E., Electrochemical systems. 3rd ed, 2004, John Wiley & Sons, Inc., Hoboken, New Jersey.
Ramadesigan, V., Northrop, P.W.C., De, S., Santhanagopalan, S., Braatz, R.D., Subramanian, V.R., Modeling and simulation of lithium-ion batteries from a systems engineering perspective. J Electrochem Soc 159:3 (2012), R31–R45.
Dai, Y., Srinivasan, V., On graded electrode porosity as a design tool for improving the energy density of batteries. J Electrochem Soc 163:3 (2016), A406–A416.
Kim, J.-S., Lee, D.-C., Lee, J.-J., Kim, C.-W., Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments. Sci Rep, 10(1), 2020, 15586.
Astaneh, M., Andric, J., Löfdahl, L., Stopp, P., Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications. Energy, 239, 2022, 122092.
Hosseinzadeh, E., Marco, J., Jennings, P., Electrochemical-thermal modelling and optimisation of lithium-ion battery design parameters using analysis of variance. Energies 10:1278 (2017), 1–22.
Golmon, S., Maute, K., Dunn, M.L., Multiscale design optimization of lithium ion batteries using adjoint sensitivity analysis. Internat J Numer Methods Engrg 92:5 (2012), 475–494.
Ning, G., Popov, B.N., Cycle life modeling of lithium-ion batteries. J Electrochem Soc 151:10 (2004), A1584–A1591.
Santhanagopalan, S., Guo, Q., Ramadass, P., White, R.E., Review of models for predicting the cycling performance of lithium ion batteries. J Power Sources 156:2 (2006), 620–628 c.
Guo, M., Sikha, G., White, R.E., Single-particle model for a lithium-ion cell: Thermal behavior. J Electrochem Soc 158:2 (2011), A122–A132.
Moura, S.J., Argomedo, F.B., Klein, R., Mirtabatabaei, A., Krstic, M., Battery state estimation for a single particle model with electrolyte dynamics. IEEE Trans Control Syst Technol 25:2 (2017), 453–468.
Tanim, T.R., Rahn, C.D., Wang, C.-Y., State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model. Energy 80 (2015), 731–739.
Di Domenico, D., Stefanopoulou, A., Fiengo, G., Lithium-ion battery state of charge and critical surface charge estimation using an electrochemical model-based extended Kalman filter. J Dyn Syst Meas Control 132:6 (2010), 061302.1–061302.11.
Manwell, J.F., McGowan, J.G., Lead acid battery storage model for hybrid energy systems. Sol Energy 50:5 (1993), 399–405.
Newman, J., Optimization of porosity and thickness of a battery electrode by means of a reaction-zone model. J Electrochem Soc 142:1 (1995), 97–101.
Lee, D.-C., Lee, K.-J., Kim, C.-W., Optimization of a lithium-ion battery for maximization of energy density with design of experiments and micro-genetic algorithm. Int J Precis Eng Manuf Green Technol 7:4 (2020), 829–836.
Liu, C., Liu, L., Optimal design of Li-ion batteries through multi-physics modeling and multi-objective optimization. J Electrochem Soc 164:11 (2017), E3254–E3264.
Wang, F., Zhang, H., Zhou, A., A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm Evol Comput, 60, 2021, 100808.
Alfi, A., Modares, H., System identification and control using adaptive particle swarm optimization. Appl Math Model 35:3 (2011), 1210–1221.
Srinivasan, V., Newman, J., Discharge model for the lithium iron-phosphate electrode. J Electrochem Soc 151:10 (2004), A1517–A1529.
Safari, M., Delacourt, C., Modeling of a commercial graphite/LiFePO4 cell. J Electrochem Soc 158:5 (2011), A562–A571.
Wayland, M., Tesla will change the type of battery cells it uses in all its standard-range cars. 2021, CNBC https://tinyurl.com/yc58nxh8.
Moura, S.J., Chaturvedi, N.A., Krstić, M., Adaptive partial differential equation observer for battery state-of-charge/state-of-health estimation via an electrochemical model. J Dyn Syst Meas Control, 136(1), 2014, 011015–1–011015–11.
Couto, L.D., Electrochemical modeling, supervision and control of lithium-ion batteries. [Ph.D. thesis], 2018, École Polytechnique de Bruxelles, ULB, Brussels, Belgium.
Marquis, S.G., Sulzer, V., Timms, R., Please, C.P., Chapman, S.J., An asymptotic derivation of a single particle model with electrolyte. J Power Sources, 2019.
Christensen, J., Modeling diffusion-induced stress in Li-ion cells with porous electrodes. J Electrochem Soc 157:3 (2010), A366–A380.
Gommes, C.J., Gilet, T., Combine dimensional analysis with educated guessing. Chem Eng Prog 114:2 (2018), 49–54.
Engelbrecht, A.P., Computational intelligence: An introduction. 2007, John Wiley & Sons.
Klein, R., Chaturvedi, N.A., Christensen, J., Ahmed, J., Findeisen, R., Kojic, A., Electrochemical model based observer design for a lithium-ion battery. IEEE Trans Control Syst Technol 21:2 (2013), 289–301.
Rahn, C.D., Wang, C.-Y., Governing equations. Battery systems engineering, 2013, John Wiley & Sons Ltd, 23–48.
Stewart, S., Albertus, P., Srinivasan, V., Plitz, I., Pereira, N., Amatucci, G., et al. Optimizing the performance of lithium titanate spinel paired with activated carbon or iron phosphate. J Electrochem Soc 155:3 (2008), A253–A261.
Albertus, P., Couts, J., Srinivasan, V., Newman, J., II. A combined model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles. J Power Sources 183:2 (2008), 771–782.
Valoen, L.O., Reimers, J.N., Transport properties of LiPF6-based Li-ion battery electrolytes. J Electrochem Soc, 152(5), 2005, A882.
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