Physics-Based Modelling and Simulation Framework for Multi-Objective Optimization of Lithium-Ion Cells in Electric Vehicle Applications

dc.contributor.advisorEl-Mounayri, Hazim
dc.contributor.advisorTovar, Andres
dc.contributor.authorGaonkar, Ashwin
dc.contributor.otherZhu, Likun
dc.contributor.otherShin, Hosop
dc.date.accessioned2022-05-27T14:32:00Z
dc.date.available2022-05-27T14:32:00Z
dc.date.issued2022-05
dc.degree.date2022en_US
dc.degree.disciplineMechanical Engineeringen
dc.degree.grantorPurdue Universityen_US
dc.degree.levelM.S.M.E.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractIn the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms. To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22microns to 240microns and the porosity varies from 0.22 to 0.54. Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade.en_US
dc.identifier.urihttps://hdl.handle.net/1805/29181
dc.identifier.urihttp://dx.doi.org/10.7912/C2/2931
dc.language.isoen_USen_US
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectLithium-ion batteryen_US
dc.subjectBayesian optimizationen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectCycling performance simulationen_US
dc.subjectFast chargingen_US
dc.subjectCapacity fadeen_US
dc.subjectNickel rich cathode materialen_US
dc.titlePhysics-Based Modelling and Simulation Framework for Multi-Objective Optimization of Lithium-Ion Cells in Electric Vehicle Applicationsen_US
dc.typeThesisen
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