Browsing by Subject "Bayesian optimization"

Now showing 1 - 5 of 5
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    Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization
    (ASME, 2019-12) Liu, Kai; Wu, Tong; Detwiler, Duane; Panchal, Jitesh; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and Technology
    This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.
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    Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials
    (Elsevier, 2022-04-30) Valladares, Homero; Li , Tianyi; Zhu, Likun; El-Mounayri, Hazim; Hashem, Ahmed M.; Abdel-Ghany, Ashraf E.; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and Technology
    The increasing adoption of lithium-ion batteries (LIBs) in consumer electronics, electric vehicles, and smart grids poses two challenges: the accurate prediction of the battery health to prevent operational impairments and the development of new materials for high-performance LIBs. Characterized by their flexibility and mathematical tractability, Gaussian processes (GPs) provide a powerful framework for monitoring and optimization tasks. This study employs two GP-based techniques: co-kriging surrogate modelling and Bayesian optimization. The GP training data comes from the cycling performance test of five CR2032 cells with Ni contents of 0.0, 0.4, 0.5, 0.6, and 1.0 in their cathode active material Li2NixMn2-xO4. The co-kriging surrogate predicts the capacity degradation profile of a cell by exploiting information from different cells. Bayesian optimization identifies new Ni compositions that can produce cells with high initial specific capacity and large cycle life. The study shows the predictive capabilities of the co-kriging surrogate when correlated data is available. Bayesian optimization predicts that a Ni content of 0.44 produces cells with an initial specific capacity of 103.4 ± 3.8 mAh g−1 and a cycle life of 595 ± 12 cycles. Furthermore, the Bayesian strategy identifies other Ni contents that can improve the overall quality of the current Pareto front.
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    Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios
    (MDPI AG, 2022-05-31) Gaonkar, Ashwin; Valladares, Homero; Tovar, Andres; Zhu, Likun; El-Mounayri , Hazim; Mechanical Engineering, School of Engineering and Technology
    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 their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.
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    Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios
    (MDPI, 2022-05-31) Gaonkar, Ashwin; Valladares, Homero; Tovar, Andres; Zhu, Likun; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    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 their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.
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    Physics-Based Modelling and Simulation Framework for Multi-Objective Optimization of Lithium-Ion Cells in Electric Vehicle Applications
    (2022-05) Gaonkar, Ashwin; El-Mounayri, Hazim; Tovar, Andres; Zhu, Likun; Shin, Hosop
    In 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.