Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- The Use of Evolutionary Algorithms for the Design of Lithium-Ion Battery Packs and Battery Cells(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2013-12-05) Rodríguez Montoya, César Alejandro; Sosa Hernández, Víctor Adrián; emimmayorquin; School of Engineering and Sciences; Campus Estado de MéxicoIn the contemporary landscape, the prevailing shift towards the adoption of electric vehicles for personal transportation has propelled lithium batteries into the spotlight. Consequently, the demand for better-optimized batteries has surged, driven by the aspiration for enhanced performance without compromising cost-effectiveness or longevity. This research delves into the use of evolutionary algorithms in the pursuit of lithium battery optimization. To address this multifaceted challenge, we have formulated the battery design problem as a constrained many-objective optimization problem (CMaOP). Within this context, our set of objective functions encompasses critical battery attributes: the maximization of specific energy, and durability; and the minimization of heat generation, and price. The decision variables encapsulate various physical characteristics of the battery that can be fine-tuned during the manufacturing process. These variables include the choice of materials for the positive electrode, dimensions of individual layers, and geometric characteristics of the battery canister, among others. The investigation is conducted with a specific focus on three distinct applications: electric vehicles, drones, and cell phones. The requirements of these applications establish the constraints of the problem. To tackle this problem, we have extended and adapted the Island-based Multi-Indicator Algorithm (IMIA) framework yielding into the Island-based Multi-Indicator Constraint-handler Algorithm (IMICA). The algorithm relies on the cooperative work of various quality indicators to favor the generation of optimized solutions while meeting the concepts of coverage and distribution in the Pareto front approximation. The algorithm was able to solve the problem efficiently. When compared with the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), the algorithm managed to find a greater number of non-dominated feasible solutions and a greater hypervolume. Furthermore, the solutions found by the algorithm also prove to be competitive against standardized batteries.