Ciencias Exactas y Ciencias de la Salud

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Design and implementation of hess for small vehicle applications
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-02) López Celis, Nicolás Alonso; Escobar, Gerardo; emimmayorquin; Valdez, Jesús Elias; Escuela de ingeniería; Campus Monterrey; Schacht, Ricardo
This thesis investigates the integration and optimization of hybrid energy storage systems (HESS) for small electric vehicles, focusing on the combined use of batteries and Super Ca- pacitors (SCs). The primary objective is to enhance the overall performance and longevity of the storage system by leveraging the complementary characteristics of batteries and SCs. This research encompasses the modeling, simulation, and control of HESS, utilizing control strategies to manage the distribution and separation of energy between the two sources, en- suring the safety of the energy sources. Initially, the thesis delves into the state of the art of HESS, detailing the different plant and control configurations, including passive and active topologies. The dynamic models of batteries and SCs are developed using equivalent circuits, emphasizing the balance between model fidelity and control complexity. Additionally, the study explores various driving cycles, such as UDDS, EUDC, and Artemis Road, to assess the performance of HESS under different driving conditions. The core of the research is the implementation of a battery protection system. The State of Power (SOP ) algorithm is employed to dynamically adjust power limits based on real bat- tery conditions, thereby enhancing safety and performance. Furthermore, a global controller, specifically a Rule Base (RB) algorithm, is integrated to manage the State of Charge (SOC) of the SC. This ensures proper power distribution, reduces battery stress, and extends the lifes- pan of the storage components. Simulation results, conducted using MATLAB and SIMULINK, are presented to demonstrate the effectiveness of the proposed HESS configuration and control strategies. The simulations include the conversion of speed profiles into power demands and the allocation of power to the battery and SC using a low pass filter (LPF ). The results highlight the differences between ideal and actual power allocation, showcasing the effectiveness of the SOP and RB algo- rithms in managing energy allocation. The converter controllers maintain the desired voltage levels in the DC bus. The findings underscore the importance of integrating batteries and SCs in a HESS for small electric vehicles, offering performance improvements. The research also identifies areas for future work, including the need for more accurate models and real-world testing to validate the simulation results.
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 México
In 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.

Ciencias Exactas y Ciencias de la Salud

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