Ciencias Exactas y Ciencias de la Salud

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Methodological approach to incorporate Deep Generative and Natural Language Processing models in the engineering design process
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) de la Tejera de la Peña, Javier Alberto; Ramírez Mendoza, Ricardo Ambrocio; emimmayorquin; Hernández Luna, Alberto Abelardo; Aguayo Téllez, Humberto; Anthony, Brian; School of Engineering and Sciences; Campus Monterrey; Bustamante Bello, Martín Rogelio
The engineering design process allows to create or enhance designs to fulfill any particular need methodologically. With the advances in artificial intelligence, mainly in Deep Learning, a new perspective is coming to systems engineering design, aided by intelligent algorithms. However, a downfall of engineering design is the lack of quantitative outcomes, making troublesome the use of artificial intelligence. For this, one of the solutions is using axiomatic design (AD) in the design process of systems. The work presented in this thesis, introduces a methodology that involves classic engineering methodologies and the proposal of incorporating current state-of-the-art algorithms and models for the conceptual design, as the main contribution of the research work. This research work and methodology proposal is meant to reduce the time of the design process and understand the needs/requirements implicated in the same design process, with the possibility of developing more robust designs closer to the original needs/requirements.
Machine Learning-Based Methodology for Intelligent Energy Management Strategy in Heavy-Duty Fuel Cell Hybrid Electric Vehicles with Pantograph
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-07) Julio Rodríguez, Jose del Carmen; Ramírez Mendoza, Ricardo Ambrocio; emimmayorquin; School of Engineering and Sciences; Campus Monterrey; Santana Díaz, Alfredo
This work presents the proposal and validation of a novel methodology for enhancing energy management strategies in heavy-duty FCHEVs with overhead current collector (pantograph) by means of ML-based predictions for the characterization of the driver-vehicle system from a holistic approach, correlating its energy and power profiles with characteristics of the route where it transits, specifically the speed profile and the height profile for the complete route. The base concept is the possibility of characterizing the vehicle’s energy use from an approach that also considers the driver behavior and road characteristic. This data-driven characterization using historic and real-time data stream from the vehicle allowed for a ML-model to be trained to make predictions using limited information from the upcoming route. The predictions created with the described method included energy demand, power base-values and optimal SoC profiles. These predictions were then used in an energy management strategy by means of a heuristic controller that received and used the optimal SoC profile and the power demand base-value of the complete route, thus allowing the controller to perform in accordance to current and upcoming vehicle energy demand. The methodology begins with the clusterization of vehicle and road data to define zonetypes for assigning labels to individual samples by means of unsupervised machine learning. Next, the labeled data is used to train a supervised machine learning classification algorithm which is then used to make predictions about the upcoming route. The clusterization and zone type predictions are then used for discretizing the complete route in a step called zonification, where the route is divided into sections with their own characteristics, providing a base on which the energy management strategy can be adjusted and executed accordingly. The data used for these tasks included vehicle dynamics data and energy demand profiles, as well as road information. The information used regarding the road was the expected speed profile and the elevation profile of the route. Both of these features can be obtained from external sources like vehicle to X communication or third-party navigation services and cartography. The ML-enhanced EMS controller was then validated through simulation using real data from 5 different routes in Germany and its performance was compared to that of other 3 controllers which made use of different approaches for the actuation and control of the onboard energy systems. The results were consistent in demonstrating the superior performance of the controller making use of the ML predictions, obtaining the best scores in FC degradation and H2 mass consumption indexes, with 25% and 27% less than the next best performer on each index respectively.
Set based parametric design with dimensionless trade-off curves
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018) Fernández Caballero, Juan Carlos; Ramírez Mendoza, Ricardo Ambrocio; emipsanchez; Sobek II, Durward K.; Moreno Granadas, Diana P.; Capetillo, Azael; Escuela de Ingeniería y Ciencias; Campus Monterrey; Hernández Luna, Alberto Abelardo
Over the past decade, Set-Based design has been getting the attention from researchers looking for strategies to shorten product development cycles while improving performance and saving resources. Most of the publications related to the topic have focused more on practical applications, opening the opportunity to explore theoretical development. While many authors have proposed several methods and tools to enable Set-Based Design, the techniques are often applied individually. Therefore, the need to develop a roadmap integrating many of these methods and tools has been identified. This research work proposes paper integrates the GFIS (Given-Find-In Order To-Subjected To) Method for Problem Statement, Nondimensionalization with Causal Mapping, Dimensionless Trade-off Curves and Labeled Fuzzy Sets. These techniques will contribute to facilitate the generation of multiple solutions in a systematic manner by identifying key parameters, variables, requirements and significant interactions, mapping the design space and finding feasible and preferred designs.

Ciencias Exactas y Ciencias de la Salud

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