Optimization of energy storage and the power management using artificial neural networks

• Grant number: 21/14026-8
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2022
• End date: September 15, 2026
• Field of knowledge: Engineering - Mechanical Engineering
• Principal Investigator: Ludmila Corrêa de Alkmin e Silva
• Grantee: Matheus Henrique Rodrigues Miranda
• Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated scholarship(s): 24/08117-9 - Development of a methodology for vehicle systems using artificial neural networks based on experimental tests, BE.EP.DR

Abstract

To provide the development of new technologies aimed at mobility, the need to understand the dynamic behavior of the systems, and components of the vehicle arises, such as the energy storage system and power management. The focuses of this research are the application of machine learning techniques, specifically the artificial neural networks method, to develop an energy storage model suitable for mainly electric vehicles. In addition, we have the development of a strategy for power management, using artificial neural networks, enabling the adequacy of power division for different driving conditions. The combination of longitudinal and lateral dynamics has the most influence on vehicle performance. Therefore, the planar dynamics need to be considered in the power management strategy. To develop these models an extensive literature survey must be conducted on the development and implementation of artificial neural networks, the energy storage system, strategies for power management, and the modeling of the vehicle's planar dynamics. Therefore, this work aims to contribute to the development of the mathematical model of a vehicle energy storage system suitable for the application conditions, which is often disregarded. This model aims to increase the lifetime of this component and the autonomy of the vehicle. Furthermore, a power management system that encompasses this energy system and acts in real-time is proposed, ensuring the greatest efficiency of the vehicle's propulsion system. (AU)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LOURENCO, MARIA AUGUSTA DE MENEZES; ECKERT, JONY JAVORSKI; SILVA, FABRICIO LEONARDO; MIRANDA, MATHEUS HENRIQUE RODRIGUES; DE ALKMIN E SILVA, LUDMILA CORREA. Uncertainty analysis of vehicle fuel consumption in twin-roller chassis dynamometer experiments and simulation models. MECHANISM AND MACHINE THEORY, v. 180, p. 23-pg., 2023-02-01. (21/14026-8)

SILVA, FABRICIO LEONARDO; ECKERT, JONY J.; MIRANDA, MATHEUS H. R.; DA SILVA, SAMUEL FILGUEIRA; SILVA, LUDMILA C. A.; DEDINI, FRANCO GIUSEPPE. A comparative analysis of optimized gear shifting controls for minimizing fuel consumption and engine emissions using neural networks, fuzzy logic, and rule-based approaches. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 135, p. 20-pg., 2024-06-13. (21/14026-8)

DE CARVALHO, AQUILA CHAGAS; MIRANDA, MATHEUS HENRIQUE RODRIGUES; DE ALKMIN E SILVA, LUDMILA CORREA; SILVA, FABRICIO LEONARDO; KAWANO, RAFAEL ROGORA; ECKERT, JONY JAVORSKI; DEDINI, FRANCO GIUSEPPE. Systematic Opportunity Scan of Energy Recovery Technologies Applied to Trucks with Electric Refrigerated Units. INVENTIONS, v. 9, n. 3, p. 24-pg., 2024-06-01. (21/14026-8)

MIRANDA, MATHEUS H. R.; SILVA, FABRICIO L.; LOURENCO, MARIA A. M.; ECKERT, JONY J.; SILVA, LUDMILA C. A.. Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation. ENERGY, v. 285, p. 15-pg., 2023-10-31. (21/14026-8)

RAMIREZ-QUINTERO, DEYBER ALEXANDER; SILVA, FABRICIO LEONARDO; MIRANDA, MATHEUS H. R.; SILVA, LUDMILA C. A.; ECKERT, JONY J.. Influence of different types of insulating materials, compressors, and refrigerants on the cost and energy performance of refrigerated transport systems. APPLIED THERMAL ENGINEERING, v. 269, p. 16-pg., 2025-06-15. (21/14026-8)