Diseño y desarrollo de una aplicación móvil inteligente para el diagnóstico y mantenimiento vehicular mediante el análisis de códigos OBD-II, con servicios de inteligencia artificial para la detección temprana de fallas y la optimización del mantenimiento preventivo.

Abstract

The degree project consists of implementing a mobile application for detecting, reading, and displaying diagnostic trouble codes (DTCs) using OBD-II technology. The mobile application uses the ESP32 microcontroller and an OBD-II adapter; this integration enables communication with the vehicle's ECU, reading of codes (DTCs), and their deletion. The information collected is stored in Firebase and processed with a backend developed in Flask, deployed in the cloud. An artificial intelligence API is incorporated for DTC code analysis and vehicle diagnostics. This solution is aimed at drivers and technicians as an intuitive tool that enables diagnostics and recommendations for preventive maintenance. This proves the viability of integrating hardware and software in the automotive field.