An IoV-Based Real-Time Telemetry and Monitoring System for Electric Racing Vehicles: Design, Implementation, and Field Validation Andrés Pérez-González, Arley F. Villa-Salazar, Ingry N. Gomez-Miranda, Juan D. Velásquez-Gómez, Andres F. Romero-Maya, et al. Vehicles, 2025 The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring.
Enhancing Multi-Objective Performance: Optimizing the Efficiency of an Electric Racing Vehicle Ingry N. Gomez-Miranda, Arley. F. Villa-Salazar, Andrés Pérez-González, Andres. F. Romero-Maya, Juan. D. Velásquez-Gómez, et al. World Electric Vehicle Journal, 2025 The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured 2×3×3 factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility.
Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach A. F. Villa-Salazar, I. N. Gomez-Miranda, A. F. Romero-Maya, J. D. Velásquez-Gómez, K. Lemmel-Vélez World Electric Vehicle Journal, 2024 The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.
Reducing Dynamic Energy in Networks on Chip Arley Villa Salazar, Gustavo Patino 2019 Congreso Internacional De Innovacion Y Tendencias En Ingenieria Coniiti 2019 Conference Proceedings, 2019 Networks on Chip (NoCs) have been recognized as a viable solution to solve the Systems on Chip (SoC) design challenges, providing a scalable and efficient communication structure in multicore systems. However, this solution is inefficient in terms of energy consumption. In order to minimize such consumption, it is essential to design an efficient routing algorithm that minimizes energy consumption while maximizing throughput. This paper proposes an energy aware routing algorithm (called EA-NoC) that optimizes the dynamic energy by determining the optimal route between source and destination, avoiding unnecessary energy consumption. The routing algorithms were simulated on a 2D-mesh topology, and compared to the State-of-the-Art routing algorithms implemented in the Noxim simulator. The experimental results show that the proposed algorithm outperforms existing algorithms in literature (28% better on average, in terms of dynamic power).
Machine-to-machine communication for intelligent transport systems Yudy Andrea Quintero, Arley Villa Salazar, Gustavo Patino 2016 IEEE Colombian Conference on Communications and Computing Colcom 2016 Conference Proceedings, 2016 This paper describes an algorithm implementing a machine-to-machine communication for sending and receiving information between an On-Board Unit (OBU) in a massive public transportation vehicle and an information system managed by the transportation company that operates the vehicles. The communications interface of this system was implemented by using WebSockets and JSON messages. The prototype was tested and found to be functional, complying the normativity of the Public Transportation of Medellin.
