Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics.

Accurate photovoltaic (PV) panel characterization under real environmental conditions is critical for optimizing renewable energy systems. However, commercial I-V curve tracers are often expensive, inflexible, and unsuitable for rapid field deployment. This paper presents a low-cost, Arduino-based data acquisition (DAQ) system capable of real-time I-V curve tracing using automated resistive load switching in a single microcontroller. The system integrates current and voltage measurements with a relay-controlled resistor bank, enabling sequential load variation without manual intervention. A total of 23 unique resistor combinations were programmed, providing fast and granular coverage of the PV panel’s operating range. Experimental validation was conducted on a commercial PV panel under natural sunlight conditions. The system total data acquisition time was 45 seconds and achieved high agreement with a single-diode PV model, with a mean absolute percentage error (MAPE) of 4.40% under standard conditions (manufacturer’s data). When re-optimized with field-acquired data, the model’s accuracy improved significantly, reducing the MAPE from 18.23% to 7.06% under variable irradiance. This open-source solution provides a cost-effective, portable, and scalable alternative to commercial tracers, with potential for integration into IoT-enabled PV monitoring platforms. Its design supports research, education, and deployment in low-resource environments where accessible renewable energy diagnostics are essential.