| Issue |
E3S Web Conf.
Volume 706, 2026
3rd International Conference on Environment, Green Technology, and Digital Society (INTERCONNECTS 2025)
|
|
|---|---|---|
| Article Number | 02009 | |
| Number of page(s) | 12 | |
| Section | Engineering and Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202670602009 | |
| Published online | 21 April 2026 | |
Development and Validation of a Low Cost Data Acquisition System for Automotive Engine Sensors
Universitas Negeri Padang, Padang, Indonesia
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
This research presents the development and validation of an ESP32-based data acquisition system for an automotive engine stand in vocational engineering education. Conventional use of multimeters and oscilloscopes is restricted to single-channel, non-synchronised measurements, limiting students’ ability to analyse dynamic engine-sensor behaviour. The proposed system interfaces with eight automotive sensors namely MAP, TPS, IAT, ECT, O₂, CKP, CMP, and knock, through dedicated signal-conditioning circuits and an ESP32 microcontroller. Firmware written in C/C++ using the Arduino IDE enables multi-channel sampling, real-time clock time-stamping, Bluetooth communication, and simultaneous logging to microSD and PLX-DAQ/Excel on a personal computer, together with Android-based monitoring. System performance was evaluated on a K3-VE engine stand by comparing measured voltages and waveforms with digital-multimeter and oscilloscope readings at idle, 2000, and 3000 rpm. For TPS, MAP, ECT, and IAT, the mean absolute percentage error generally ranges from 0% to about 3%, while the O₂ sensor shows larger errors of approximately 1–8% but still reproduces the correct operating range and trends. CKP, CMP, and knock channels successfully capture the essential digital and filtered waveforms for timing and knock demonstrations. These findings indicate that the platform provides sufficiently accurate, real-time multi-sensor monitoring for automotive diagnostics training and offers a scalable basis for future IoT-oriented instructional tools.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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