Data acquisition system for solar electric vehicles

Abstract

Increasing complexities in vehicle systems, compounded with hidden and hard to reach automotive components makes it challenging to identify faults and collect necessary data during operation of the vehicle. Sensors are critical components in a modern automobile. Sensors collect each and every data of the process running inside the hood. Several type of sensors like accelerometer, thermal camera module, motion sensor, pulse rate sensors, etc. collect minute level information thereby transferring to the microcontroller unit which is then transferred through IoT modules to the servers. Data acquisition is basically collection of all relevant data. In reference to automotive applications, during travelling we cannot determine that everything is up to the mark or not and we cannot even detect any problem without the help of any technical expert. Data acquisition is very critical in automobiles. Analyses of collected after the driving sessions enables the driver to improve driving behavior and take better decisions in real time during driving for enhanced safety. It helps in preventive maintenance of key components. Machine learning algorithms when applied to sufficiently large data sets of vehicles collected during operation can give fruitful insights for better design of systems. Special IoT modules like ZigBee is being used to transfer data to the nearest receiver at a faster rate and frequency. Photodiode condition and battery management in a solar electric vehicle are being traced to predict the lifespan and health monitoring of the vehicle. This will help us to know about the vehicle in a much detailed manner and hence analyse the data received so that we can be aware of the faulty situation in cars.