One problem in the development stage of driverless cars on public roads is how to monitor the condition of the vehicle that is affected by different weather and environment, including dry, snowy or heavy rain conditions, which are sometimes difficult to predict and require monitoring systems that should be real-time and accurate. For this reason, an internet of things-based car condition monitoring system is needed to be developed. The development covers three essential parts, which include the development of hardware and digital communication modules, web server development and data analysis and mobile application development for monitoring. The purpose of this study is to design a hardware module needed for car vehicle condition monitoring system; a prototype completed with the onboard diagnostic module and embedded system that will send data to the server in real time. The system was successfully designed and developed using ELM327 and ESP32 with SIM808 for communication module. The benefits of this research are as a tool for asset management of car vehicles both for personal and corporate needs. This study also supports the development of driverless cars that require real-time data input between the vehicle and the server.
A new method for reducing input-referred thermal noise of dynamic comparator circuit without increasing load capacitance as commonly used in the conventional method is proposed. An implementation circuit with selectable low-noise mode operation is presented, which enable both low-noise mode and standard mode operation by a single circuit. Simulation in 180 nm CMOS process technology shows that the proposed new method and circuit topology can achieve up to 90% increase in gain of comparator first stage, resulting in up to 40% decrease in input-referred thermal noise voltage, compared with a conventional circuit with similar load capacitance. The proposed circuit is also able to operate with similar performance as the conventional circuit when low-noise mode operation is not necessary.
The battery monitoring system (BMoS) is crucial to monitor the condition of the battery in supplying and absorbing the energy when operating and simultaneously determine the optimal limits for achieving long battery life. All of this can be done by measuring the battery parameters and increasing the state of charge (SoC) and the state of health (SoH) of the battery. The battery dataset from NASA is used for evaluation. In this work, the gradient vector is employed to obtain the trend of the energy supply pattern from the battery. In addition, a support vector machine (SVM) is adopted for an accurate battery accuracy index. This is in line with the use of polynomial regression; hence, points V1 and V2 are obtained as the boundaries of the normal-usage phase. Furthermore, testing of the time length distribution is also carried out on the length of time the battery was successfully extracted from the classification. All these stages can be used to calculate the rate of battery degradation during use so that this strategy can be applied in real situations by continuously comparing values. In this case, using the voltage gradient, SVM method, and the suggested polynomial regression, MAPE (%), MAE, and RMSE can be obtained against the battery value graph with values of 0.3% , 0.0106, and 0.0136, respectively. With this error value, the dynamics of the SoC value of the battery can be obtained, and the SoH problem can be resolved with a shorter usage time by avoiding the voltage-drop phase.
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