This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).

Automated Photovoltaic (PV) Panels are accessible and reliable engines for harnessing energy delivered from the Sun rays. In this paper, we design an efficient solar tracking device composed of an Arduino UNO board, two stepper motors, a pair of specialized L298N circuits and an optocoupler. We also present a novel approach for orienting the solar panel towards the Sun based on the cast-shadow effect as well as a low-cost and sensorless solution for providing an automated variant of the PV panel. The implementation of a dedicated blocking mechanism for the stepper motors managed to reduce the overall power consumption of the system by 86.93 ^% and thus eliminated the device's dependability from the AC network. The experimental results show a 45.21% voltage, 49.71 ^% current and 58.82% power increase over the static PV panel by using monocrystalline solar cells.

This paper introduces a novel mathematical approach to significantly enhance dual-axis solar trackers’ Solar Reliability Factor (SRF) by developing and implementing an advanced Online Built-In Self-Test (OBIST) architecture. This innovative architecture is designed to efficiently address and correct single bit-flip errors within the system’s microcontroller unit (MCU), a common control unit in contemporary solar trackers. By employing an improved diagnostic scheme based on extended Hamming codes, our OBIST architecture identifies and autonomously corrects all detected single bit-flip errors, reducing the fault coverage to 0%. This capability marks a significant advancement in the field, directly contributing to a substantial increase in the SRF by 47.48%. The study meticulously analyzes the potential fault domain influenced by environmental factors such as prolonged sunlight exposure and varying weather conditions, which are critical in the regular operation of solar trackers. Furthermore, we introduce a probabilistic model for defining and addressing stuck-at-faults, enhancing the system’s overall reliability. The successful application of novel fault coverage-aware metrics demonstrates the OBIST architecture’s effectiveness in improving solar tracker reliability, significantly contributing to the photovoltaic (PV) systems domain. This research presents a groundbreaking approach to enhancing solar tracker reliability and sets the stage for future advancements in the maintenance and efficiency of renewable energy systems.

This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware metrics greatly change the test and reliability factor curve of solar tracking systems, achieving significantly reduced calculation steps and computation time.

This paper presents an improved mathematical model for calculating the solar test factor (STF) and solar reliability factor (SRF) of a photovoltaic (PV) automated equipment. By employing a unified metrics system and a combined testing suite encompassing various energy-efficient testing techniques, the aim of this paper is to determine a general fault coverage and improve the global SRF of a closed-loop dual-axis solar tracking system. Accelerated testing coupled with reliability analysis are essential tools for assessing the performance of modern solar tracking devices since PV system malfunctioning is directly connected to economic loss, which is an important aspect for the solar energy domain. The experimental results show that the unified metrics system is potentially suitable for assessing the reliability evaluation of many types of solar tracking systems. Additionally, the proposed combined testing platform proves efficient regarding fault coverage (overall coverage of 66.35% for all test scenarios), test time (an average of 275 min for 2864 test cycles), and power consumption (zero costs regarding electricity consumption for all considered test cases) points of view.