The paper discusses developing a new optional course at Tallinn University of Technology (TalTech), Estonia inspired by international competition focused on autonomous driving algorithms. As a part of the curriculum, the course was designed to bridge theoretical knowledge with practical skills, fostering interdisciplinary understanding and preparing students for the competition. The course structure combines traditional and active learning methods, emphasizing collaborative group work and independent problem-solving. The course outcomes cover key mechatronics concepts, including control algorithms, feedback systems, and image processing. Students work with a custom mobile platform designed for the course. Feedback indicates positive evaluations of the course content and structure. The course successfully aids students in recognizing interdisciplinary connections of educational mobile robotic platforms within mechatronics, simplifying navigation through the learning process. Challenges identified include the course duration and the fragility of platform components, prompting suggestions for more examples, longer sessions, and platform improvements. The paper concludes that the course achieves its goals, guiding students in recognizing interdisciplinary connections and cultivating a holistic understanding of mechatronics.

Citation: Ibrahim, M.; Rassõlkin, A.; Vaimann, T.; Kallaste, A.; Zakis, J.; Hyunh, V.K.; Pomarnacki, R. Digital Twin as a Virtual Sensor for Wind Turbine Applications. Energies 2023, 16, 6246. https://doi.org/10.3390/ en16176246 Academic Editors: Dan-Cristian Popa and Emil Cazacu Abstract: Digital twins (DTs) have been implemented in various applications, including wind turbine generators (WTGs). They are used to create virtual replicas of physical turbines, which can be used to monitor and optimize their performance. By simulating the behavior of physical turbines in real time, DTs enable operators to predict potential failures and optimize maintenance schedules, resulting in increased reliability, safety, and efficiency. WTGs rely on accurate wind speed measurements for safe and efficient operation. However, physical wind speed sensors are prone to inaccuracies and failures due to environmental factors or inherent issues, resulting in partial or missing measurements that can affect the turbine's performance. This paper proposes a DT-based sensing methodology to overcome these limitations by augmenting the physical sensor platform with virtual sensor arrays. A test bench of a direct drive WTG based on a permanent magnet synchronous generator (PMSG) was prepared, and its mathematical model was derived. MATLAB/Simulink was used to develop the WTG virtual model based on its mathematical model. A data acquisition system (DAS) equipped with an ActiveX server was used to facilitate real-time data exchange between the virtual and physical models. The virtual sensor was then validated and tuned using real sensory data from the physical turbine model. The results from the developed DT model showed the power of the DT as a virtual sensor in estimating wind speed according to the generated power.

Abstract The demand for energy is a relevant topic in the field of science and engineering, which has been discussed throughout the last years due to the challenges of climate change and environmental concerns around the world. Currently, electric vehicles (EVs) offer a source of mobility that emphasises the use of energy storage devices to reduce CO2 emissions. The growing development of advanced data analytics and the Internet of Things has driven the implementation of the Digital Twin (DT), all to improve efficiency in the build, design and operation of the system. Regarding the components of EVs, the batteries are considered as the most expensive elements to analyse according to the State of Health and the State of Charge, which lead to implement the most optimal models, along with a DT for battery systems. The present article provides a literature review about the current development trends of EVs' energy storage technologies, with their corresponding battery systems, which gives an overview to understand different type of models and to identify future challenges in the industrial sector. Additionally, a solid explanation of the DT focussed on battery systems for EVs is discussed, highlighting some study cases, characteristics, and technological opportunities.

In the electric automotive industry, manufacturers usually do not declare the electrical parameters of electric vehicle (EV) motors. In advanced control systems, accurate knowledge of motor parameters is essential in order to achieve high dynamic performance. Conventional tests for parameter estimation might be risky not only to the motor windings but also to the measurement devices. In this research study, a dynamic analytical approach is used to estimate the electrical parameters of a three-phase permanent magnet synchronous motor PMSM used in an autonomous electric vehicle (AEV). A detailed d-q mathematical model of the PMSM model was presented. No-load/on load tests were experimentally performed on the motor. The motor simulation dynamic model was built using MATLAB/Simulink. Ant lion optimization (ALO) search algorithm was implemented within the motor simulation model to search proper parameters that achieve equality between experimental and simulated d-q current values. The obtained results showed high agreement between simulation and experimental results.