The interest in electric traction has reached a very high level in recent decades; there is no doubt that electric vehicles have become among the main means of transport and will be the first choice in the future, but to dominate the market, a lot of research efforts are still devoted to this purpose. Electric machines are crucial components of electric vehicle powertrains. The bulk of traction drive systems have converged in recent years toward having some sort of permanent magnet machines because there is a growing trend toward enhancing the power density and efficiency of traction machines, resulting in unique designs and refinements to fundamental machine topologies, as well as the introduction of new machine classes. This paper presents the technological aspect of the different components of the electric powertrain and highlights the important information on the electric vehicle’s architecture. It focuses on a multi-criteria comparison of different electric motors utilized in the electric traction system to give a clear vision to allow choosing the adequate electrical motor for the desired application. The proposed comparative analysis shows that the induction motor better meets the major necessities of the electric powertrain, whereas the permanent magnet synchronous motor is nonetheless the most used by electric vehicle manufacturers.

In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

In the era of rapidly advancing technological landscapes, the fusion of cutting-edge sensor technology, innovations in electric vehicles, and artificial intelligence are driving the growth of diagnosis and prognostics health management techniques tailored for electric vehicle applications. Such a system plays a pivotal role in ensuring the reliable, efficient, and safe operation of complex engineering systems like electric vehicles by facilitating fault detection, diagnosis, recovery, and prognostics. The main challenge in the field of diagnosis, prognostics and health management is to pinpoint the optimal methods and areas of application in electric vehicle to extract the most pertinent information. In this paper, a novel intelligent digital twin-based architecture for intelligent diagnosis, prognostics and health management of electric vehicle powertrains is proposed, it aims to address the aforementioned challenges. The proposed architecture is based on a multilayered approach that integrates multiple intelligent digital twin models at different abstraction levels and leverages transfer learning strategies to enable individualization and servitization of the proposed system. Furthermore, pivotal technologies enabling this approach are delved into, along with a discussion of inherent challenges and emerging opportunities. The findings underscore the benefit of using distributed computing and real-time prognostics and health management information to enhance the reliability, efficiency, and availability of the electric vehicles, concurrently ensuring driver safety. The proposal is enriched with a case study that illustrates the practical application of our architecture in a real-world electric vehicle monitoring system scenario, showcasing its potential for customization and service-oriented features, and provides foresight into vehicle parameter trends.

The presented article aims to design an educational test bench setup for smart grids and renewable energies with multiple features and techniques used in a microgrid. The test bench is designed for students, laboratory engineers, and researchers, which enables electrical microgrid system studies and testing of new, advanced control algorithms to optimize the energy efficiency. The idea behind this work is to design hybrid energy sources, such as wind power, solar photovoltaic power, hydroelectric power, hydrogen energy, and different types of energy storage systems such as batteries, pumped storage, and flywheel, integrating different electrical loads. The user can visualize the state of the components of each emulated scenario through an open-source software that interacts and communicates using OPC Unified Architecture protocol. The researchers can test and validate new solutions to manage the energy behavior in the grid using machine learning and optimization algorithms integrated in the software in form of blocks that can be modified and improved, and then simulate the results. A model-based system of engineering is provided, which describes the different requirements and case studies of the designed test bench, respecting the open-source software and the frugal innovation features in which there is use of low-cost hardware and open-source software. The users obtain the opportunity to add new sources and new loads, change software platforms, and communicate with other simulators and equipment. The students can understand the different features of smart grids, such as defect classification, energy forecasting, energy optimization, and basics of production, transmission, and consumption.