In power electronics, the traditional design approach of power converters involves a range of power semiconductor devices with different ratings, optimized to operate at different conditions, where different suitable ancillary circuitry and power circuit topologies are also required. This dispersion in power devices and circuits leads to significant engineering efforts, the inability to take full advantage from scale economies to reduce costs, and the inability to concentrate efforts to improve performance. In the electric vehicle (EV) market, this is translated to a lack of standardization on the EV power conversion system designs across the different models and types of vehicles available, meaning that nowadays EV OEMs invest billions of euros to develop their own solutions. SCAPE aims at achieving three main objectives: i) propose a standardisable, modular, and scalable approach, based on multilevel technology, for the design of the EV power conversion systems ii) develop highly-compact and integrated building-block implementation. iii) propose intelligent modulation and control strategies, online diagnosis, and digital twin for predictive maintenance with machine learning. Reaching these objectives will enable reducing the cost of the EV power electronics thanks to scale economies, improving its performance features (reliability, efficiency, power density, etc.), and enabling advanced functionalities. This will allow satisfying the user’s needs, increase the acceptance and affordability of zero-emission vehicles, reduce green-house gasses emission, and enable a full-market penetration of the EV. Having this approach adopted by EU automotive manufacturers will allow creating a cost-efficient production chain in the EU based on economies of scale and advanced integration technologies, as a competitive advantage against other manufacturers.

Intelligent Reliability 4.0 (iRel40) has the ultimate goal of improving reliability for electronic components and systems by reducing failure rates along the entire value chain. Trend for system integration, especially for heterogeneous integration, is miniaturization. Thus, reliability becomes an increasing challenge on device and system level and faces exceptional requirements for future complex applications. Applications require customer acceptance and satisfaction at acceptable cost. Reliability must be guaranteed when using systems in new and critical environments. In iRel40, 79 partners from 14 countries collaborate in 6 technical work packages along the value chain. WP1 focuses on specifications and requirements. WP2 and WP3 focus on modelling, simulation, materials and interfaces based on test vehicles. WP4 applies the test vehicle knowledge to industrial pilots related to production. WP5 applies the knowledge to testing. WP6 focuses on application use cases applying the industrial pilots. We assess and validate the iRel40 results. Reliable electronic components and systems are developed faster and new processes are transferred to production with higher speed. Crucial insight gained by Physics of Failure and AI methods will push overall quality levels and reliability. iRel40 results will strengthen production along the value chain and support sustainable success of Electronic Components and Systems investment in Europe. By collaboration between academy, industry and knowledge institutes on this challenging topic of reliability, the project secures more than 25.000 jobs in the 25 participating production and testing sites in Europe. The project supports new applications and reliable chips push applications in energy efficiency, e-mobility, autonomous driving and IoT. This unique project brings, for the first time ever, world-leading reliability experts and European manufacturing expertise together to generate a sustainable pan-European reliability community.

The project idea is focusing on AI-augmented automation supporting modeling, coding, testing, and monitoring as part of a continuous development in Cyber-Physical Systems (CPSs). The growing complexity of CPS poses several challenges throughout all software development and analysis phases, but also during their usage and maintenance. Many leading companies have started envisaging the automation of tomorrow to be brought about by Artificial Intelligence (AI) tech. While the number of companies that invest significant resources in software development is constantly increasing, the use of AI in the development and design techniques is still immature. The project targets the development of a model-based framework to support teams during the automated continuous development of CPSs by means of integrated AI-augmented solutions. The overall AIDOaRT infrastructure will work with existing data sources, including traditional IT monitoring, log events, along with software models and measurements. The infrastructure is intended to operate within the DevOps process combining software development and information technology (IT) operations. Moreover, AI technological innovations have to ensure that systems are designed responsibly and contribute to our trust in their behaviour (i.e., requiring both accountability and explainability). AIDOaRT aims to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRT framework to analyze event streams in real-time and historical data, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection.

European industry faces stiff competition on the global arena. Electronic Components and Systems become more and more complex, thus calling for modern engineering practices to be applied in order to better tackle both productivity and quality. Model-based technologies promise significant productivity gains, which have already been proven in several studies and applications. However, these technologies still need more enhancements to scale up for real-life industrial projects and to provide more benefits in different contexts. The ultimate objective of improving productivity, while reducing costs and ensuring quality in development, integration and maintenance, can be achieved by using techniques integrating seamlessly design time and runtime aspects. Industrial scale system models, which are usually multi-disciplinary, multi-teams and serving to several product lines have to be be exploited at runtime, e.g. by advanced tracing and monitoring, thus boosting the overall quality of the final system and providing lessons-learnt for future product generations. MegaM@Rt brings model-based engineering to the next level in order to help European industry reducing development and maintenance costs while reinforcing both productivity and quality. To achieve that, MegaM@Rt will create a framework incorporating methods and tools for continuous development and runtime validation to significantly improve productivity, quality and predictability of large and complex industrial systems. MegaM@Rt addresses the scalability challenges with advanced megamodelling and traceability approaches, while runtime aspects will be tackled via so-called “models@runtime”, online testing and execution traces analysis. MegaM@Rt brings together a strong international consortium involving experts from France, Spain, Italy and Finland. The partners cover the whole value chain from research organizations to tool providers, including 9 end-users with large industrial case studies for results validation.

Global environmental issues, social inequality and geopolitical changes will pose numerous problems for our society in the future. To face these new challenges and deal with them, there is a need to understand and appropriately utilize new digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), robotics and biotechnologies. A-IQ Ready proposes cutting-edge quantum sensing, edge continuum orchestration of AI and distributed collaborative intelligence technologies to implement the vision of intelligent and autonomous ECS for the digital age. Quantum magnetic flux and gyro sensors enable highest sensitivity and accuracy without any need for calibration, offer unmatched properties when used in combination with a magnetic field map. Such a localization system will enhance the timing and accuracy of the autonomous agents and will reduce false alarms or misinformation by means of AI and multi-agent system concepts. As a priority, the communication guidance and decision making of groups of agents need to be based on cutting-edge technologies. Edge continuum orchestration of AI will allow decentralizing the development of applications, while ensuring an optimal use of the available resources. Combined with the quantum sensors, the edge continuum will be equipped with innovative, multi-physical capabilities to sense the environment, generating “slim” but accurate measurements. Distributed intelligence will enable emergent behavior and massive collaboration of multiple agents towards a common goal. By exploring the synergies of these cutting-edge technologies through civil safety and security, digital health, smart logistics for supply chains and propulsion use cases, A-IQ Ready will provide the basis for the digital society in Europe based on values, moving towards the ideal of Society 5.0.