AI4SOFTENG represents an excellent chemistry of 13 Partners including experts and practitioners in Model-Drive Software Engineering and AI Engineering (particularly LLMs) from across Europe and USA with four use-cases from Software Development, Healthcare, Cyber-Physical System involving multi-architectural and resource-constrained systems and Next Generation Battery Management in EVs. The AI4SOFENG mission is to significantly contribute to the transformation of agile software development into a sector turbocharged for design-by-contact high efficiency, reliability, and security-privacy compliant delivery. This is to exploit advanced AI-powered software development support to deliver high-quality reliable fast time-to-market solutions that are socially responsible, scalable, sustainable and lend themselves to audible ethical and regulatory compliance. AI4SOFTENG aims deliver AI-powered support to software developers to help reduce worker stress past the pain-points in agile SW development pipelines and higher productivity thus enhanced job satisfaction and creativity. Accordingly, the project will develop, deliver and validate, in 4 use-cases, a platform comprising a set of tools including AISysDev and Test Automation Tool to support the software lifecycle end-to-end for both functional and non-functional deliverables by supporting a host of tasks such as code generation, cross-compilation advanced debugging and dynamic slicing, adaptation, security-privacy by design, reliability, energy efficiency and adaptation as well as project management, process quality and maintenance support through data-driven insights for informed design decision-making and strategic software development planning Additionally with a commitment to advance user-centred design, it will provide structured design in advanced prompt engineering to support software developers team to best exploit to capabilities of the platform.

Heavy-duty vehicles account for about 25% of EU road transport CO2 emissions and about 6% of total EU emissions. In line with the Paris Agreement and Green Deal targets, Regulation (EU) 2019/1242 setting CO2 emission standards for HDVs (from August 14, 2019) forces the transition to a seamless integration of zero-emission vehicles into fleets. In line with the European 2050 goals ESCALATE aims to demonstrate high-efficiency zHDV powertrains (up to 10% increase) for long-haul applications that will provide a range of 800 km without refueling/recharging and cover at least 500 km average daily operation (6+ months) in real conditions. ESCALATE will achieve this by following modularity and scalability approach starting from the β-level of hardware and software innovations and aiming to reach the γ-level in the first sprint and eventually the δ-level at the project end through its 2 sprint-V-cycle. ESCALATE is built on the novel concepts around 3 main innovation areas, which are: i) Standardized well-designed, cost effective modular and scalable multi-powertrain components; ii) Fast Fueling & Grid-friendly charging solutions; and iii) Digital Twin (DT) & AI-based management tools considering capacity, availability, speed, and nature of the charging infrastructures as well as the fleet structures. Throughout the project lifetime, 5 pilots, 5 DTs and 5 case studies on TCO (with the target of 10% reduction), together with their environmental performance via TranSensusLCA will be performed. The ultimate goal is to develop well-designed modular building blocks with a TRL7/8 based on business model innovations used for 3 types of zHDVs {b-HDV,f-HDV,r-HDV}. Furthermore, 3 white papers will be produced, one of which will contribute defining the pathway for reducing well-to-wheel GHG emissions from HDVs based on results and policy assessments.

Mobility electrification plays a critical role in the economy decarbonisation, and we are on the edge of an industrial revolution linked to the massive deployment of the electric vehicle (EV). Their technologies readiness level has significantly increased, and the EV can now replace the thermal vehicle in terms of service provided, supporting the EU decarbonisation effort. Besides the reduction of critical material, and decrease of cost, optimising the lifetime of the EV components is essential to ease their adoption, especially the powertrain sub-components that have the major impact on EV cost and CO2 emissions. A new-generation of diagnostic and prognostic systems for the powertrain will be a game changer to ensure EV adoption, because they will estimate its degradation, anticipate failures, and ease reparability thus extending its lifespan. With significant improvement of sensors, complex modelling and data processing methods such as Artificial Intelligence (AI), predictive maintenance (PdM) has gained a lot of interest in different fields. Development of PdM methods for the sub-components of the EV powertrain (battery, fuel cell, e-motor, power electronics) is at the heart of TEAMING. Thanks to international staff exchanges, TEAMING will significantly improve the different facets of the PdM solution: sensors, modelling, Digital Twins, adapted AI, and Physics-Informed Machine Learning methods are at the centre of the studies and present a major potential in term of innovation. TEAMING will advance PdM system to better diagnose the internal physical phenomena of the different EV powertrain components and optimise their performance, lifetime, safety, and reliability.”

Data and AI-driven smart technologies, with an emphasis on (i) the connection between the urban space and the industrial space, (ii) sustainable living and (ii) sustainable industrial production, hold the transformative potential to enhance the sustainability and climate resilience across EU, in private and work spaces. Assuming this, NexTArc is devoted to augmenting the adoption of Trustworthy edge AI and IoT across 3 complementary and interrelated application domains, organised as Use Cases (UC): SLN - Smart, sustainable and Liveable Neighbourhood in Urban Spaces; STI - Smart, sustainable and transparent industrial Spaces; and TEM - Trustworthy and Eco-friendly Multimodal Connectivity of Urban and Industrial Spaces through people and freight mobility, incl. the inter and intra-mobility. Building on this vision, NexTArc aims to promote the cross-fertilization of ideas among a broad spectrum of stakeholders, 38 partners in 10 countries, integrated over a four-fold Innovation Module (IM) approach: i) cyber-resilience on chip; ii) low-power embedded AI; iii) improved computation and dependability covering the high-performance needs; iv) holistic solution stack to enable trustworthy services, which resonate with the EU Chips Act, etc. NexTArc has identified 6 Specific Objectives: 1) Driving adoption of AI while enhancing connectivity preparedness; 2) Targeting a 40% increase in data transmission rates and a 30% reduction in energy use during data processes, while ensuring robust architectural resilience; 3) Fortifying cyber-physical security with an aim for full-compliance with EU’s Chip and Cybersecurity act; 4) Realising open HW/SW to ensure designs that are secure, safe, private, and accountable; 5) Proactively adapting to the dynamic landscape of open-source innovations and key industry standards; 6) Orchestrating 4 IM, unveiling 15 Key Innovations to develop the solutions that are needed for Europe to take the technological lead towards a sustainable society.

The project OPEVA aims for innovation on aggregating information from the vehicle, not only from the battery but also from other internal sensors and behaviours, to create a model of performance and consumption specific to the individual vehicle and its driver (TD1). It aims to optimize the individual driving episode using the out-vehicle data such as state of the road, weather, charging station location and occupancy etc. that are collated from the back-end systems (TD2). OPEVA will further address the challenges associated with the communication between the vehicle and the infrastructure to gather data from the back-end systems (TD3). It aims for innovation in the use of recharging stations and related applications (TD4). It further aims to achieve better understanding on what the battery and its constituent cells are really doing during real world use for an improved battery management system (TD5). Finally, TD6 covers the driver-oriented human factors for optimizing the electrical vehicle usage. The TDs from the most deeply embedded in the vehicle to its support in the cloud, which need to interwork in an optimal fashion to deliver in one decade a better level of systemic optimisation for personal mobility that took ten decades to achieve with fossil fuels. On the other hand, economic factors (N-TD1), legal and ethical aspects (N-TD2), EV related development by the human (N-TD3), and societal and environmental factors (N-TD4) will be taken into consideration in the OPEVA methods for a higher acceptance and the awareness of the society regarding the these developments.