Digitalization is the key to fighting climate change and achieving the objectives of the European Green Deal, while contributing to the green energy transition, energy efficient buildings, sustainable transportation and industrial digital transformation. Digital technologies and AI solutions can deliver operational efficiencies and reduced costs in many industries, enable the development and implementation of smart energy/transport/building systems, increase the connectivity of people and systems. Digital transition and its successful implementation requires strengthening digital skills, at different levels, “from researchers taking a more analytical and empirical approach (looking at digital skills requirements and digital skills gaps), to policymakers and providers of training programmes and skills development initiatives taking a practical approach (launching new programmes)” . In this context, the mission of DiTArtIS project is to strengthen the research and innovation excellence of the beneficiaries, especially of UTC, as well as enhance the digital skills of their staff. This will be done through building teams of excellence to derive new ideas and tackle challenges, to ensure that UTC and twinning partners will sustainably increase scientific excellence and innovation capacity in the field of electromechanical and power systems applications, by integrating digital technologies and AI solutions and techniques.

NEMOSHIP ambition is to contribute to the European Partnership “Zero Emission Waterborne Transport (ZEWT)” objectives by providing new deployable technological solutions needed for all main types of waterborne transport to reach a “net zero emission” by 2050. To reach this goal, NEMOSHIP will: - develop (i) a modular and standardised battery energy storage solution enabling to exploit heterogeneous storage units and (ii) a cloud-based digital platform enabling a data-driven optimal and safe exploitation, - demonstrate these innovations at TRL 7 maturity for hybrid ships and their adaptability for full-electric ships thanks to: (i) a retrofitted hybrid offshore vessel (hybrid diesel/electric after NEMOSHIP BESS installation), (ii) a newly designed hybrid cruise vessel (LNG/electric propulsion) and (iii) a semi-virtual demonstration for two additional full-electric vessels such as ferries and short-sea shipping. All results will be built upon a treasure chest of 18 years of ESS operation data. Thanks to a very ambitious exploitation plan, accompanied by very large dissemination actions, the NEMOSHIP consortium estimates that these innovations will reach the following impacts by 2030: (i) electrification of about 7% of the EU fleet; (ii) generate a potential revenue of €300M thanks to the sales of the NEMOSHIP products and services; (iii) reduce EU maritime GHG emissions by 30% compared to business as usual (BAU) scenario; and (iv) create at least 260 direct jobs (over 1000 indirect). The NEMOSHIP consortium is composed of 11 partners (3 RTO, 1 SME, 7 large companies) and covers the whole value chain, from research-oriented partners and dissemination and exploitation specialists to software developers, energy system designers, integration partners, naval architects and end-users.

To face the climate change, tens of millions of electrified vehicles need to be deployed in the next decade. To meet this challenge, the automotive industry must shift mass production from thermal to electrified vehicles. The challenge is further complicated by electrified vehicles having more components and architectures than thermal vehicles. Realizing this paradigm shift is only possible if there are innovative methods to significantly reduce their development and testing time. The main goal of PANDA is to provide a unified organisation of digital models to seamlessly integrate virtual and real testing of all types of electrified vehicles and their components. The complexity of developing electrified vehicles becomes manageable by delivering a modular simulation framework. Development partners can share models (in open or in black-box form), avoiding sensitive IP issues and greatly increasing the development flexibility. The proposed method will enable 1) an easy reuse of models for different development tasks, 2) a replacement of real tests by virtual tests and 3) real-time testing on vehicle level. This method will be integrated in a multi-power open platform based on existing industrial software, enabling Stand-Alone or Cloud Computing. The method will be validated using two existing vehicles (a BEV and a FCV). Also, real and virtual tests of the integrated electrical subsystems of an innovative P-HEV will be performed. PANDA will reduce the time-to-market of electrified vehicles by 20%, by harmonizing the interaction between the models. In addition, the seamless integration will give developers access to other subsystem models, which will decrease the correlation efforts on components by 20%. The open platform will 1) make it easier for OEMs, suppliers, SMEs and research institutions to interact and 2) enable a fair competition. These innovations will make the European market more flexible, more open to innovation and ultimately more competitive.

IMOCO4.E targets to provide vertically distributed edge-to-cloud intelligence for machines, robots and other human-in-the-loop cyber-physical systems having actively controlled moving elements. They face ever-growing requirements on long-term energy efficiency, size, motion speed, precision, adaptability, self-diagnostic, secure connectivity or new human-cognitive features. IMOCO4.E strives to perceive and understand complex machines and robots. The two main pillars of the project are digital twins and AI principles (machine/deep learning). These pillars build on the I-MECH reference framework and methodology, by adding new tools to layer 3 that delivers an intelligible view on the system, from the initial design throughout its entire life cycle. For effective employment, completely new demands are created on the Edge layers (Layer 1) of the motion control systems (including variable speed drives and smart sensors) which cannot be routinely handled via available commercial products. Based on this, the subsequent mission is to bring adequate edge intelligence into the Instrumentation and Control Layers, to analyse and process machine data at the appropriate levels of the feedback control loops and to synchronise the digital twins with either simulated or real-time physical world. At all levels, AI techniques are employable. Summing up, IMOCO4.E strives to deliver a reference platform consisting of AI and digital twin toolchains and a set of mating building blocks for resilient manufacturing applications. The optimal energy efficient performance and easy (re)configurability, traceability and cyber-security are crucial. The IMOCO4.E reference platform benefits will be directly verified in applications for semicon, packaging, industrial robotics and healthcare. Additionally, the project demonstrates the results in other generic “motion-control-centred” domains. The project outputs will affect the entire value chain of the production automation and application markets.