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Samsung's battery and renewable energy division plays a pivotal role in the electric vehicle (EV) market. Holding a 5% market share, the company exemplifies the convergence of the automotive and technology industries by supplying vehicle batteries alongside other electronic components. Its expertise lies in the production of lithium-ion batteries, celebrated for their exceptional energy density, performance, and reliability, making them a preferred choice for modern electric vehicles. Through continuous innovation, Samsung remains a key contributor to the advancement of sustainable transportation technologies.

This deliverable reports the selection of the optimum power devices for implementing the SCAPE high-voltage switching cells, after a literature review and commercial availability check. In addition to suitable electrical characteristics, the selection of candidates considered the suitability and availability of bare-die components for their subsequent chip embedding process. Two SiC MOSFET references have been selected and samples have been obtained for an initial test campaign (GeneSiC G4R12MT07. 750V – 12 mΩ and Wolfspeed CPM3-0650-0015A. 650V – 15 mΩ). For the development of the low-voltage switching cells of the auxiliary SCAPE converters, GaN HEMTs from EPC will be selected. The deliverable also includes a prospective and literature review about power device emerging technologies. 

The study introduces a proof-of-concept self-adaptive neural network model predictive control (SA-NNMPC) system, which uses a neural network as main component of the prediction model, for the anti-jerk control of electric vehicles. Through the adaptation mechanism of the network and cost function weights during vehicle operation, which is activated when the plant behaves significantly differently from its digital twin, the SA-NNMPC architecture adjusts to the progressive vehicle aging, or to the replacement of hardware parts, which is expected to be an important feature of next-generation vehicles. Validation tests and simulations show that the neural network accurately replicates the drivetrain dynamics of the considered electric vehicle, and, for nominal conditions, already leads to a performance improvement of the NNMPC implementation – which can run in real-time on a rapid control prototyping unit – with respect to a benchmarking nonlinear model predictive anti-jerk controller. Moreover, the preliminary simulation results confirm the potential of the proposed architecture in terms of: i) adaptability to operating conditions not covered in the original training, and variations of vehicle parameters; and ii) auto-tuning of the algorithm when applied to different vehicles.

The Horizon Europe HighScape project will explore the feasibility of a family of highly efficient power electronics (PE) components and systems for Battery Electric Vehicles (BEVs), including integrated traction inverters, onboard chargers (OBCs), DC-DC converters, and electric drives for auxiliaries and chassis actuators. 

In the work leading to this deliverable, the HighScape component providers and developers, focusing on the adoption of Wide Bandgap (WBG) based PE devices, have been generating the detailed simulation models of the respective components and systems (i.e., traction motor and traction inverter, OBCs, DC-DC converters, drives for Heating, Ventilation, and Air Conditioning (HVAC), and high voltage levelling suspension systems, and thermal systems for PE components/the whole vehicle), with a coverage of their parametrisation involving a wide range of BEV applications targeted in the project. The models enable model-based component and system design at the electrical, electronic, thermal and control levels. The components and systems models have been assembled into a vehicle simulation toolchain, for the rapid assessment of the implications of component design at the vehicle level, including considerations of thermal aspects. Due to the associated computational effort, the component models have been converted into surrogate models, such as Functional Mock-up Units (FMU) before their inclusion in the BEV simulation model. The definition, benefits and limitations of such surrogate models are discussed in the document. 

This report D2.4 is related to T2.4 where comprehensive simulation activities are carried out to assess the vehicle performance improvement resulting from the EM-TECH motor solutions: 1) the modular motor solution is assessed through a wide range of vehicle applications; 2) the high-efficient motor solution is demonstrated from WLTP drive cycle simulation, supported by advanced pulse and glide (PnG) control and brake blending strategy for further energy consumption reduction and also supported by virtual motor temperature sensor; 3) the E-gear IWM solution is evaluated by optimal gear shifting strategies; 4) the rapid-response motor solution is leveraged by an advanced anti-brake system (ABS) and traction control (TC) development. It is demonstrated that the simulation toolchain developed in D2.4 has been instrumental in advancing the EM-TECH project. By enabling the integration, evaluation, and continuous refinement of cutting-edge e-axle and e-corner technologies, the toolchain has provided valuable insights into vehicle-level performance. It effectively assesses the impact of design decisions on energy efficiency, thermal behaviour, and advanced vehicle control strategies, such as pulse-and-glide, ABS, TC, gear-shifting, and virtual sensing techniques, under both WLTP and real-world operation conditions. The toolchain has successfully bridged the gap between component-level development and vehicle-level performance assessment, ensuring alignment with technical requirements and design objectives. Its flexibility in supporting rapid updates and re-parameterisation has been crucial in accommodating advancements from component suppliers, thus promoting strong collaboration among project partners. 

This report (D6.1) focuses on the integration and testing of two advanced powertrain components: the smart e-corner and the smart e-axle. The In-Wheel Motor (IWM) from ELA is integrated into the smart e-corner at TUIL and the baseline On-board Axial Flux Motor (AFM) is mounted on the powertrain test rig at USR. In parallel, a smart e-axle will be implemented at USR, while the dSpace Scalexio Rapid Prototyping system at TUIL will operate a virtual Vehicle model within a X-in-the-Loop (XiL) framework. This setup allows for distributed real-time simulation and control across both sites.

The experimental configurations will be equipped with comprehensive instrumentation—voltage, current, torque, temperature, and vibration sensors—to measure electrical and mechanical performance, thermal behaviour, and Noise, Vibration, and Harshness (NVH) characteristics. Control and monitoring will be coordinated through the dSpace platform and through a secure Virtual Private Network (VPN) connection between USR and TUIL.

EFFEREST aims to explore the holistic user-centric efficiency, cost, and lifetime optimization potentials of future Electric Vehicles (EVs). The project involves developing user-centric solutions for the electric powertrain and Heating, Ventilation, and Air Conditioning (HVAC) systems, as well as novel design methodologies. The deliverable reports on three state-of-the-art EVs benchmarked on a chassis dynamometer under different environmental conditions and driving cycles. It provides an analysis of comfort and efficiency.

The Sustainable & Smart Mobility Strategy outlines the European Union's comprehensive strategy to transform its transport sector into a sustainable, smart, and resilient system. Recognising the critical role of mobility in economic and social life, the strategy aims to address the environmental and societal costs associated with transport, such as greenhouse gas emissions and pollution. By setting ambitious targets for 2030 and 2050, the EU seeks to significantly reduce emissions, enhance digitalisation, and ensure inclusive connectivity. This roadmap includes ten flagship areas with specific actions to modernise the transport sector, promote zero-emission vehicles, improve infrastructure, and foster innovation, all while ensuring that the transition is socially fair and just.

This deliverable provides updated specifications and requirements for the electrical, electronic, and thermal management subsystems of the RHODaS powertrain, building on those previously defined. It focuses on the design of a three-level, three-phase modular T-type converter based on SiC and GaN semiconductors, which must be compact to achieve the targeted gravimetric and volumetric power densities when mounted on top of the motor. The document details the converter’s dimensions, the integration of IMD components, and the semiconductor technologies under consideration.

Because commercially available GaN devices currently support only low voltages and currents, the deliverable proposes alternative strategies, such as using prototype GaN packs or parallelising multiple transistors, alongside a roadmap to address future design challenges. It also describes the supervision and monitoring strategies, including cloud-based functions, and defines the specifications for the thermal management system, with attention to environmental conditions and cooling requirements. The report concludes with a consolidated summary of the converter specifications, providing a reference for subsequent development and validation.

Insights on the technology developed in the EU-funded project EM-TECH for using recycled permanent magnets