Digital design

Modern Smart Grids are complex systems incorporating physical components like distributed energy resources and storage, along with cyber components for advanced control, networking, and monitoring. This study proposes an integrated methodology for risk prioritization and failure mode classification into low, moderate and high-risk faults using Grey Relational Analysis (GRA) together with Failure Mode and Effects Analysis (FMEA) and Deep Learning algorithms. The results demonstrate that, especially in complex systems like Smart Microgrids, the proposed method more accurately captures the coupling relationships between failure modes compared to the conventional FMEA method.

31% of failures in power electronics are caused by semiconductor switch failure. Semiconductor junction temperature is the most crucial failure stress factor. A Remaining Useful Lifetime (RUL) estimation platform is needed for Prognostic and Health Management of power electronics converters.

This paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.

The RHODaS Webinar Series presents four interconnected sessions exploring the latest European research on next-generation electric powertrain technologies. Hosted by the RHODaS consortium under the Horizon Europe framework, funded by ‪‪the European Commission‬ and as part of the E-VOLVE Cluster, the webinar series will feature insights from the RHODaS, SCAPE, ‪‪Maxima‬ and EM-TECH projects, bringing together leading experts in power electronics, digital systems, and sustainability. Each webinar focuses on a specific technological domain critical to the electrification of transport — from component design and thermal management to digital intelligence and circularity. Together, they illustrate how European research is transforming electric mobility through efficiency, reliability, and environmental responsibility. 

Digitalization is redefining how electric vehicles are monitored and optimized. Experts from RHODaS, SCAPE, and MAXIMA will showcase digital twin technologies, fault management systems, and real-time health assessment tools for next-generation powertrains.

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.

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 work aims to bridge the gap between individual research to provide a comprehensive review from a technically informed and academically neutral standpoint. Conceptual groundwork of digital twin technology is built systematically for the reader, to allow insight into its inception and evolution. The study sifts the digital twin domain for contributions in smart vehicle systems, exploring its potential and contemporaneous challenges to realization.

Description of the virtual sensing for electric motor and inverter control developed in the EU funded project EM-TECH.