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This paper presents a non-invasive, real-time fault detection and predictive maintenance framework for Connected Electric Truck powertrain. Leveraging the ultra-low-power ISM330DHCX sensor’s Machine Learning Core (MLC), the system performs on-sensor processing of accelerometer and gyroscope signals using Edge AI and TinyML techniques. Decision Trees (DTs) and an eight-DT based Random Forest (RF) model were employed to classify vibration patterns under varying operating conditions. Five experiments were conducted on electric motor test benches to simulate rotor imbalance and gather data in both normal and faulty states. Vibration signals were first classified into three broader fault categories and then mapped into 12 subclasses using a novel output mapping scheme. Guided by the Design, Implementation, Potential Failure and Functional Failure (DIPF) curve, this framework enables accurate detection of rotor unbalance faults. Experimental results showed that while single-DT models achieved high training accuracies (95−98.42%) but poor validation performance (<50%), the eight-DT based Random Forest with subclass splitting significantly improved generalization, achieving 98.74% validation accuracy. By minimizing power consumption and bandwidth requirements, this on-sensor Edge AI sensor network approach offers a scalable solution for predictive maintenance, with the potential to reduce downtime and maintenance costs by 30−50%. The findings highlight the promise of ultra-low-power intelligent sensing for enhancing the reliability and efficiency of Connected Electric Trucks.

The Dual Active Bridge Converter topology is widely recognized for its high power density in high-power applications, enabling soft switching and achieving high efficiencies in both buck and boost operation modes. However, under conventional phase-shift modulation, operation at light or no load results in hard-switching and high effective currents, leading to increased overall losses, one of its main drawbacks. These issues have been primarily addressed by implementing complex modulation strategies, leveraging from the multiple degrees of freedom in the control of the converter power, particularly the inner and outer shift angles of its bridges. Contrary to the traditional approach, this work proposes the modulation of the series inductance of the DAB converter by implementing a switched inductor, aiming for a simplified modulation strategy. The proposed method effectively achieves zero current under no-load conditions and significantly reduces effective currents at light loads compared to the traditional phase-shift modulation approach. Although an in-depth comparison with other modulation schemes is required, this work represents a stepping stone in the analysis of the topology and the comprehension of its trade-offs.

This article explores battery health monitoring in electric vehicles (EVs) using machine learning to address challenges in battery durability and enable new business models. It introduces a virtual battery prototype that applies supervised learning methods, such as Random Forest and Deep Neural Network regression, to estimate real-time energy slack and monitor battery health. The study also presents a carbon balance optimization application, aiming to minimize carbon emissions and charging costs for EV fleets through grid optimization. The model enables continuous battery health monitoring, opening opportunities for innovative commercial use cases for EV users, fleet managers, and grid operators.

This report identifies the critical research and innovation (R&I) priorities for decarbonising Europe's heavy-duty vehicles, based on direct feedback from industry stakeholders. The findings reveal a consensus: battery electric technology is the primary pathway forward, with significant stakeholder support for R&I focused on its improvement. While battery electric technology is perceived as more mature, hydrogen is considered a complementary solution for the most demanding long-haul routes. Large-scale demonstrations are suggested for de-risking operations and evaluating integration with the transport and energy system. The analysis confirms that achieving TCO parity or better compared to diesel is the most important factor for market uptake. This study provides direct, evidence-based guidance for EU transport R&I policy, helping to chart the road ahead and orient R&I call programming to meet the ambitious CO₂ emission standards for heavy-duty vehicles.

This report presents a comprehensive overview of the activities carried out in Work Package 4 (WP4), specifically Task 4.3, of the RHODAS project, which focuses on the design and development of a digital twin framework for key components of electric powertrains.  The developed framework comprises three individual digital twins: the inverter digital twin, the e-motor digital twin, and the gearbox digital twin. The inverter digital twin models the electrothermal behavior of power electronics modules (SiC and GaN) to support performance monitoring and failure prediction. The e-motor digital twin captures the thermal behavior of the rotor and stator of the RHODAS e-motor, enabling accurate simulation under various driving conditions. The gearbox digital twin is based on a data-driven thermal equivalent circuit model that estimates the gearbox oil temperature using a range of input data.  All digital twin models are implemented in both MATLAB/Simulink and C++ to enable seamless integration into the RHODAS cloud platform, supporting both online and offline thermal monitoring and efficient computation.  The deliverable also includes extensive sensitivity analyses to investigate the behavior of the e-motor, power inverter, and gearbox under different operating modes. This outcome delivers a robust and scalable digital twin architecture that enhances monitoring, diagnostics, and predictive maintenance capabilities within the RHODAS electric vehicle platform. 

DENSO Corporation, based in Japan, is a global leader in automotive technology and components. The company specializes in the development and production of advanced systems and products for vehicles, including powertrain control systems, thermal systems, electronics, and advanced safety technologies. Founded in 1949, DENSO operates in over 200 locations worldwide and serves major automakers, including Toyota, Honda, and General Motors.

In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

The design and optimisation of a permanent magnet-assisted synchronous reluctance (PMaSynR) traction machine is described to improve its energy efficiency over a selection of driving cycles, when installed on a four-wheel-drive electrically powered vehicle for urban use, with two on-board powertrains. The driving cycle-based optimisation is defined with the objective of minimising motor energy loss under strict size constraints, while maintaining the peak torque and restricting the torque ripple. The key design parameters that exert the most significant influence on the selected performance indicators are identified through a parametric sensitivity analysis. The optimisation brings a motor design that is characterised by an energy loss reduction of 8.2% over the WLTP Class 2 driving cycle and 11.7% over the NEDC and Artemis Urban driving cycles, at the price of a 4.7% peak torque reduction with respect to the baseline machine. Additional analysis, implemented outside the optimisation framework, revealed that different coil turn adjustments would reduce the energy loss along the considered driving cycles. However, under realistic size constraints, the optimal design solutions are the same.

This paper presents a novel Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) infrastructure designed to optimize energy flow between electric vehicles and the electrical grid. The system is equipped with a bidirectional converter and a three-phase inverter/rectifier, minimizing the number of switches to reduce weight and size. A model predictive control (MPC) scheme is introduced to regulate the converter's operation and maintain grid stability, while also functioning as an active power filter when idle. Simulation results using MATLAB/Simulink demonstrate the system's efficiency, verifying its ability to manage energy transfer and mitigate harmonic distortion effectively.

In this paper an electro-mechanical levelling system based on wide band-gap power electronics is proposed. The system is currently under development. Therefore, this document aims at introducing the reasons behind the choice of an electro-mechanical actuator operating at high voltage. High-level simulation models for the different parts have been developed to study the system response and to guide the design and the optimization of the various components. Preliminary results extracted from the simulating model are also provided.