Digital design

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 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.