Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Abstract

The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

“…All of this was seamlessly integrated into the ROS and Gazebo ecosystem, enabling efficient communication between the wheelchair model and other components of the robotic system. Thanks to this integration, we could simulate and evaluate the performance of the model under a wide range of environmental conditions and operational situations, ensuring that our virtual wheelchair is at the forefront in terms of realism and functionality [114,115]. Our advanced implementation of actuator dynamics and drive control within ROS/Gazebo allowed us to offer an exceptionally accurate and functional simulation of the wheelchair, furthering our commitment to creating innovative and cutting-edge solutions in the field of robotics and mobility assistance.…”

Parasitic-Based Model for Characterizing False Turn-On and Switching-Based Voltage Oscillation in Hybrid T-Type Converter

“…Through boosting the switching frequency, the passive components of the converters can be shrunk, hence offering compact and high power density converter design [4,5]. Thanks to the high switching capability of SiC, they can be used in resonant converters, which are widely employed in various applications like electric vehicle battery chargers [6], drive systems [7], and V2X power converters [8]. Furthermore, SiC semiconductors are able to maintain higher junction temperature, which makes them suitable candidates for the compact power electronic converters in integrated motor drive applications [9].…”