Development of a Diagnostic Procedure for Vehicle’s Built-in Electric Motors

Electric and hybrid vehicles, similarly to combustion engine ones, can experience malfunctions, yet preventive diagnostics for their electric motors are underdeveloped. While many methods exist for testing electric motors in heavy industry, they are not commonly applied in the vehicle industry. Our study aims to develop a real-time, non-invasive fault detection procedure for electric motors in these vehicles. Previous research has focused on simulations, but our work involves real measurements conducted in a controlled laboratory using a two-axle chassis dyno. We present the hybrid vehicle’s drivetrain, our equipment, and the feasibility of simulated methods, and we also detail the evaluation method used.

Authors

Institutions

Publication Details

Published
2024-10-28
DOI
https://doi.org/10.3390/engproc2024079004
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a Diagnostic Procedure for Vehicle’s Built-in Electric Motors

Márton Jagicza, Dávid Józsa, Gábor László Tóth, Balázs Baráth
Machine Fault Diagnosis Techniques
article

Development of a Diagnostic Procedure for Vehicle’s Built-in Electric Motors

Márton Jagicza, Dávid Józsa, Gábor László Tóth, Balázs Baráth
article en

Abstract

Electric and hybrid vehicles, similarly to combustion engine ones, can experience malfunctions, yet preventive diagnostics for their electric motors are underdeveloped. While many methods exist for testing electric motors in heavy industry, they are not commonly applied in the vehicle industry. Our study aims to develop a real-time, non-invasive fault detection procedure for electric motors in these vehicles. Previous research has focused on simulations, but our work involves real measurements conducted in a controlled laboratory using a two-axle chassis dyno. We present the hybrid vehicle’s drivetrain, our equipment, and the feasibility of simulated methods, and we also detail the evaluation method used.

Széchenyi István University (HU)
Openalex Percentile: Top 78%
Machine Fault Diagnosis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.