Computer Vision for DC Partial Discharge Diagnostics in Traction Battery Systems-Reference-Cited by-同舟云学术

Computer Vision for DC Partial Discharge Diagnostics in Traction Battery Systems

Published:2023-08-15 Issue:8 Volume:14 Page:222
ISSN:2032-6653
Container-title:World Electric Vehicle Journal
language:en
Short-container-title:WEVJ

Author:

Sangouard Ronan¹²^ORCID,Freudenberg Ivo¹³^ORCID,Kertel Maximilian¹⁴^ORCID

Affiliation:

1. BMW AG, 80809 Munich, Germany

2. Department of Computer Science, Technical University of Munich, 80333 München, Germany

3. Faculty of Electrical Engineering, Darmstadt University of Applied Sciences, 64295 Darmstadt, Germany

4. Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany

Abstract

The tendency towards thin insulation layers in traction battery systems presents new challenges regarding insulation quality and service life. Phase-resolved DC partial discharge diagnostics can help to identify defects. Furthermore, different root causes are characterized by different patterns. However, to industrialize the procedure, there is the need for an automatic pattern recognition system. This paper shows how methods from computer vision can be applied to DC partial discharge diagnostics. The derived system is self-learning, needs no tedious manual calibration, and can identify defects within a matter of seconds. Thus, the combination of computer vision and phase-resolved DC partial discharge diagnostics provides an industrializable system for detecting insulation faults and identifying their root causes.

Funder

BMW AG

Publisher

MDPI AG

Subject

Automotive Engineering

Link

https://www.mdpi.com/2032-6653/14/8/222/pdf

Reference38 articles.

1. Cozzi, L., and Bouckaert, S. (2023). CO2 Emissions in 2022, International Energy Agency. Technical Report.

2. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles;Xiong;Appl. Energy,2020

3. Küchler, A. (2017). High Voltage Engineering: Fundamentals-Technology-Applications, Springer.

4. Guo, J., Zheng, Z., and Caprara, A. (July, January 22). Partial Discharge Tests in DC Applications: A Review. Proceedings of the 2020 IEEE Electrical Insulation Conference (EIC), Knoxville, TN, USA.

5. Freudenberg, I., Betz, T., Gillilan, S., and Hild, D. (2022, January 8–10). Early fault detection of thin insulation layers in traction battery systems using dc partial discharge diagnostics. Proceedings of the VDE High Voltage Technology; 4. ETG-Symposium, Berlin, Germany.