Print Email Facebook Twitter An online data driven fault diagnosis and thermal runaway early warning for electric vehicle batteries Title An online data driven fault diagnosis and thermal runaway early warning for electric vehicle batteries Author Sun, S.Z. (TU Delft DC systems, Energy conversion & Storage; Beijing Institute of Technology) Wang, Zhenpo (Beijing Institute of Technology) Liu, Peng (Beijing Institute of Technology) Qin, Z. (TU Delft DC systems, Energy conversion & Storage) Chen, Yong Han, Yang (The University of Manchester) Wang, Peng (Zhejiang Geely Automobile Research Institute Co) Bauer, P. (TU Delft DC systems, Energy conversion & Storage) Date 2022 Abstract Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use. Subject Discrete Fréchet distance (DFD)fault diagnosislithium-ion battery (LIB)local outlier factor (LOF) To reference this document use: http://resolver.tudelft.nl/uuid:a03ab1c9-bc1f-4a62-b1d2-20783b4d60d4 DOI https://doi.org/10.1109/TPEL.2022.3173038 Embargo date 2023-07-01 ISSN 1941-0107 Source IEEE Transactions on Power Electronics, 37 (10), 12636-12646 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 S.Z. Sun, Zhenpo Wang, Peng Liu, Z. Qin, Yong Chen, Yang Han, Peng Wang, P. Bauer Files PDF An_Online_Data_Driven_Fau ... teries.pdf 6.59 MB Close viewer /islandora/object/uuid:a03ab1c9-bc1f-4a62-b1d2-20783b4d60d4/datastream/OBJ/view