@inproceedings{55213cb15fba42f4a6cefe14fba2f767,
title = "LSTM Model-Based Fault Detection for Electric Vehicle{\textquoteright}s Battery Packs",
abstract = "An LSTM model-based fault detection method dedicated for a liquid leakage and liquid intrusion detection system was proposed and described. The method utilized two individual residual evaluation approaches. The first based on statistical analysis and the second based on model error modelling methodology. Both approaches were trained and tested using collected datasets of a prototyped laboratory stand, simulating liquid intrusion and liquid leakage faults of an electric vehicle{\textquoteright}s battery pack with direct liquid cooling battery thermal management system. Obtained results were compared and discussed in details and have revealed notably higher robustness of one of the proposed approaches.",
keywords = "Battery pack, Electric vehicle, Liquid intrusion, Liquid leakage, Long short-term memory model, Model-based fault detection",
author = "Grzegorz W{\'o}jcik and Piotr Przysta{\l}ka",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Diagnostics of Processes and Systems, DPS 2022 ; Conference date: 05-09-2022 Through 07-09-2022",
year = "2023",
doi = "10.1007/978-3-031-16159-9\_18",
language = "English",
isbn = "9783031161582",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "217--229",
editor = "Zdzislaw Kowalczuk",
booktitle = "Intelligent and Safe Computer Systems in Control and Diagnostics",
address = "Germany",
}