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Artificial Neural Network–Based Multisensor Monitoring System for Collision Damage Assessment of Lithium‐Ion Battery Cells
Author(s) -
Zhang Jian,
Lv Dian,
Simeone Alessandro
Publication year - 2020
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202000031
Subject(s) - collision , battery (electricity) , automotive engineering , computer science , artificial neural network , simulation , engineering , artificial intelligence , computer security , power (physics) , physics , quantum mechanics
The sales of electric vehicles (EVs) have seen a significant upward trend in recent years. The occurrence of severe collision accidents has raised awareness concerning safety issues of lithium‐ion batteries (LIBs) which are the core components of EV energy storage systems. When collision accidents of vehicles occur, in most cases, the battery cells will immediately burn and even explode, whereas in some cases, despite not directly showing visual damages, the battery cells may lead to delayed catastrophic failures. Herein, the assessment of the battery cells collision damage based on sensor signal data is focused on by identifying potentially unsafe cells. A campaign of collision experimental tests and a series of electrical performance tests are conducted on single‐cell specimens. A cell damage characterization procedure is proposed and implemented on the battery cells after the collision tests. The impact force and z ‐axis acceleration signal features and respective damage classes are input to an artificial neural network (ANN) pattern classifier to train a model to assess the battery cell collision damage.