Artificial Neural Network enabled P2D Model Deployment for End-of-Line Battery Cell Characterization
作者： Artem Turetskyy , Vincent Laue , Raphael Lamprecht , Sebastian Thiede , Ulrike Krewer
DOI: 10.1109/INDIN41052.2019.8972181
关键词: Estimation theory 、 Battery (electricity) 、 Characterization (materials science) 、 Software deployment 、 Line (electrical engineering) 、 Process (computing) 、 Artificial neural network 、 Battery cell 、 Control engineering 、 Computer science
摘要: The production chain of lithium-ion battery cells is an intricate process with manifold process-product interdependencies leading to a complex product, the cell. In order assess quality and performance cell in end-of-line characterization, models need be fit experimental data estimate unmeasured physical parameters This procedure laborious error prone model complexity large number therefore presently not suitable as end-of line test large-scale production. this paper, used generate by varying measured train artificial neural network for fast reliable diagnostics. presented capable fitting less than second make it more characterization.
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