Voltage faults diagnosis for lithium-ion batteries in electric vehicles using optimized graphical neural network.

ABSTRACT: Diagnosing voltage faults of lithium-ion batteries is a critical function in the battery management system. Accurate diagnosis of voltage faults is crucial for ensuring the safety and reliability of energy storage applications and electric vehicles (EVs). This article proposes an optimized Graphical Neural Network (GNN) model. Specifically, the optimized GNN model extracts the relationships between various batteries by learning the topology of the batteries. The proposed method combines the physical coupling between batteries and the entanglement of measurement results with the strong nonlinear processing capability of neural networks to improve the effectiveness of fault localization. Experimental results on three publicly available datasets show that the proposed method outperforms basel

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC12301446 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature