Enabling Online Search and Fault Inference for Batteries Based on Knowledge Graph

Abstract

The safety of batteries has become a major obstacle to the promotion and application of electric vehicles, and the use of cloud-based vehicle practical big data to summarize the fault knowledge of batteries to improve product quality and reduce maintenance costs has attracted widespread attention from academia and industrial communities. In this paper, a method is proposed to construct the battery fault knowledge graph which supports online knowledge query and fault inference. Reliability models for battery undervoltage, inconsistency, and capacity loss are built based on cloud data, and are deployed and continuously updated in the cloud platform to accommodate the migration of the models to different battery products. A bidirectional long short-term memory (Bi-LSTM) neural network was established for knowledge extraction of fault logs, and the results were imported into Neo4j to form a battery fault knowledge graph. Finally, a fault knowledge online query front-end interface was built to conduct inference tests on battery faults of a manufacturer, which proves the feasibility and effectiveness of the proposed method.