Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.

The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.