An Intelligent Multifault Diagnosis Method for Lithium-Ion Battery Based on Deep Learning Framework

The nonlinearity of lithium-ion batteries complicates the fault mechanisms, particularly making the diagnosis of minor faults challenging due to the lack of obvious fault characteristics. While early-stage faults may not immediately compromise battery performance, their unchecked propagation could eventually lead to critical operational failures. This study proposes an intelligent multi-fault diagnosis method for lithium-ion batteries, enabling effective identification of minor internal short circuit, overcharge, and overdischarge faults. By leveraging voltage and current data segments alongside the incremental capacity curve as inputs for the temporal convolutional network model, achieving initial fault recognition. For diagnosed defective cells, the voltage signal is processed utilizing variational mode decomposition optimized by the bat algorithm to enhance fault features, facilitating intelligent diagnosis of multiple faults. Through validation across diverse operational scenarios and varying battery health states, the proposed method achieves an impressive fault diagnosis accuracy rate of 99.62%, enabling effective identification and diagnosis of multiple faults, which is paramount in preventing the escalation of faults and averting potential safety hazards.