Inference of Historical Abusive Operations on Li-Ion Batteries Using Series Voltage Synchronicity

To guarantee the safety of electric vehicles (EVs), abusive operations should be strictly prohibited for EV-mounted li-ion batteries (LiBs). This article proposes an intelligent strategy to infer the historical abusive operations (HAOs) on LiBs by retrospectively analyzing the relationship between HAO-induced damages and electrical behaviors. First, the electrical synchronicity among the peer LiB cells in a series module is perceived using an improved correlation coefficient formula; then the synchronicity sequences are translated into recurrence plot images (RCPIs) and Gramian angular field images (GAFIs) that can provide a wealth of textures regarding cross-time autocorrelations. Second, based on the hierarchical clustering algorithm, a pilot test is performed to preliminarily examine the separability of these images corresponding to different HAOs. Finally, the GoogLeNet model is employed to model the causality between image features and HAO specifics, whereby the type and intensity of potential HAO can be inferred. A realistic dataset is obtained by inflicting abuses such as overvoltage, overheat, and vibration on LiB cells. Experimental verifications show that the proposed strategy performs well in backtracking the HAOs on LiBs by providing effective and reliable inferences on HAO specifics. The accuracy rates of HAO type assessment and severity evaluation can achieve about 77% and 76% using RCPIs, and about 79% and 76% using GAFIs, respectively.