Model-free quantitative diagnosis of internal short circuit for lithium-ion battery packs under diverse operating conditions

Abstract

The internal short circuit (ISC) serves as a primary source of thermal runaway in lithium-ion batteries, thereby posing a potential safety risk. The early identification of ISC faults is crucial for mitigating risks associated with fire or explosion, but quantitatively diagnosing minor energy leakage in the initial stage of ISC can be a considerable challenge in the absence of battery modeling or preliminary experiments. In light of these constraints, this study puts forth a quantitative diagnosis technique underpinned by an innovative model-free approach tailored for battery pack systems. Our method hinges on the analysis of voltage characteristics and certain premises, wherein the variance in the quantity of electrical charge retained in a cell is estimated solely through measured voltage data. The ISC current is determined through measured current data while maintaining cell balance within the packs. In addition, we propose an ISC current compensation scheme to accommodate fluctuations in ambient temperatures over time. Consequently, the ISC resistances of all cells within the packs can be computed as an index of fault to assess the degree of ISC severity. To ascertain the feasibility of our proposed method, we undertook ISC pack experiments under a wide range of conditions, encompassing aspects such as cell inconsistency, cell balancing, subzero and varying ambient temperatures, and diverse charge/discharge protocols. The results of our experiments demonstrate that the proposed model-free method exhibits considerable applicability for real-world battery operational circumstances while also maintaining a high degree of accuracy in ISC resistance estimation.