Battery energy storage systems bolster power grids” absorption capacity, however, battery safety issues remain a formidable challenge. Timely and precise fault diagnosis, coupled with early-stage fault warnings., is crucial. This study introduces an eigen decomposition-based multi-fault diagnosis approach for lithium-ion battery packs, enabling online diagnosis of short circuits, electrical connection faults, and voltage sensor malfunctions. By incorporating an interleaved measurement topology, precise fault type differentiation is achieved. Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness. The interleaved topology can be seamlessly integrated using common voltage measurement solutions, eliminating the need for additional design complexities, while sensor number redundancy enhances fault tolerance of battery management systems (BMS). A cloud-side collaboration method is proposed, where the BMS functions as an edge device for specific data computations, while the parameters are fine-tuned by the server through big data analytics. This approach circumvents cumber-some server calculations, thereby curbing server cost escalation. The edge computing process is divided into two steps, with partial calculations often sufficient to evaluate battery safety, thus reducing the computational load on edge devices. Several battery tests are conducted, and the results confirm the method's capability, feasibility, and validity in early-stage fault diagnosis.