Abstract: The large-scale deployment of high-energy-density lithium-ion batteries (LIBs) in electric transportation and grid storage has imposed increasingly stringent requirements on battery safety, reliability, and intelligent management. However, the limited observability of internal electrochemical, thermal, and mechanical states remains a fundamental challenge, leading to persistent safety risks, degraded low-temperature performance, and accelerated aging, which collectively hinder the scalable adoption of electrified systems. To overcome these challenges, conventional battery management systems (BMSs) are evolving beyond voltage-current-temperature measurements toward high-fidelity state estimation enabled by advanced nondestructive sensing technologies, including emerging internal and implantable diagnostic concepts. Based on a comprehensive analysis of physical signals associated with material aging and failure mechanisms, this review provides a systematic and critical assessment of nondestructive testing techniques for battery state monitoring. Beyond a conventional technique-oriented summary, recent advances in battery state diagnosis and lifetime management algorithms are examined, with a particular emphasis on multisource physical feature fusion strategies. More importantly, this review establishes a unified framework linking degradation mechanisms, internal physical signals, and state estimation strategies, offering a cross-scale perspective to guide the codesign of advanced sensing technologies and intelligent algorithms, thereby facilitating the development of next-generation BMSs.