Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies.

Lithium-ion batteries (LIBs) are pivotal in electric vehicles (EVs), grid storage, and portable electronics, but their high energy density introduces safety risks, particularly thermal runaway (TR). TR can lead to fires, explosions, and hazardous emissions, posing severe health and environmental threats. Experimental investigation of TR commonly relies on abuse testing methods, among which mechanical abuse via nail penetration (NP) and thermal abuse (TA) are widely used to simulate crash-induced and heat-driven failure scenarios, respectively. This review provides a comprehensive and comparative synthesis of NP and TA testing methodologies, examining how variations in test configuration, cell parameters (capacity, state of charge, and chemistry), and environmental conditions influence TR behavior and emission characteristics. Particular emphasis is placed on comparing reported emission profiles from NP- and TA-triggered TR events, including CO₂, CO, HF, hydrocarbons, and solvent vapors, and identifying the methodological origins of discrepancies across studies. By systematically linking emission variability to gas collection methods, analytical techniques, and data normalization approaches, this review highlights key limitations in current testing standards related to emission characterization. Finally, recommendations are offered for harmonizing abuse testing protocols and improving experimental design to enhance reproducibility, enabling meaningful cross-study comparison, and supporting safer deployment of LIBs in high-risk applications such as EVs and grid-scale energy storage.