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.

The lithium-ion battery (LIB) has the advantages of high energy density, low self-discharge rate, long cycle life, fast charging rate and low maintenance costs. It is one of the most widely used chemical energy storage devices at present. However, the safety of LIB is the main factor that restricts its commercial scalable application, specifically in hazardous environments such as underground coal mines. When a LIB is operating under mechanical and electrical abuse such as extrusion, impact, overcharge and overheating, it will trigger thermal runaway and subsequently cause fire or even an explosion. According to the relevant requirements in IEC60079, the explosion-proof protection of LIB can be adapted to the working environment of high dust and explosive gas environments such as in the mining face of coal production. This paper presents an overview of the LIB-relevant technology, thermal runaway, safety and applications in the general mining industry with implications to establish a theoretical and technical basis for the application of high-capacity LIBs in the industry. These then promote intelligent, safe and efficient production not only for the coal mine industry but also for non-coal applications.