Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures.

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy.

To enhance our understanding of the thermal characteristics of lithium-ion batteries and gain valuable insights into the thermal impacts of battery thermal management systems (BTMSs), it is crucial to develop precise thermal models for lithium-ion batteries that enable numerical simulations. The primary objective of creating a battery thermal model is to define equations related to heat generation, energy conservation, and boundary conditions. However, a standalone thermal model often lacks the necessary accuracy to effectively anticipate thermal behavior. Consequently, the thermal model is commonly integrated with an electrochemical model or an equivalent circuit model. This article provides a comprehensive review of the thermal behavior and modeling of lithium-ion batteries. It highlights the critical role of temperature in affecting battery performance, safety, and lifespan. The study explores the challenges posed by temperature variations, both too low and too high, and their impact on the battery’s electrical and thermal balance. Various thermal analysis approaches, including experimental measurements and simulation-based modeling, are described to comprehend the thermal characteristics of lithium-ion batteries under different operating conditions. The accurate modeling of batteries involves explaining the electrochemical model and the thermal model as well as methods for coupling electrochemical, electrical, and thermal aspects, along with an equivalent circuit model. Additionally, this review comprehensively outlines the advancements made in understanding the thermal behavior of lithium-ion batteries. In summary, there is a strong desire for a battery model that is efficient, highly accurate, and accompanied by an effective thermal management system. Furthermore, it is crucial to prioritize the enhancement of current thermal models to improve the overall performance and safety of lithium-ion batteries.

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

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.

This paper provides an overview of the significance of precise thermal analysis in the context of lithium-ion battery systems. It underscores the requirement for additional research to create efficient methodologies for modeling and controlling thermal properties, with the ultimate goal of enhancing both the safety and performance of Li-ion batteries. The interaction between temperature regulation and lithium-ion batteries is pivotal due to the intrinsic heat generation within these energy storage systems. A profound understanding of the thermal behaviors exhibited by lithium-ion batteries, along with the implementation of advanced temperature control strategies for battery packs, remains a critical pursuit. Utilizing tailored models to dissect the thermal dynamics of lithium-ion batteries significantly enhances our comprehension of their thermal management across a wide range of operational scenarios. This comprehensive review systematically explores diverse research endeavors that employ simulations and models to unravel intricate thermal characteristics, behavioral nuances, and potential runaway incidents associated with lithium-ion batteries. The primary objective of this review is to underscore the effectiveness of employed characterization methodologies and emphasize the pivotal roles that key parameters—specifically, current rate and temperature—play in shaping thermal dynamics. Notably, the enhancement of thermal design systems is often more feasible than direct alterations to the lithium-ion battery designs themselves. As a result, this thermal review primarily focuses on the realm of thermal systems. The synthesized insights offer a panoramic overview of research findings, with a deeper understanding requiring consultation of specific published studies and their corresponding modeling endeavors.