Explore the vast range of titles available.

The powertrain in electric vehicles typically comprises various components, including lithium-ion batteries (LIBs), a battery management system, an energy converter, an electric motor, and a mechanical transmission system. Electric vehicles utilize the electrical energy stored in LIBs to efficiently drive the motors efficiently. LIBs find widespread use in portable electronic devices like laptops, mobile phones, and other electronic appliances, with potential applications in the automotive sector. To examine the thermal performance of LIBs across diverse applications and establish accurate thermal models for batteries, it is essential to understand heat generation. Numerous researchers have proposed various methods to determine the heat generation of LIBs through comprehensive experimental laboratory measurements. This study comprehensively explores diverse experimental and modeling techniques used to analyze the thermal behavior and heat generation of LIBs.

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.

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.

A precise lithium-ion battery model is required to specify their appropriateness for different applications and to study their dynamic behavior. In addition, it is important to design an efficient battery system for power applications. In this investigation, a second-order equivalent electrical circuit battery model, which is the most conventional method of characterizing the behavior of a lithium-ion battery, was developed. The current pulse procedure was employed for parameterization of the model. The construction of the model was described in detail, and a battery model for a 13 Ah lithium titanate oxide battery cell was demonstrated. Comprehensive characterization experiments were accomplished for an extensive range of operating situations. The outcomes were employed to parameterize the suggested dynamic model of the lithium titanate oxide battery cell. The simulation outcomes were compared to the laboratory measurements. In addition, the proposed lithium-ion battery model was validated. The recommended model was assessed, and the proposed model was able to anticipate precisely the current and voltage performance.

This review examines the aging mechanisms and performance decline of lithium-ion batteries under various conditions, focusing on temperature effects, charge/discharge efficiency, and operational limits. It covers high-temperature aging and its impact on the solid electrolyte interphase (SEI) layer, as well as thermal runaway risks. Low-temperature aging is also discussed, emphasizing reversible capacity loss, increased resistance, and lithium plating. The review addresses degradation from overcharge/over-discharge scenarios and explores coulombic efficiency (CE) degradation and its link to capacity loss. By synthesizing current research, it provides insights into optimizing battery management and enhancing performance.

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.

Metal–air batteries represent a category of energy storage system that leverages the reaction between metal and oxygen from the atmosphere to produce electricity. These batteries, known for their high energy density, have attracted considerable attention as potential solutions for extending the range of electric vehicles. Understanding the capabilities and limitations of metal-air batteries as range extenders is crucial for advancing electric vehicle technology, as these batteries could offer the additional energy needed to overcome current range limitations. This review paper provides a detailed overview of various metal-air battery technologies, delving into their design, functionality, and inherent challenges. By analyzing key theoretical and practical parameters, the study highlights how these factors influence overall battery performance. Additionally, the review addresses critical cost considerations, particularly the relationship between vehicle cost and driving range, uncovering the significant trade-offs involved in adopting metal-air batteries. Through an examination of nearly all the existing metal-air batteries, this paper sheds light on their potential to serve as effective range extenders, thereby facilitating the transition to a cleaner, more sustainable transportation landscape.

A precise interpretation of lithium-ion battery (LIB) heat generation is indispensable to the advancement and accomplishment of thermal management systems for different applications of LIB, including electric vehicles. The internal resistance of a lithium titanate oxide (LTO)-based LIB was determined at different state of charge (SOC) levels and current rates to understand the relationship between internal resistance and heat generation. Random and different pulse discharge current step durations were applied to consider the effect of different SOC interval levels on heat generation. The total generated heat was measured for different discharge rates and operating temperatures in a Netzsch IBC 284 calorimeter. It was seen that a 6.7% SOC decrease at high SOC levels corresponds to 0.377 Wh, 0.728 Wh, and 1.002 Wh heat generation for 26A, 52A, and 78A step discharge, both at 20 °C and 30 °C. However, a 1.85% SOC decrease at medium SOC levels corresponds already to 0.57 Wh, 0.76 Wh, and 0.62 Wh heat generation. It can be inferred that the impact of SOC level on heat generation for this cell is more prominent at a lower than at a higher SOC.

Electric vehicles (EVs) and hybrid vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for their implementation are that they consume less or do not consume fossil fuel (no carbon dioxide pollution) and do not cause sound pollution. However, this technology has some challenges, including complex and troublesome accurate state of health estimation, which is affected by different factors. According to the increase in electric and hybrid vehicles’ application, it is crucial to have a more accurate and reliable estimation of state of charge (SOC) and state of health (SOH) in different environmental conditions. This allows improving battery management system operation for optimal utilization of a battery pack in various operating conditions. This article proposes an approach to estimate battery capacity based on two parameters. First, a practical and straightforward method is introduced to assess the battery’s internal resistance, which is directly related to the battery’s remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, simple, accurate, and reliable technique is proposed to estimate battery capacity appropriately. The root mean square percentage error and the mean absolute percentage error of the proposed methods were calculated and were less than 0.02%. It was concluded the geometry method has all the advantages of a recursive manner, including a fading memory, a close form of a solution, and being applicable in embedded systems.

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.