Lithium-ion Battery Systems Research Articles

Thermal management system for prismatic lithium-ion batteries coupling with U-type channels and pyrolytic graphite honeycomb fins

The battery thermal management system (BTMS) is crucial for the safety and lifespan of electric vehicles. This paper proposes a hybrid BTMS combining liquid cooling plates arranged on the side and pyrolytic graphite honeycomb fins (PGHF). The cold plate contains two U-type channels with staggered inlets, and the phase change material (PCM) is filled in the honeycomb fins. The internal resistance and entropic coefficient of the battery are obtained experimentally and used to model the battery’s heat production. The heat dissipation capabilities of six different BTMS are compared at a discharge rate of 3C. Compared with traditional serpentine cold plate cooling, the designed BTMS reduces the maximum temperature, maximum temperature difference, and pressure drop by 5.32 K, 6.82 K, and 462.94 Pa, respectively, indicating significant improvements in cooling performance and substantial reductions in system power consumption. In this paper, the effects of different inlet mass flow rates and honeycomb fin porosity on the heat dissipation of the system are investigated to determine their optimal values. Results indicate that when the mass flow rate is 1.0 g∙s−1 and the honeycomb fin porosity is 45.68 %, the maximum temperature of the battery is 310.38 K, the maximum temperature difference is 1.82 K, and the pressure drop is 279.35 Pa, which represents the optimal cooling performance.

Qualitative analysis of the coupling effect of fluid velocity distribution in microchannels on the performance of water-cooling lithium-ion batteries system

ABSTRACT Lithium-ion batteries are the direct power sources of electric vehicles, but their high heat flux density restricts their development. Microchannel heat sinks (MCHS) can assist in heat dissipation to overcome this. The distribution of fluid velocity among microchannels has a considerable effect on the performance of MCHS for a water-cooling lithium-ion batteries system. In this paper, a new microchannel heat sink with high depth to width aspect ratio is proposed and the influence of four different aspect ratios on the distribution of fluid velocity among microchannels and heat transfer performance of MCHS was numerically studied by computational fluid dynamics (CFD). The results showed that at the same inlet flow rate, the larger the aspect ratio of the microchannels, the better the uniformity of the internal fluid velocity. The heat performance of microchannel structures with four different aspect ratios for a water-cooling lithium-ion batteries is further investigated experimentally. Compared with the simulation results of the fluid velocity distribution, the coupling effect between the fluid velocity distribution in the microchannels and the heat dissipation performance of a water-cooling lithium-ion batteries is analyzed. The results fully demonstrated that a larger aspect ratio of the microchannels results in a better heat dissipation performance on the surface of the lithium-ion batteries. Optimizing the aspect ratio of the microchannels to facilitate a relatively uniform velocity distribution to improve heat dissipation performance of the water-cooling system may be a key factor to be considered, which provides a new idea for passive thermal management of batteries.

Exploring the Performance Impact of Neural Network Optimization on Energy Analysis of Biosensor

With the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method.

Microstructure-based digital twin thermo-electrochemical modeling of LIBs at the cell-to-module scale

As the application of lithium-ion batteries (LIBs) expands beyond conventional electric vehicles (EVs) to heavy vehicles such as electric trucks or trams, the importance of thermal management in LIB systems is increasing, even at the module or pack level. In particular, because monitoring the thermal behaviors of each cell is not feasible, thermo-electrochemical modeling and simulations in the module or pack level are essential for analyzing and ensuring thermal stability. However, because the conventional lumped thermo-electrochemical models cannot reflect the actual structure of LIB cells, there might be considerable differences may exist between simulation and experimental results. To fill these gaps, we have newly developed a 3D microstructure-based digital twin model of a battery module (8.8 Ah/18.5 V, five LIB pouch cells in series) for an unmanned railway vehicle. Unlike traditional lumped models, our digital twin model accurately well reflects the internal structure of cells and can calculate the heat generation of each component inside a cell. As a result, contrary to a lumped model, the digital twin model can not only simulate the inhomogeneous temperature gradient inside a cell, but also estimates higher local maximum temperatures (TDT, max/TL, max = 137.2 °C/123.9 °C @ 10C discharge) in cells which can trigger thermal runaway. Therefore, microstructure-based digital twin modeling can alleviate concerns regarding the thermal runaway of LIB cells, modules, and packs, and provide safe operating conditions.

Enhancing multi-type fault diagnosis in lithium-ion battery systems: Vision transformer-based transfer learning approach

Ensuring the accurate diagnosis of internal short circuit (ISC) faults and consistency anomalies is crucial for maintaining the high safety and longevity of battery systems. The challenge lies in the high similarity between the features of ISC faults and consistency anomalies, which can complicate accurate fault diagnosis. This study introduces a multi-fault diagnostic model that leverages the Vision Transformer (ViT) and employs simulated data to accurately pinpoint ISC faults and intricate consistency anomalies, such as capacity and state of charge anomalies, achieving an impressive accuracy rate of up to 100 %. The process begins with an in-depth analysis of fault mechanisms to extract and differentiate multi-fault features using a mean difference model. Subsequently, the ViT's exceptional ability to capture temporal information, which aids in the accurate identification of fault features. The integration of transfer learning with experimental data further refines the real-world diagnosis accuracy. The results confirm the robustness of the proposed method, highlighting its potential to substantially mitigate safety hazards in battery systems and enhance operational reliability.

Recyclability study for the next generation of cobalt-free lithium-ion battery systems with C-LNMO, Si/C- LRLO and TiNbO-LNMO active materials via hydrometallurgical route

The scarcity and uncertain supply of cobalt poses significant challenges for the lithium-ion battery industry. To address this issue, alternative chemistries excluding cobalt have been developed. Despite solving supply issues, these developments raise concerns about recyclability and their impact on current recycling trends.This study presents a recycling strategy and evaluates its performance for next-generation cobalt-free lithium-ion batteries. It focuses on three prototypes that use innovative cathode materials (titanium niobium oxide, carbon, and silicon/carbon) and electrolyte systems (ionic liquid and gelified). The proposed recycling process includes pyrolysis, mechanical separation, neutral and acid leaching, cementation, and neutralisation for selective metal separation.The results indicate that gelified electrolytes exhibit greater thermal stability than their liquid counterparts, indicating an effect on the degradation temperature and gas emissions, which are crucial for metal recovery. This study highlights the reduced toxicity of ionic liquid electrolytes and emphasises the need for strict pyrolysis gas handling due to hazardous emissions. High recovery rates (65–90 %) were achieved for nickel, manganese, lithium, and anode components.With potential for process optimisation to improve the quality of products, this study shows that cobalt-free battery systems can be integrated into existing recycling frameworks with adjustments, supporting progress towards sustainable battery practices.

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Failure mechanism and thermal runaway behavior of lithium-ion battery induced by arc faults

As the widespread of lithium-ion battery systems such as electric vehicles and energy storage systems, the number of safety incidents due to electrical faults are increasing. Many accident reports have demonstrated that arc faults have become one of the main triggers of LIB system accidents, however, the related studies are inadequate. In this study, an arc imitation system is employed to investigate the influence of different arc energies on battery safety valve, as well as the electrochemical characteristics of faulty batteries. The results show that the minimum arc power to breach the safety valve ranges from 110 to 441 W. The maximum temperature rise rate on the battery surface can exceed 15 °C/s with arc power of around 1000 W. Further, the testing of in-situ and ex-situ indicate the faulty batteries undergo degradation and failure due to that moisture in the air enters the battery interior, resulting in increased internal resistance, loss of active materials and cyclable lithium. Finally, the faulty battery has no valve opening during thermal runaway, and the ignition time is four hundred seconds earlier than that of the normal battery, indicating more severe fire dangers. The results are valuable for safety design of battery systems in relation to arc faults, as well as the characteristic for fault detection and early warning.

Carbon-Based 3D Architectures as Anodes for Lithium-Ion Battery Systems.

Graphite, with its exceptional cyclic performance, continues to dominate as the preferred anode material for lithium-ion batteries. However as high-energy application gains momentum, there is growing demand for higher capacities that alloying/de alloying and conversion type anode materials can offer. Despite their potential, these materials are plagued by challenges such as volumetric fluctuations, low conductivities, and poor cyclic stability. Carbon nanostructures, on the other hand, show tremendous promise with their low volume expansion, high ion diffusion rates, and excellent conductivity. Nevertheless, their limited areal and volumetric densities restrict their widespread utilization. To address these limitations, various strategies such as doping, composite formation, and structural modification have been proposed. This article provides a succinct overview of carbon nanomaterials and their electrochemical performance as 3D carbon-based anodes, along with a comprehensive analysis of the strategies employed to overcome associated challenges while evaluating their potential prospects in the field.

Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems: Advances, Challenges, and Prospects

Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems: Advances, Challenges, and Prospects

Impact of plate-deflected flame on thermal runaway propagation of lithium-ion battery

Ceiling jet fire is a common phenomena in thermal runaway (TR) accidents of electric vehicle and energy storage power station scenarios based on lithium-ion battery, which is a potential risk of inducing large-scale fire spread in battery modules, but it is not been known clearly. This work investigates the influence of ceiling jet fire on TR propagation of lithium nickel cobalt manganese oxide (NCM) battery module by arranging a ceiling-like obstacle directly above the tested module. A series of overheat-induced TR propagation tests under different height (H) of ceiling-like obstacle are conducted to gain better understanding of the heat transfer mechanism. The heat transfer and average heating power of ceiling jet fire to adjacent cells are determined for the first time. Results indicate the TR propagation speed of the module accelerates with the decrease of H, and the TR propagation speed inside the cell is not affected by ceiling jet fire. More importantly, the heat transferred from cell experiencing TR to adjacent cells through ceiling jet fire (Qfl) decreases from 11087.33 J to 7872.02 J, and the ratio of Qfl to the total heat released in TR (ΔEtot) declines from 4.02 % to 2.85 % as H increases from 5 to 15 cm. Finally, the change process of contact thermal resistance between adjacent cells and its relationship with heating power are revealed during TR propagation. This work provides a basic understanding of the influence mechanism of ceiling jet fire on TR propagation, which possess important guidance for the safety protection of lithium-ion battery system.

Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape.

Design and Implementation of a Non-Destructive AC Heating System for Lithium-Ion Battery Modules

The electrification of transportation is experiencing rapid development. Electric bicycles (e-bikes) are commonly employed as convenient modes of transportation. Thanks to the advantages of long life and high energy density, lithium-ion batteries (LIBs) are widely used in e-bikes. In certain business models, e-bikes can utilize rental LIBs, which are centrally managed at charging stations. The low-temperature charging and discharging performance of the LIB system poses a significant challenge during usage. Among various heating methods, alternating current (AC) heating has garnered attention due to its high efficiency and has been applied to quickly warm up the LIB system. To address this issue, an AC heating model was established to determine the appropriate frequency and magnitude of the current, and a prototype AC heating system for the LIB modules used in e-bikes was designed. A full-bridge topology system model was established, and an experimental platform was constructed to test the effectiveness of the proposed AC heating topology and thermoelectric model under different AC heating frequencies and currents. The results show that the proposed AC heating system can heat an 18650 battery module within 20 min. Under an ambient temperature of −20 °C, using a 10 A, a 100 Hz excitation current achieves a heating rate of 1.3 °C per minute, with minimum power losses. The prototype also has a fast response time of only 70 ms. Finally, the strategies of LIB heating and insulation are proposed for the scenario of a battery swapping station. This research holds great significance in resolving the problem of low-temperature heating for e-bikes in cold regions.

Meeting the challenge of mitigating Li-ion battery fires for aviation

The aviation industry faces a pressing challenge in reducing its environmental footprint, especially with the projected growth in global passenger traffic. Electrification and more-electric aircraft, particularly through Lithium-ion Battery (LiB) systems, offers a promising pathway to reduce emissions but introduces significant safety concerns, particularly regarding thermal runaway (TR) events. This prospective paper examines the unique challenges posed by aviation environments for developing safe LiB systems. It discusses multifaceted mitigation approaches, integrating fire containment structures with advanced thermal management and fire protection technologies. Various cooling technologies and fire suppression agents are explored for their effectiveness in extinguishing LiB fires and mitigating thermal runaway propagation. Integrated LiB suppression systems are proposed to combine fire containment, suppression, and thermal management functionalities for achieving the demanding specific energy density levels that will be required. Scaling safe LiB pack solutions for commercial aviation requires coordinated efforts among regulators, original equipment manufacturers (OEMs), engineers, and researchers to establish standardized design criteria, develop validated modeling tools, and establish rigorous certification testing requirements. In conclusion, addressing the safety concerns of large LiB packs in aircraft applications requires a holistic, integrated approach. This paper provides insights into current research, identifies key challenges, and outlines future directions for advancing LiB safety in aviation.

Stress-Relieving Carboxylated Polythiophene/Single-Walled Carbon Nanotube Conductive Layer for Stable Silicon Microparticle Lithium-Ion Battery Anodes

Silicon is one of the most promising anode materials for next generation Lithium ion batteries (LIBs) on account of its ultrahigh specific capacity (4200 mAh g-1) and relatively low working potential of 0.3 V (vs. Li+/Li). However, practical commercial implementation of silicon poses a great challenge due to technical difficulties such as low conductivity (10-5-10-3 S cm-1), low initial Coulombic efficiency (50-80%) and stress-induced fracture, along with SEI accumulation due to severe volume changes (~400%) inherent in silicon-based anodes.To address these challenges and advance the next generation of LIBs, we present poly[3-(potassium-4-butanoate)thiophene] (PPBT) and single-walled carbon nanotube (SWNT) coated silicon microparticles (Si MPs) (PPBT/SWNT@Si MPs) as active materials for highly stable Si-based anodes for LIB systems. The water-soluble conjugated polymer, PPBT, was selected for its ability to serve as an ion and electron conducting physical/chemical linker (10-5 S cm-1 vs. PVDF; ~ 10-8 S cm-1) to the surface of various high capacity anode active materials systems. In consideration of current practical requirements for Si-based anode materials, Si MPs were selected as starting materials due to their lower surface area than the nanoscale analogs, a characteristic that can further improve LIB bulk capacity. In addition, Si MPs are known to have high initial Coulombic efficiency and industrial compatibility. With the help of PPBT, which facilitates unraveling of entangled single-walled carbon nanotubes, SWNTs were successfully attached to the surface of the active materials. PPBT carboxylate substituents were found to be chemically bound to the Si MP surface, providing an electrochemically conductive environment in intimate contact with the 1D SWNTs and Si MPs.Due to facilitated ion/electron transport kinetics, PPBT/SWNT@SiMP anodes exhibited superior capacity retention and higher reversible capacities, even at a high current density of 8 A g-1, compared to bare Si MP anodes. Improved initial Coulombic efficiency and stable cycling performance over 300 cycles were achieved. In situ Raman spectroscopy was employed to investigate the evolution of stress on the SWNTs during lithiation/delithiation, which elucidated the mechanism underlying the enhancement in PPBT/SWNT@Si MP anode cycling performance. The single-walled carbon nanotubes within the PPBT/SWNT@Si MP anodes consistently exhibited reversible stress recovery and 45 % less tensile stress variation after the 10th cycle than SWNT@Si MP control anodes fabricated by direct mixing of the microparticles with SWNTs to create SWNT@Si MPs.Combined, the results suggest that a well-developed conductive interface can enhance the rate performance, and improve electrode stability and Coulombic efficiency. Furthermore, the PPBT/SWNT coating directly bound to the Si MP surface provides for efficient stress relaxation of Si-based electrodes, resulting in reversible lithiation/delithiation, low electrode resistance, and reduced SEI layer formation.

Breaking the Temperature Limit of Lithium-Ion Batteries With Carbon Nanotube-Based Electrodes and "Constructive Alliance" Electrolyte Strategy.

Lithium-ion batteries (LIBs) are paramount in energy storage in consumer electronics and electric vehicles. However, a narrow operating temperature range severely constrains their evolution. In this study, a wide-temperature operating LIB system is constructed utilizing carbon nanotube (CNT)-based electrodes and a "constructive alliance" electrolyte. The unique microstructure of the CNT current collector, with high electrical and thermal conductivity, accelerates the reaction kinetics of active materials at subzero temperatures and optimizes the thermal management of the entire electrode at elevated temperatures. Furthermore, a strategy employing the "constructive alliance" electrolyte is proposed, demonstrating that a simple combination of commercially available electrolytes can enhance resilience to harsh thermal conditions. Molecular dynamics simulations and density functional theory calculations reveal that the hybrid electrolyte predominantly adopts aggregate solvation structures and possesses low Li+ desolvation barriers regardless of thermal variations. Consequently, the assembled Li4Ti5O12//LiCoO2 full cell, with a negative/positive electrode material ratio of 1.2, exhibits outstanding electrochemical performance in the wide temperature range of -40 and 60°C. This innovative strategy overcomes challenges in wide-temperature electrolyte research and offers promise for next-generation wide-temperature LIBs.

Shape Optimization for Lithium-Ion Battery with Porous Electrodes

In this presentation, we introduce a gradient-based shape optimization framework to design porous electrodes for maximum energy storage. Lithium-ion batteries are becoming an increasingly important energy source and storage device. Large surface area that advocates high reaction rates often leads to small porosities that deteriorate electrochemical transport, which significantly restricts ion penetration depth, limiting the material utilization in planar electrodes. Consequently, architected electrodes are required to optimize performance.We consider a model with two materials, namely porous electrode and pure electrolyte. Density-based topology optimization with continuous material volume fraction has previously been implemented on lithium-ion battery system for both half-cell and full-cell model to maximizes its energy storage. Despite the capability of producing complex interdigitated structures, an appropriate penalization scheme for intermediate materials is required for the optimizer to provide near binary designs. Setting up such a penalization is often time-consuming due to the large number of design-dependent variables that affect the forward model differently. Alternatively, the shape optimization approach used in this work produces binary designs naturally by morphing conformal meshes, circumventing inaccuracies associated with fuzzy interfaces. Shape sensitivities are computed via the adjoint method and leveraging automatic differentiation. The optimization problem is solved using a nonlinear programming technique.In this presentation, we perform shape optimization on a half-cell model to maximize its energy storage when a fixed charging current is applied. We study multiple arrangements of physical parameters and compare their performance against a monolithic electrode. Both 2D and 3D results are presented.

Electrochemical-thermal analysis of large-sized lithium-ion batteries: Influence of cell thickness and cooling strategy in charging

With the transition to electric propulsion accelerating, lithium-ion batteries (LIBs) are essential due to their high energy density and efficiency. However, higher energy requirements and rapid charging pose significant safety and performance challenges, particularly for large cells. This research uniquely investigates the thermal and electrochemical responses of large-sized prismatic LiFePO4 cells under various cell thicknesses (ranging from 11.3 mm to 90 mm), charging conditions (ranging from 0.3C to 1C), and cooling strategies. The model reveals that higher charging rates markedly increase temperature and variability within the cells. Charging a 45 mm-thick cell at a 1C rate and with natural air cooling results in a temperature rise of 24.3K and a deviation of 6.18K. In contrast, charging at 0.3C results in 5.4K and 1.17K, respectively. Despite the smaller heat exchange surface, bottom-cooling is most efficient for thick cells due to significant anisotropic heat conduction. Single-side cooling becomes more effective for thinner cells. A critical cell thickness for such transition exists at around 22.5 mm. Double-side cooling consistently outperforms single-side cooling, significantly reducing temperature deviations across various cell thicknesses. Best cooling performance is seen in the case of the thinnest cell (11.3 mm) with double-side cooling, with only 3K temperature rise and 1K deviation, at 1C charging. Additionally, improved thermal management during charging slightly increases heat generation of the cell, due to the higher resistance at lower temperatures, elevating ohmic heat generation from separators and electrodes. Polarization heat from the negative electrode is identified as the dominant heat source, driven by increased overpotential at higher temperatures due to elevated Li ion concentration at the electrode surface. The findings provide crucial insights into the thermal and electrochemical behaviors of large-sized LIBs, offering valuable guidance for the design and management of these LIB systems to enhance safety, performance, and longevity in electric vehicle applications, addressing a pressing need in the field.