Li-ion Battery Module Research Articles

Automated Upscaling Via Symbolic Computing for Thermal Runaway Analysis in Li-Ion Battery Modules

Despite the availability of advanced numerical capabilities and computational power, complex multi-physical, multiscale systems continue to challenge modeling efforts due to their elusive behaviors and nontrivial couplings. High-fidelity simulations have paved the way for simulating such systems, but the associated computational costs prohibit such tools from being used in iterative routines for engineering design and optimization. While numerical strategies for accelerating complex at-scale multi-physical simulations exist, they often concede predictive accuracy and lack rigorous justification. Alternatively, methods for developing rigorous multiscale models (e.g., upscaling techniques) offer a priori modeling error guarantees, but are primarily used in academic settings due to their requirements in time and specialized expertise for handling analytically intractable derivations. In our previous work, we developed an automated upscaling engine, Symbolica, that uses symbolic computation to avoid these overheads and rapidly develop multiscale models via rigorous homogenization theory. In this work, we extend Symbolica’s model development capabilities to accommodate multi-domain structures and demonstrate Symbolica’s generality in model development by applying it to a real-world problem; namely, to analyze thermal runaway in Li-ion battery modules. We obtain homogenized models that capture the sharp spatiotemporal gradients associated with thermal runaway to within the guaranteed modeling error. Upon numerically validating the models, we demonstrate how Symbolica can be used to reduce the level of effort in rigorous multiscale modeling and model implementation for real-world applications. Figure 1

Mechanical vibration's effect on thermal management module of Li-ion battery module based on phase change material (PCM) in a high-temperature environment

PCM-based systems play a crucial role in managing the thermal conditions of electric vehicle (EV) battery modules, a critical aspect in ensuring their optimal performance. However, an often overlooked facet in current research is the impact of mechanical vibration on EV battery modules during real-world driving scenarios. Addressing this gap, the current article takes a pioneering approach, drawing inspiration from the groundbreaking study by Du and Chen in 2023. This study delves into the effects of mechanical vibration on a PCM-based Battery Thermal Management System (BTMS) specifically adapted for battery modules operating in high-temperature environments. The results, shedding light on a previously underestimated factor, unveil the remarkable ability of mechanical vibration to ameliorate the surge in temperature within the battery module, particularly under high discharge rates. An unprecedented revelation surfaces as mechanical vibration emerges as a factor influencing the thickness of the encapsulated PCM, offering a unique avenue for augmenting the energy density of the powertrain. In this challenging thermal environment, mechanical vibration fosters temperature uniformity among batteries and curtails heat accumulation within the battery module. Remarkably, when the vibration amplitude surpasses a critical threshold, the impact of amplitude variations becomes negligible. Moreover, the intricate relationship between vibration frequency and temperature change complicates the scenario, with medium to high frequencies demonstrating a propensity to enhance the management system's overall thermal performance. These groundbreaking findings transcend the theoretical realm, offering tangible insights with immense implications for practical design and application. By recognizing and harnessing the potential benefits of mechanical vibration, engineers and designers can optimize BTMS, paving the way for more efficient and resilient EV battery modules.

Editor's Note for article entitled “Control of temperature distribution for li-ion battery modules via longitudinal fins”

Editor's Note for article entitled “Control of temperature distribution for li-ion battery modules via longitudinal fins”

A particle filter-based approach for real-time temperature estimation in a lithium-ion battery module during the cooling-down process

Temperature exerts a remarkable influence on the safety, performance, and lifespan of lithium-ion (Li-ion) batteries. Consequently, the real-time monitoring of this variable within battery cells emerges as both crucial and a significant challenge for battery thermal management systems. This paper introduces an approach for real-time temperature estimation in a Li-ion battery module during the cooling-down process by applying the particle filter (PF) technique. A battery module comprising fifteen cylindrical lithium cobalt oxide cells connected in series was employed for experimentation. The battery module was arranged into three rows, the PF employs real-time temperature measurements from the first cell of each string, based on a fractal-time model and an artificial evolution approach for parameter estimation, to estimate two unknown parameters and the temperature of the remaining twelve cells. Furthermore, the temperature measured on the surface of each cell in the module was compared both the PF and computational fluid dynamics (CFD) approach. The results exhibit a favorable agreement between the cell temperature estimates derived from the PF, the CFD simulations, and their respective experimental measurements. Finally, the proposed approach facilitates real-time temperature monitoring in battery modules with a reduced number of temperature sensors, thereby implying a cost reduction in the data acquisition system.

Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

An experimental investigation of forced convective liquid immersion cooling of a 12-cell li-ion battery module

This study details an experimental investigation of forced convective liquid immersion cooling of lithium-ion batteries. Twelve Samsung INR 18650 20S cylindrical cells, each with a nominal voltage of 4.2 V and nominal capacity of 2 Ah, are placed in a 4-in-series, 3-in-parallel arrangement inside a polycarbonate chamber. The cells are immersed in the dielectric fluid SF-33 and are discharged at C rates in the range of 1C to 4C. Temperature sensors placed along the body of each cell near the electrodes monitor their thermal behaviour during experiments. Results show that liquid immersion cooling is an effective method to maintain all cell temperatures within the desired temperature range of 15°C to 35°C for all cases tested, maintaining temperature differences within individual cells to ≤1.5°C, and those between cells to ≤3°C. This research contributes to the understanding of thermal management strategies for lithium-ion batteries, particularly in scenarios involving high discharge rate applications.

Experimental study on cooling performance of cylindrical 6S5P lithium-ion battery module with phase change material

The performance of the Li-ion battery module (6S5P) with composite PCMs is investigated for its cooling behavior. To transfer the heat generated by the battery module, paraffin (PCM1) and Granular Paraffin (PCM2) with copper sintering are used. The battery module consists of 30 cells with a capacity of 13 Ah, and the nominal voltage is 22.2 V. The performance of battery modules is studied at a discharge rate of 1C with different cooling approaches viz natural cooling, forced cooling, PCM cooling, and PCM + Copper sintering cooling. The composite PCM is prepared by the sintering method to control the temperature achieved during the solidification of copper. It has been observed that the addition of copper sintering to the PCM enhances the cooling performance of battery modules. The thermal efficiencies obtained in different cooling approaches, viz natural cooling, forced cooling, PCM1, PCM1 + Cu, PCM2, and PCM2 + Cu efficiencies are 9.15 %, 11.04 %, 30.35 %, 38.92 %, 48.38 %, and 54 %, respectively. The results indicate the importance of PCM-based cooling in improving the cooling efficiency of lithium-ion battery packs, particularly when sintered with copper compared with natural and forced cooling in terms of maximum temperature (Tmax) and maximum temperature difference (ΔTmax).

State-of-Charge Evaluation for Real-Time Monitoring and Evaluation of Lithium-Ion Battery Performance

Nowadays, lithium-ion batteries have been garnered significant attention as the primary energy source for energy storage devices within the renewable energy sector. Key concerns surrounding the utilization of lithium-ion batteries include ensuring satisfactory design lifespan and safe operation. Consequently, there's been a practical need for battery management. Responding to this demand, various battery state indicators have seen widespread implementation. Among the battery state indicators, accurate state-of-charge (SOC) estimation is an essential requirement for many situations where Li-Ion batteries (LiBs) are used. The effectiveness of a Battery Management System (BMS) safeguards the battery against deep discharging and over-charging to maximize its lifespan. This paper conducts state of charge (SOC) evaluation of a Li-ion battery module (12V, 13 Ah lithium titanate oxide (LTO) battery) for battery management systems (BMS) in energy storage systems (ESSs).

A Digitalized Methodology for Co-Design Structural and Performance Optimization of Battery Modules

In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today’s optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs are optimized simultaneously in this study by coupling 3D multi-physical behavior models to multi-objective heuristic optimization algorithms. Heat generation at various loading and ambient conditions are estimated with a physics-based, fractional-order battery cell-level model, which is extrapolated to a module that further accounts for the interconnected cells’ heat transfer phenomena. Several key performance indicators such as the surface temperature increase, the temperature variations on the cells, and heat uniformity within the module are recorded. For the air-cooled study case, the proposed coupled framework performs more than 250 module evaluations in a relatively short time for the whole available electro-thermal-mechanical design space, thereby ensuring global optimal results in the final design. The optimal module design proposed by this method is built in this work, and it is experimentally evaluated with a module composed of 12 series-connected Li-ion NMC/C 43Ah prismatic battery cells. The performance is validated at various conditions, which is achieved by accounting the thermal efficiency and pressure drop with regard to power consumption improvements. The validations presented in this study verify the applicability and overall efficiency of the proposed methodology, as well as paves the way toward better energy and cost-efficient battery systems.

Assessing battery degradation as a key performance indicator for multi-objective optimization of multi-carrier energy systems

The Pareto frontier is extensively adopted in multi-objective optimization, especially in multi-carrier energy system modeling. Despite the various methodologies available to derive the frontier, it represents different optimal solutions, making the final selection non-trivial. The modeler’s expertise is crucial in determining the weight factors assigned to each objective for selecting the final solution from the Pareto frontier. This study proposes a novel approach to support such decision-making, introducing an additional key performance indicator, the state of health of the battery, evaluated through physical battery modeling. By comparing different scheduling schemes in multi-objective multi-carrier energy systems, each with its distinct battery operational strategy, this newly introduced indicator has deployed to automatically identify the ultimate solution from the Pareto frontier, without additional weighting coefficients. Such an approach, therefore, automates the decision process, which supports easy engineering, especially for the small scale multi-energy systems such as smart homes, like the case study presented in this work that has four distinct energy carriers, adopting the 12 V 128 Ah LFP chemistry Li-ion battery modules, demonstrates the effectiveness of this automated selection process. Furthermore, when compared to the maximum values across the entire frontier, the automatically chosen solution exhibits reductions of 27.96% in CO2 emissions and 3.67% reduction in overall costs. Over long-term operation, this approach has the potential to extend battery lifespan by up to 26.67%, directly impacting the economics of multi-carrier energy systems.

Numerical and experimental validation of fiber metal laminate structure for lithium-ion battery protection subjected to high-velocity impact loading

A novel structural protection based on fiber metal laminate (FML) was developed for a lithium-ion battery module to address ground impact issues in electric vehicles (EVs). To verify the battery structural protection and for experimental validation, a new high-velocity impact test methodology was proposed. FML was selected for its high strength-to-weight ratio. The design implemented was the optimum configuration identified in a prior study. Finite element models comprising FML and lithium iron phosphate batteries were developed. The numerical simulation results, which correlated well with the experiments, enabled observation of the perforation process and failure modes of the FML. In the experiment, the safety valve of the center battery in the module experienced a compression of 3.28 mm. Despite this deformation, the FML effectively protected the lithium-ion batteries, as evidenced by the stable battery voltage during the high-velocity impact test.

Mitigation strategy for Li-ion battery module thermal runaway propagation triggered by overcharging

The popularity and development of alternative energy vehicles have drawn critical attention to thermal safety concerns. The occurrence of fire and explosion due to thermal runaway propagation (TRP) has become a hurdle in developing new energy vehicles. Therefore, to study the TRP behavior of modules subjected to overcharged electrical abuse, we built a 1 × 5 pouch Li-ion battery module TRP model that combines a P2D electrochemical model and a 3D thermal runaway model. We present a complete analysis of TRP characteristics by examining TRP behavior under various design parameters such as thermal runaway trigger temperature (TTR), cell spacing (L), thermal conductivity (K), and heat dissipation coefficient (h). Increasing TTR can delay TR in each cell, effectively slowing the propagation speed, and TRP is prevented at TTR = 583 K. As L increases, the TRP is considerably extended, but TR in the overcharged cell is shortened. The higher K has a delaying impact on the TR of the overcharged cell but leads to a noticeably shorter TRP time. Higher h slows TRP and prolongs TR trigger time of the overcharged cell. We also investigated the intrinsic mechanism of TRP suppression by performing a heat flux analysis and screening several critical cases for TRP suppression. TRP's actual condition is delineated along a criticality line, distinguishing between propagation and non-propagation phases, and confirmed by experimental validation of typical TRP, critical TRP, and non-TRP cases. Furthermore, we studied the parameters requirement for battery module thermal management in the non-TRP region. These findings provide valuable insights for enhancing the safety design of battery systems and mitigating heat-related hazards.

A novel compact thermal management model for performance evaluation of tesla-like lithium-ion battery packs

This study presents a compact thermal management model for Li-ion battery packs, with a specific focus on Tesla Model S vehicles. The model, which utilizes a detailed thermal resistance network, provides faster results than traditional Computational Fluid Dynamics (CFD) tools, making it approximately 10.5 times faster without compromising on comprehensive assessment. Key factors such as the C-rate and initial fluid temperature were found to significantly impact battery temperature and the maximum temperature difference. For instance, as the C-rate increases from 0.5 to 5, the average battery temperature rises by 84.5 °C, a condition that would lead to thermal runaway in practical scenarios. Meanwhile, an increase in the initial fluid temperature from 15 °C to 30 °C results in an approximate increase of 13.2 °C in the average battery temperature. The coolant velocity also plays a crucial role in managing heat dissipation from the batteries. The model, offering easy customization and reduced computational complexity, is ideal for modeling and simulations of Li-ion battery modules and packs. This research significantly contributes to improving the efficiency and safety of Li-ion batteries, particularly in electric vehicles, providing valuable insights for the design and analysis of battery thermal management systems (BTMS).

Uncertainty assessment method for thermal runaway propagation of lithium-ion battery pack

To some extent, the thermal runaway issue pertaining to lithium-ion battery still present challenging problems, to put the safety-related issue under control in high-power and high-energy applications. One of these challenging issues relates to the underpinning stochastic uncertainties associated with thermal runaway propagation. Therefore, this study proposes a methodology to evaluate the uncertainty of thermal runaway propagation in Li-ion battery modules. Firstly, a numerical thermal runaway propagation model is developed for phase change material-based thermal management system and verified by experiments. Subsequently, the Adaptive Kriging High-dimensional model representation method is employed to construct a surrogate model, followed by global sensitivity analysis to evaluate the impact of six different influencing factors on thermal runaway propagation. Finally, the random fluctuation of the critical factors affecting thermal runaway propagation is integrated to analyze and quantify the uncertainty. The results show that the state of charge, the thermal conductivity of phase change material, and inter-cell spacing are critical factors affecting thermal runaway propagation. With the system parameters proposed in this paper, the critical state of charge range that significantly impacts the uncertainty of thermal runaway propagation is approximately 40% to 55%. The analysis of the propagation results and the cell temperature indicates that the highest level of uncertainty occurs at around 45% state of charge. In this case, the thermal runaway propagation is significantly impacted by changes in key factors, and the battery temperature exhibits high fluctuation levels. Furthermore, although there is a 59% likelihood of no propagation, the potential damage to all batteries becomes exceedingly significant once thermal runaway propagation occurs. The method proposed in this study effectively evaluates the uncertainty of thermal runaway propagation in battery systems. It identifies the causes of the uncertainty, which can be used to implement effective suppression measures to enhance safety.

Experimental and numerical investigation on the effect of cell arrangement on thermal runaway propagation in air cooled cylindrical Li-ion battery modules

Experimental and numerical investigation on the effect of cell arrangement on thermal runaway propagation in air cooled cylindrical Li-ion battery modules

Computer vision application for industrial Li-ion battery module disassembly

Automated robot-assisted disassembly is essential for the flexible disassembly of Li-ion battery modules for economic and safety reasons. In such a case, a CAD model for the planning process is of immense benefit. The geometric uncertainties due to the breathing of the Li-ion cells as well as the presence of component tolerances underline the importance of a sensor-based detection approach to determine the actual state of the battery module, which is crucial to ensure an automated and reliable disassembly process. In this paper, we present a method for 3D camera-based localization of points on deformed battery modules, aiding in identifying support points for milling operations in robot-assisted disassembly cells. This separation operation planning employs a CAD model, and our introduced computer vision “data processing pipeline”—a systematic series of processing steps—bridges the gap between the CAD model and the actual battery module. This involves capturing the module using a 3D camera and subsequently registering its points with the CAD model’s points. Central to this process are two algorithms: The Bayesian Coherent Point Drift (BCPD) algorithm ensures accurate non-rigid registration, while TEASER++ aids in reducing computational time. We demonstrate the effectiveness of these combined algorithms in our pipeline through rigorous testing and metrics, evidencing that a balance between accuracy and computational speed can be attained by adjusting point density.

Design of novel thermal management system for Li-ion battery module using metal matrix based passive cooling method

Reliable, efficient and safe energy storage is important for electric vehicles and renewable energy storage of power grid. Lithium-ion battery is preferred as energy storage device due to its higher energy density, low self-discharge and longer cycle life. Lithium-ion cells generate heat during high discharge rates and harsh ambient temperature operation, which raises cell temperature and impacts its performance. Therefore, an efficient thermal management system (TMS) is necessary to alleviate thermal issues during charge/discharge process. A combination of phase change material (PCM) with high conductive plate metal matrix is proposed to eliminate the limitation on the heat transfer characteristics of air-cooled systems. To analyze the thermal and electrochemical behavior of a Lithium-ion cell, a detailed CFD simulation with empirical NTGK electro-chemical model is used. The model is first validated with available experimental data and the analysis is then extended to predict thermal response of individual cell of 3s3p module under natural convection boundary conditions. Poor heat transfer capability associated with natural convection at elevated temperature and discharge rate causes the cell maximum temperature (Tmax) and uniformity (ΔT) to be higher than acceptable values of 45 °C and 3 °C respectively. A thermal management system based on passive cooling using PCM is proposed and analyzed using CFD model by first validating the PCM melting behavior under uniform heating. The model is then extended to analyze the impact of PCM on cell temperature rise at different discharge rates of 8 A - 25 A (≈1C - 3.2C) and various operating temperatures of 25°C to 35°C. Low thermal conductivity of PCM limits heat dissipation which leads to higher temperature gradient. A novel TMS based on plate metal matrix structure enclosing PCM is proposed to enhance heat conduction across PCM and increase heat dissipation to maintain battery module under safe operating temperature limits. This approach has brought down the module maximum temperature to 44.1 °C and temperature difference to 1.6 °C during operation at 25 A (3.2C) and 35 °C. Hence, PCM with high latent heat and low thermal conductivity could be used with high conductivity plate metal matrix to improve cell performance by keeping the cell temperature at required range.

Automated upscaling via symbolic computing for thermal runaway analysis in Li-ion battery modules

Despite the availability of advanced numerical capabilities and computational power, complex multi-physical, multiscale systems continue to challenge modeling efforts due to their elusive behaviors and nontrivial couplings. High-fidelity simulations have paved the way for simulating such systems, but the associated computational costs prohibit such tools from being used in iterative routines for engineering design and optimization. While numerical strategies for accelerating complex at-scale multi-physical simulations exist, they often concede predictive accuracy and lack rigorous justification. Alternatively, methods for developing rigorous multiscale models (e.g., upscaling techniques) offer a priori modeling error guarantees, but are primarily used in academic settings due to their requirements in time and specialized expertise for handling analytically intractable derivations. In our previous work, we developed an automated upscaling engine, Symbolica, that uses symbolic computation to avoid these overheads and rapidly develop multiscale models via rigorous homogenization theory. In this work, we extend Symbolica’s model development capabilities to accommodate multi-domain structures and demonstrate Symbolica’s generality in model development by applying it to a real-world problem; namely, to analyze thermal runaway in Li-ion battery modules. We obtain homogenized models that capture the sharp spatiotemporal gradients associated with thermal runaway to within the guaranteed modeling error. Upon numerically validating the models, we demonstrate how Symbolica can be used to reduce the level of effort in rigorous multiscale modeling and model implementation for real-world applications.

Development of a Hybrid Vehicular Prototype with H2/air PEMFC Stack and Li-Ion Battery Module

Abstract. Recent years have marked a huge increase in worldwide efforts to improve the design and manufacture of miniaturized low- and high- power Polymer Electrolyte Membrane Fuel Cells (PEMFCs) for diverse electronic applications, ranging from stationary backup power to mobile transportation, and even as energy sources for a new generation of drone and hybrid vehicular transportation technologies. This communication compiles recent contributions developed by our group of researchers who has worked in the synthesis and characterization of Pt-based catalysts for oxygen reduction reaction (ORR) and in to address the performance and durability of H2/O2 PEMFC - Li Ion batteries in an electric vehicular transport prototype. We will provide an overview of our current contributions in the design, manufacturing, and development of low temperature, high-energy H2/O2 fuel cells which generated electricity is storage in a set of Li-Ion batteries for a vehicular transport prototype, Cinvestav-Sicarú, with more than an hour and thirty minutes of autonomy. Resumen. Los últimos años han marcado un aumento sustancial en los esfuerzos mundiales para mejorar el diseño y la fabricación de celdas de combustible de membrana de electrolito de polímero (PEMFC) miniaturizadas de baja y alta potencia para diversas aplicaciones electrónicas, que van desde energía de respaldo estacionaria hasta transporte móvil, e incluso como energía. Fuentes para una nueva generación de tecnologías de transporte de drones y vehículos híbridos. Este trabajo recopila contribuciones recientes desarrolladas por nuestro grupo de investigación que ha trabajado en la síntesis y caracterización de catalizadores basados ​​en Pt para la reacción de reducción de oxígeno (ORR) y para abordar el rendimiento y la durabilidad de una celda de combustible H2/O2 – banco de baterías de Iones de litio en un prototipo de transporte vehicular eléctrico. Proporcionaremos una descripción general de nuestros desarrollos actuales en el diseño, fabricación y desarrollo de celdas de combustible H2/O2 de alta energía y baja temperatura que generan electricidad y se almacenan en un conjunto de baterías de iones de litio para un prototipo de transporte vehicular, con más de una hora y media de autonomía.

Uncertainty analysis of stack pressure in EV battery module systems using a phenomenological modeling approach

Li-ion battery module systems, which are utilized to power electric vehicles (EVs), consist of a collection of battery cells that generate the necessary electrical energy and a structure designed to protect the battery system's interior components. However, a swelling effect, which results from internal composition changes that arise due to electrochemical reactions, causes the volume of the battery cells to change as the battery status changes (e.g., state of charge (SOC) and degradation). This volumetric change results in stack pressure evolution within the battery module, which leads to structural deformation and, eventually, failure of the module system. The objective of this research is to develop a method to reliably identify the stack pressure in the module level, considering the uncertainty in the system. A phenomenological model is employed to simply and statistically represent the complex mechanical behavior of the battery cells. An equivalent mechanical model is implemented to estimate the stack pressure within the module system. This paper presents three case studies that examine and compare the distributions of the stack pressure under different designs, utilizing the proposed method. The results of these case studies highlight the importance of uncertainty analysis in the design process to ensure robustness and reliability while maintaining fixed costs.