Prediction Of Thermal Behavior Research Articles

Co-pyrolysis of coal-derived sludge and low-rank coal: Thermal behaviour and char yield prediction

Coal-derived sludge, a solid waste produced by the coal industry, offers potential opportunities for resource recovery due to its high organic matter. However, its products face challenges related to low utilization efficiency and economic value. Effective and clean treatment of coal-derived sludge is essential for sustainable development. Herein, we studied the co-pyrolysis treatment of coal-derived sludge and low-rank coal at different temperatures (500 °C−900 °C) and pretreatment methods (mechanical mixing and hydrothermal co-treatment). The co-pyrolysis of sludge and coal could increase the pyrolysis char yield and H2 yield, as well as reduce CO2 emissions. The hydrothermal co-treatment significantly improved the cleanliness of the co-pyrolysis treatment. Then we conducted a comprehensive analysis of the properties of the pyrolysis char using different characterization techniques. In order to better evaluate the distribution of co-pyrolysis product yield, six machine learning models were developed to predice co-pyrolysis char yield. The best model-predicted values showed excellent predictive performance when compared to the experimental values at high pyrolysis temperatures (≥700 °C). This study provided a new perspective on the resource utilization of coal-derived sludge and low-rank coal.

Enhancing the Accuracy and Generality of the Debye–Grüneisen Model: Optimizing the Volume Dependence for Accurate Predictions Across Varied Compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. The current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of Ni-based alloys without expensive phonon calculations.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

Editor's Note for article entitled “Prediction of battery thermal behavior in the presence of a constructal theory-based heat pipe (CBHP): A multiphysics model and pattern-based machine learning approach”

Editor's Note for article entitled “Prediction of battery thermal behavior in the presence of a constructal theory-based heat pipe (CBHP): A multiphysics model and pattern-based machine learning approach”

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior.

ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.

Prediction of thermal behavior of Earthen-Straw composite material with 3D numerical modeling of a random geometry without overlapping

Nowadays, stabilizing raw soil with straw fibers represents a sustainable eco-building practice. Moreover, the rebirth of the earth’s construction is primarily driven by the material’s thermal properties. In the present work, a 3D study of the earthen-straw composite material’s thermal behavior has been carried out to promote its use in construction and ensure satisfactory living conditions for the building. To consider the most possible influencing factors, the effect of fibers concentration on thermal conductivity was investigated for the straw contents of 5%, 10% and 15% by volume of earthen. Thus, a 3D model with a random distribution of straw fibers considered as cylinders without overlapping was designed under Catia V5. The distribution uses datasets from a hybrid algorithm developed for this purpose, which is then imported to Ansys Software to carry out the simulation allowing the computation of the thermal conductivity of the homogeneous material. It can be concluded that the proposed hybrid algorithm was efficient since the 3D numerical results based on the geometry generated by the algorithm’s output were satisfactory in comparison with those deducted from the 2D study and the existing theoretical models. On the other hand, adding straw fibers in the raw earth matrix with thermal conductivity equal to 0.75 W/(m.K) has enhanced its thermal insulation. Indeed, for 15% of straw fiber’s contents, we have found 0.633 W/(m.K) for the thermal conductivity of the earthen-straw composite material, which is equivalent to a decrease of 15.6% in this intrinsic parameter.

Thermal degradation evaluation of polyethylene terephthalate microplastics: Insights from kinetics and machine learning algorithms using non-isoconversional TGA data

The thermal stability of polyethylene terephthalate (PET) microplastics (MPs) is crucial in diverse applications, necessitating a comprehensive understanding of their behavior under high temperatures. This study explores the impact of chemical modifications and aging on the thermal degradation of different PET MPs, including pristine (Pr-PET), acid-modified (Mod-PET), and oxidatively aged (Ag-PET), using kinetics, thermodynamics, and machine learning algorithms using non-isoconversional thermogravimetric analysis (TGA) data. The calculated activation energy (E) and pre-exponential factor (A) for Coats-Redfern model showed Pr-PET with highest E value of 21.09 and 15.24 kJ/mol for zero-order and first order kinetic model, indicating its increased energy requirement for chemical reactions, while Mod-PET and Ag-PET demonstrated improved thermal resistance with lower E values. Thermodynamic data from kinetic parameters showed positive values of Gibbs free energy (ΔG°), enthalpy (ΔH°) and negative values of entropy (ΔS°) (−0.17. kJ/mol·K to −0.20 kJ/mol·K) indicated non-spontaneous degradation process, necessitated energy input, and reduced disorder with decreased reactivity. For accurate predictions of thermal behavior, various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Regressor (DTM), Random Forest Regressor (RFM), and Artificial Neural Network (ANN), were utilized. The RFM consistently outperformed other models, exhibiting the lowest error metrics and providing reliable predictions of weight loss and derivative thermogravimetric (DTG) for different PET MPs with a predicted-R2 of 0.999. This study sheds light on the thermal stability of PET MPs, considering chemical modifications and aging. The integration of these methods offers a powerful approach to understanding and predicting PET MPs under thermal conditions.

Prediction of high thermal rectification behavior in carbon/C3N heteronanotubes based on nonequilibrium molecular dynamics simulations.

Carbon/C3N heteronanotubes (CC3NNTs) have garnered significant interest for their distinctive performance and versatility across various applications. However, the understanding of interfacial heat transport within these heterostructures remains limited. This study aims to enrich the field by constructing models of CC3NNTs through the bonding of CNTs and C3NNTs, and employs nonequilibrium molecular dynamics (NEMD) simulations to predict their heat flux and thermal rectification (TR) effects. Placing the heat source in the CNT region induces a stronger heat flux compared to the C3NNT region, thus demonstrating a pronounced TR effect. This effect can be attributed to the mismatch in phonon spectra, as evidenced by the cumulative correlation factor derived from the phonon density of states (phonon DOS). Using this approach, we predict that the TR ratio for zigzag CC3NNTs (ZCC3NNT) significantly exceeds that of armchair CC3NNTs (ACC3NNT). Notably, in contrast to ACC3NNT, ZCC3NNT exhibits the phenomenon of negative differential thermal resistance in the backward heat flux with a temperature difference of Δ = 120 K. This phenomenon can be attributed to a lower phonon participation ratio at Δ = 120 K compared to other values of Δ. Subsequently, given that ZCC3NNT demonstrates the most pronounced TR ratio at room temperature, we explored how stress-strain, system size, defect density, and interface position impact the TR ratio. These insights are invaluable for guiding the design of thermal rectifiers, smart thermal management systems, and microelectronic processor coolers.

Quantifying the Effects of Temperature and Depth of Discharge on Li-Ion Battery Heat Generation: An Assessment of Resistance Models for Accurate Thermal Behavior Prediction

Li-ion batteries (LiBs) are widely adopted in electric vehicles (EVs) owing to their superior properties, such as high energy density, low discharge rate, long lifespan, and lightweight construction. Since the battery pack is the sole energy source for an EV, its performance is critical for optimal vehicle operation. However, the battery's calendar life, cycle life, and overall performance are significantly affected by temperature variations. The Li-ion batteries used in EVs may encounter challenging working conditions, leading to thermal problems such as significant capacity and power loss. In contrast, thermal runaways can occur at temperatures above a specific threshold, leading to severe health deterioration and sometimes catastrophic safety hazards such as fires and explosions. As the temperature significantly impacts Li-ion batteries, a battery thermal management system that can efficiently dissipate heat is crucial to ensure the battery's optimal performance and longevity. Hence, it is crucial to develop accurate algorithms for battery thermal management systems to precisely and dynamically estimate the temperature dynamics of the batteries integrated within the battery pack.While experimental data can be used to estimate battery temperatures, the dynamic and diverse operating conditions of electric vehicles (EVs) present a significant challenge. Therefore, accurately predicting thermal response within batteries is critical. Various thermal models have been developed to predict the thermal behavior of batteries and quantify the amount of heat generated. The simplified thermal model only considers joule heating and reversible entropic heating. However, more accurate physics-based models consider reversible heat caused by the side reactions, heat generated by mass transport loss, and even mixing-induced heat. The amount of heat generated inside a Li-ion battery is determined by its equivalent internal resistance, open circuit voltage, and entropy change, which are in turn influenced by temperature and depth of discharge (DoD). To the best of the authors' knowledge, previous research on the heat generation of Li-ion batteries has been limited in some respects. Specifically, there has been little investigation into the combined impact of temperature and depth of discharge (DoD) across a wide temperature range. Most studies have been conducted under ambient temperature conditions, and only a few have focused on high temperatures within a narrow range with low discharge rates.Thus, this study aims to address the research gap regarding the impact of temperature and depth of discharge (DoD) on heat generation in Li-ion batteries by analyzing these parameters using a transient battery thermal model. The research intends to improve the accuracy and precision of battery thermal behavior prediction, which has broad implications for battery-powered applications. This study aims to evaluate the impact of different resistance models on heat generation in Li-ion batteries, explicitly comparing a constant resistance model with a model that considers resistance as a function of temperature and depth of discharge (DoD). Investigating the interdependent impact of battery temperature and DoD on heat generation is crucial to create an accurate battery thermal model with high fidelity. The current study uses a two-dimensional battery thermal model to comprehensively analyze thermal behavior of a LiFePO4-20Ah Li-ion pouch cell. In this research study, heat generation in a Li-ion battery is evaluated by estimating the internal resistance and entropic change obtained from experimentation. The energy equation is then solved using the finite difference method in MATLAB to obtain the transient thermal response of the battery. The developed transient electrothermal model is validated against experimental data under varying C rates to assess the accuracy and precision of the proposed model. The simulation results show that the thermal response obtained considering the effect of temperature and DoD on heat generation shows more accurate results than the constant resistance values. The thermal behavior of a LiFePO4 pouch cell, considering constant values for heat generation, has a maximum relative error of roughly 19.99% compared to experimental data at a 4C discharge rate. While this maximum relative error was reduced to 6.29% when considering the effect of temperature and DoD on heat generation. In the constant resistance model, more significant errors can be attributed to the fact that the resistance of a Li-ion battery varies with the depth of discharge (DoD). While the initial discharge phase of the battery exhibits minimal changes in resistance values, a substantial increase in resistance occurs during the final stages of discharge. This contrasts with the actual behavior of Li-ion batteries, which demonstrate significant variations in resistance values throughout the discharge process. Thus, coupling the effects of DoD and temperature on heat generation is necessary to accurately predict the thermal behavior of Li-ion battery. Figure 1

Prediction of Melt Pool Dimension, Porosity Generation and Thermal Behavior of Ti-6Al-4V during Powder Bed Fusion at Various Scan Speeds and Laser Powers

The additive manufacturing technology called laser powder bed fusion enables to manufacture complex parts based on the fusion of a metallic powder layer by layer. In laser powder bed fusion, the produced component quality relies significantly on the parameters of the process. In this study, the powder titanium alloy Ti-6Al-4V is employed for the purpose of predicting the melt pool dimensions. To manufacture a single bead, several combinations of scan speed and laser power are used. This research studies the influence of the scan speed and the laser power on the melt pool dimensions and on the thermal history of a specified layer of powder. The results reveal that the geometry of the melt pool is considerably responsive to the scan speed and the laser power. Furthermore, unfavorable effects such as porosity defects are analyzed in detail. Suggestions are presented to employ optimal settings to prevent these undesirable outcomes. To validate the numerical results, a comparison with experimental results from the literature is carried out. Our numerical analysis proves a satisfactory correlation with the experimental investigations. The beam power and the scanning speed effects on the average temperature of the desired layers are discussed as well.

Analysis of dynamic thermal behaviors for multi-stage hydraulic fracturing treatments in horizontal shale oil and shale gas wells

Accurate prediction of dynamic thermal behaviors during multi-stage hydraulic fracturing is profound which can avoid performance deterioration of fracturing fluids or failure of fracturing tools caused by severe temperature variation. This paper establishes a transient heat transfer model for multi-stage hydraulic fracturing in horizontal shale oil and shale gas wells (MFHWM), considering the real construction process of multi-stage hydraulic fracturing, the turbulent flow state, the heat source of viscous dissipation and the complex wellbore structure, which enables the accurate simulation of thermal behaviors in the early time of multi-stage hydraulic fracturing with large flow rate. The verification results of measured temperature data and simulation data prove the MFHWM has good precision. Numerical simulations indicate that the temperature distribution exhibits the characteristics of cyclic “cool-down” and “warm-back” and “stair-step” during the multi-stage hydraulic fracturing. In the “cool-down” period, the temperature drops sharply within the first hour and then the deceleration slows down. In the “warm-back” period, the temperature recovers exponentially relying on the shut-in time. The “stair-step” is caused by changing of heat transfer mechanism from convection to conduction. Moreover, the cyclic cooling has little effect on the total temperature drop and subsequent stages are not significantly cooler than previous stages. Besides, primary factors affecting temperature distributions of wellbore-formation system are discussed thoroughly. Results indicate that the viscous heat source cannot be ignored for multi-stage hydraulic fracturing and the temperature difference exhibits cyclic variation when considering and without considering the viscous heat source. In addition, the minimum temperature during each-stage-fracturing operation decreases first and then increases as the injection rate of fracturing fluid increases because of the compromise between the viscous heat source and forced convection. Furthermore, temperature distributions can be controlled by changing the injection temperature of fracturing fluid. As the injection temperature increases, a positive proportional relationship exists between the minimum temperature of fracturing fluid and the injection temperature. MFHWM can be a standalone tool to analyze temperature distributions of multi-stage hydraulic fracturing. It can also be used to provide better boundary conditions for stress analysis model of casing and cement sheath.

Artificial intelligence approaches to predict thermal behavior of light earth cell incorporating PCMs: Experimental CNN and LSTM validation

The dynamic modeling of heat transfer in bio-based materials is a challenging endeavor due to the wide variety of factors that influence the thermal behavior of bio-based materials with Phase Change Materials (PCMs). There are a lot of factors related to heat transfer with phase change that are sometimes beyond our control. Since the phase change response of bio-based walls with PCM is significantly non-linear, in this paper, we present the thermal behavior prediction using artificial neural networks. Based on in-situ study of light earth incorporating PCMs, the effectiveness and training of Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are assessed. According to the findings, the results showed that these models can predict precisely the heat transfers in light earth with or without phase change materials. Compared to the LSTM, it was found that the CNN model is more accurate in predicting processes involving phase shift, such as the one presented by the light earth incorporating PCMs.

Thermal Behavior Prediction of Sludge Co-Combustion with Coal: Curve Extraction and Artificial Neural Networks

Previous studies on the co-combustion of sludge and coal have not effectively utilized the characteristics of the combustion process to predict thermal behavior. Therefore, focusing on these combustion process characteristics is essential to understanding and predicting thermal behavior during the co-combustion of sludge and coal. In this paper, we use thermogravimetric analysis to study the co-combustion of coal and sludge at different temperatures (300–460 °C, 460–530 °C, and 530–600 °C). Our findings reveal that the ignition improves, but the combustion worsens with more sludge. Then, we further employ curve extraction based on temperature and image segmentation to extract the DTG (weight loss rate) curves. We successfully predicted the DTG curves for different blends using nonlinear regression and curve extraction, achieving an excellent R2 of 99.7%. Moreover, the curve extraction method predicts DTG better than artificial neural networks for two samples in terms of R2 (99.7% vs. 99.1% and 99.7% vs. 94.9%), which guides the application of co-combusting coal and sludge.

Numerical modeling of dry cell alkaline electrolyzer for HHO production

It is essential to simulate HHO dry cell in order to save money and time. Modeling and prediction of electrical, thermodynamic, chemical, and thermal behaviors of an alkaline electrolyzer is the goal of this work. Alkaline water electrolyzer was designed and fabricated for oxyhydrogen production using NaOH electrolyte of 20 g/L by water electrolysis. MATLAB/Simulink was used to solve the model equations. This study investigates how the temperature affects I-U curve, overvoltage potential, gas flow rate, and energy efficiency at various temperatures. Enthalpy, Gibbs free energy, and entropy were decreased with the temperature rise. Due to the lower energy needed to initiate the reaction and the shrinkage of gas bubbles, the highest activation and ohmic voltages at current density of 300 mA/cm2 are seen at temperature of 40°C but the lowest at temperature 80°C. The highest voltage is shown at the lowest temperature of 40°C and specific current density of 250 mA/cm2. Once the current density reaches 250 mA/cm2, the activation voltage becomes constant. As the current density increases from 50 to 100 mA/cm2, Faraday efficiency increases from 86% to 94%. At temperature of 80°C, the energy efficiency is at its maximum value but it is low at 20°C. Alkaline electrolyzer produces the high rate of HHO with the least amount of consumed power when the temperature is at 80°C. The peak energy efficiency occurs at temperature of 100°C and the lowest energy efficiency at a temperature of 20°C. The error percentages between experimental and theoretical for oxyhydrogen flow rate and cell voltage are up to 6% and 4.1%, respectively, and this confirms the model validity.

Optimal Neural Network PID Approach for Building Thermal Management

The process of thermal control and regulation in buildings is considered complex. Its complexity lies in the various internal and external physical phenomena impacting its control, and also in the increasingly important requirements of occupant comfort, energy optimization and efficiency, and optimization of measuring and monitoring equipment. Recently, the establishment of technical rules for optimal building thermal control has gained interest in academia and industry. These rules have focused mainly on three aspects: the use of renewable energy, optimal management, and the use of equipment and materials allowing the optimization of energy. However, optimal control has not been addressed enough. In this article, we present a PID controller based on a Neural Network approach for thermal building management and control. The proposed approach is based on two processes: an optimal identification process dedicated to the thermal building behavior prediction impacted by variable and invariable elements, measured and unmeasured factors, and a control process to ensure the desired performance with optimal energy control. The results obtained show the advantages of the adopted system in terms of energy optimization, with an important energy gain of 8% to 11%, along with better regulation and control performance, and in terms of occupant comfort with minimal temperature variations.

Prediction of the early age thermal behavior of mass concrete containing SCMs using ANSYS

Prediction of the early age thermal behavior of mass concrete containing SCMs using ANSYS

A comprehensive coupled 0D-ECM to 3D-CFD thermal model for heat pipe assisted-air cooling thermal management system under fast charge and discharge

Prediction of electrical and thermal behavior of lithium-ion capacitor (LiC) technology as an asymmetric technology is feasible by designing a precise model. Such a model should mimic the behavior of LiCs in heavy-duty applications where high current rates are applied. The developed model is used to design a management system based on efficient modeling tools, including 0D (zero-dimensional) electro-thermal models and 3D computational fluid dynamics (CFD) thermal models. A validated model is essential for LiCs as they operate at high dynamic current rates. In this article, the 0D second-order equivalent circuit model is developed to extract the electrical parameters of LiCs. Then, the thermal model is developed to be linked to the electrical model to make an electro-thermal platform capable of identifying the electro-thermal parameters. The characterization tests are performed within a wide range of temperatures, from the freezing temperature of −30 °C to the hot temperature of + 60 °C. Such a temperature range has never been carried out before. The validation is performed based on the owned experimental results. The applied current rates are from 0.1 A to 500 A, which shows the work's uniqueness in the field of electro-thermal modeling. Later, the extracted parameters have been set as inputs to the 3D CFD thermal model to design and develop a hybrid thermal management system (TMS) based on air cooling and heat pipes. Such a hybrid TMS maintains the maximum temperature at 24.6 °C when the temperature difference between the hottest and coldest cells is only 0.5 °C.

Evaluation thermal degradation kinetics of ionic liquid assisted polyetheretherketone‐multiwalled carbon nanotubes composites

AbstractThe incorporation of multiwalled carbon nanotubes (MWCNT) into polyetheretherketone (PEEK) composites has emerged as a promising strategy for enhancing the thermomechanical characteristics of PEEK composite materials. This study investigates the thermal behavior and kinetics prediction of PEEK/MWCNT composites comprising different ionic liquids (ILs), namely 1‐butyl‐3‐methylimidazolium hydrogen sulfate ([BMIM]HSO4), 1‐butyl‐3‐methylimidazolium acetate ([BMIM]Ac), 1‐ethyl‐3‐methylimidazolium acetate ([EMIM]Ac) and 1‐ethyl‐3‐methylimidazolium hydrogen sulfate ([EMIM]HSO4). Three non‐isothermal methods Coats‐Redfern, Broido, and Horowitz‐Metzger, were employed to model the thermal decomposition profiles of fabricated composites to calculate the activation energy. The highest decomposition temperature (580°C) was obtained for [BMIM]HSO4‐based PEEK/MWCNT composites. Moreover, a 3%–8% increase in the activation energy was obtained compared to PEEK/MWCNT manufactured without ILs. The Coats‐Redfern model was superior to Broido and Horowitz‐Metzger models in modeling the thermal degradation of developed composites, as evidenced from the higher value of the coefficient of determination (R2 ≥ 0.9899). By determining the Root Mean Square Error (RMSE) and R2 for the thermal degradation kinetics data, the artificial neural network (ANN) model was employed. The ANN model accurately predicted the mass loss curves, exhibiting R2 ≥ 0.9815 for the designed model. These findings can assist in establishing an IL‐assisted benign approach for PEEK/MWCNT/IL composites with superior thermal characteristics.

Parametric Evaluation of Thermal Behavior for Different Li-Ion Battery Chemistries

The prediction of thermal behavior is essential for an efficient initial design of thermal management systems which equip energy sources based on electrochemical cells. In this study, the surface temperature of various cylindrical types of Li-ion batteries is monitored at multiple points during discharge. Three different battery chemistries and two sizes (18650 and 21700) are considered in this study, allowing the comparison of the influence these parameters have on the temperature rise considering different discharge rates (1C, 2C and 3C). Based on repeated experimental measurements, a simple equation that describes the thermal behavior of batteries is proposed and further used to create 3D thermal maps for each analyzed battery (generally error is below 1 °C but never exceeds 3 °C). The practical utility of such an equation is that it can drastically reduce the time spent with experimental measurements required to characterize the thermal behavior of cylindrical Li-ion batteries, necessary for the initial design process of energy sources’ thermal management system.

An electrochemical-thermal coupling model for prismatic lithium-ion batteries over wide temperature range

Accurate and rapid prediction of battery thermal behavior can provide accurate basis for making battery use plan, which is beneficial for alleviating the influence of thermal effect on battery life and safety. A fast numerical solution method of electrochemical-thermal coupling model for prismatic lithium-ion batteries is proposed in this work. Firstly, based on an existing electrochemical single particle model and one-dimensional thermal resistance network, analytical expression of thermal resistance network model in two-dimensional space is obtained by theoretical formula derivation. Then, to calibrate the model parameters at different temperatures, temperature range is divided into several intervals, and optimal parameter sets at different intervals are obtained by excitation response analysis method and multi-group particle swarm optimization algorithm. Finally, to realize the accurate numerical simulation of the model, a double adaptive extended Kalman filter method is applied to further optimize the parameters in real time. Seen from the simulation results over wide temperature range, mean absolute errors of voltage and temperature are less than 30 mV and 0.12 K at −5–55 °C respectively, which indicates the capability of the developed model for high-precision numerical simulation over temperature range for prismatic lithium-ion batteries.