Battery Thermal Model Research Articles

Numerical Analysis of a Two-Layer PCM Based Battery Thermal Management System for Different Material Properties

The design and numerical analysis of the two-layer PCM (Phase Change Material)-based thermal management system for a 18650-type lithium-ion battery have been performed. In relation to simulation, the coefficient of thermal conductivity and melting temperature of the first layer of PCMs are varied. Other parameters are made identical to that of the next layer's parameters in order that the generation of two different layers of PCMs can be attained: PCM-1 and PCM-2. To obtain a more realistic approach in the numerical analysis, the battery thermal model was created in the COMSOL-MATLAB interface using the experimental internal resistance data obtained for 18650 type Li-ion batteries in the literature. While a cheaper and more accessible material with a thermal conductivity of 0.2 W/mK and a melting point of 50 °C was used in the PCM-2 layer, the thermal conductivity was changed to 0.2; 1 and 5 W/mK in the PCM-1 layer; and the melting point was changed to 30, 40 and 50 °C. In this way, for PCM layers with different thickness (tpcm), the system was optimized at two different discharge rates, 5C and 7C. As a result of the numerical analysis, it was determined that the optimum tpcm, kpcm,1 and Tm values for the 5C discharge rate were 2 mm, 0.2 W/mK and 40 °C, respectively; and the optimum tpcm, kpcm,1 and Tm values for the 7C discharge rate were 4 mm, 5 W/mK and 40 °C, respectively.

Thermo-electric behavior analysis and coupled model characterization of 21,700 cylindrical ternary lithium batteries affected by cyclic aging

In order to achieve a balance between the precision of thermal behavior simulation of lithium batteries affected by cyclic aging and the practicality of engineering popularization and application, a simple degraded battery thermal model needs to be constructed. In this article, a characterization approach for the coupled battery thermo-electric model affected by cyclic aging is designed. This method is suitable for simulating the thermal behavior of lithium degraded batteries with different materials and shapes under different environmental temperatures. This paper introduces the idea by taking the 21,700 cylindrical ternary lithium batteries as an example. Firstly, based on the interaction mechanism between the growth of the solid electrolyte interface film on the battery negative electrode surface at the microscopic level and the battery thermoelectric coupling characteristics at the macro level, the paper constructs a theoretical model of the degraded battery. Further, it conducts experiments to analyze the battery charging and discharging behaviors in the process of cyclic aging. Based on experimental data, this paper conducts multiple fitting calculations to extract essential modeling parameters. Subsequently, this paper builds the battery physical model in the simulation software based on the above modeling parameters. It applies the battery physical model to simulate the thermal characteristics and temperature field. Then, it conducts experiments to demonstrate the precision of the model. This paper uses the infrared imaging technique to visualize and analyze temperature field variations on the battery surface. And it uses thermocouple temperature sensors to capture the battery surface temperature changes. The simulation results are compared with the experimental data, the errors are less than 5 %. Compared with other existing battery thermal models, the model of this paper is more suitable for engineering popularization and application of thermal behavior simulation of lithium batteries affected by cyclic aging.

Kinetics study on inhibiting battery thermal runaway using an inorganic phase change material with a super high thermochemical storage capacity

Lithium-ion batteries are susceptible to fires and explosions due to thermal runaway, a serious safety hazard. This study explores the potential of using hydrated inorganic salt (TCM40) composite phase change materials to prevent thermal runaway in battery packs. TCM40 composites stand out due to their exceptional thermochemical heat storage capacity, which allows them to effectively absorb excess heat during runaway events. The research investigates how thermal conductivity, thermal storage capacity, and cell spacing influence the propagation of thermal runaway. The findings demonstrate that TCM40 composites, with a thermal storage density exceeding 1000 kJ/kg, are significantly more effective in preventing thermal runaway compared to traditional latent heat storage phase change materials with lower capacities. To gain a comprehensive understanding of thermal runaway mitigation, a combined thermal management model was developed. This model integrates a battery thermal runaway model with a kinetic model describing the decomposition of TCM40 composites. The analysis reveals that the high heat absorption capability of TCM40 composites minimizes heat transfer to neighboring cells during thermal runaway. Furthermore, the model provides valuable insights into the synergistic effects of thermal conductivity and heat storage capacity on runaway propagation. This knowledge can be directly applied to design safer battery packs, even for compact configurations where cell spacing is less than 2 mm. This study offers significant advancements in both thermal protection materials and design strategies for lithium-ion battery packs. These advancements have the potential to significantly improve battery system safety and minimize the risk of explosions.

Cargo E-Bike Robust Speed Control Using an MPC Battery Thermal Lumped Model Approach

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPC-integrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs.

Numerical simulation study on the impact of convective heat transfer on lithium battery air cooling thermal model

To enhance the accuracy of lithium battery thermal models, this study investigates the impact of temperature-dependent convective heat transfer coefficients on the battery’s air cooling and heat dissipation model, based on the sweeping in-line robs bundle method proposed by Zukauskas. By calculating and fitting the relationship between the convective heat transfer coefficient and temperature at flow rates of 0.05 m/s, 0.15 m/s, 0.25 m/s, and 0.35 m/s, it was found that the relationship is complex. An electrochemical-thermal coupling model was established using the operational characteristics of lithium batteries, and a thermal runaway reaction kinetics model was created using isothermal thermal runaway experiments and least squares optimization. The temperature-dependent convective heat transfer coefficient was then integrated into both models. Numerical simulations revealed that during normal discharge, the maximum temperature difference in the battery when the convective heat transfer coefficient is a function of temperature is less than 1 % compared to when it is constant. However, in the high-temperature thermal runaway model, the impact of temperature-dependent convective heat transfer coefficients on the thermal runaway critical parameters is minimal at flow rates of 0.05 m/s and 0.15 m/s. When the flow rate increases to 0.25 m/s and 0.35 m/s, the impact on the trigger time of thermal runaway is 17.34 % and 18.07 %, respectively. Experimental validation and research results indicate that the temperature effect on the convective heat transfer coefficient should be considered in high-temperature thermal runaway and thermal management models to calculate the convective heat transfer more accurately within the battery pack, improving model accuracy and reducing the risks of thermal runaway.

Novel hybrid BTMS by considering safety and driving cycle under extreme fast charge/discharge rates

A battery thermal management system integrating phase change material and mini-channels, with a focus on safety and economic considerations, is proposed to enhance thermal performance across various working conditions, safety scenarios, and realistic driving cycles while minimizing energy consumption. A battery thermal model and PCM simulation model were developed and validated using experimental data. Numerical analyses were conducted to compare the combination of PCM and liquid cooling (hybrid cooling) with fully liquid cooling under different charge/discharge rates and realistic driving cycles, evaluating their safety characteristics, power consumption, and thermal efficiency. The results demonstrated that the hybrid cooling approach can effectively maintain both the maximum temperature and the temperature difference within a desirable range, even when the inlet velocity is reduced from 0.12 to 0.05 m/s compared to liquid cooling. Additionally, the hybrid BTMS provides better working conditions for the battery pack, requiring only 4 × 10−5 W of pumping power compared to 16 × 10−5 W in the liquid cooling system. This underscores the capability of the designed BTMS to cool high-capacity battery packs with significantly lower fluid flow rates. Furthermore, the performance of the proposed cooling system was examined in the event of liquid channel failure, with results showing that the hybrid BTMS can successfully maintain the battery's maximum temperature within an allowable range during fast charge/discharge processes. Lastly, evaluations under realistic driving conditions revealed the superiority of hybrid cooling in reducing peak temperatures and minimizing battery temperature variation.

A novel instrumentation approach for achieving in-situ temperature sensing of a Li-ion cylindrical cell under immersion cooling

Battery thermal management systems are critical for high-performance electric vehicles, where the ability to remove heat and homogenize temperature distributions in single cells and packs are key considerations. Immersion cooling, which immerses the battery in a dielectric fluid, can improve cooling efficiency compared to air cooling. However, stringent instrumentation standards make in-situ monitoring under immersion cooling a difficult challenge. This study proposes a novel cell instrumentation method based on the integrated combination of three O-rings, which for the first time achieves in-situ temperature sensing of batteries under oil-immersed cooling conditions. Reliability of the sealing system and instrumentation methodology is ensured by leakage tests and oil corrosion assessment. Data such as cell impedance and discharge capacity also support the success and reliability of the instrumentation process without affecting the battery performance. Compared with natural air cooling, immersion cooling can reduce the temperature rise during battery operation by >10 °C under high C-rate discharge conditions. However, this is accompanied by an increase in radial temperature gradient. The temperature gradient of the cell with oil immersed reaches a maximum of >7 °C, while the temperature gradient under natural cooling under the same test conditions is only 3 to 4 °C. This leads to intensified temperature inhomogeneity of the cell, which may accelerate the local ageing and degradation. Our instrumentation approach contributes to a deeper understanding of the thermal behaviour of batteries under direct immersion cooling, aiding the development of more advanced cooling strategies and associated battery thermal management models.

Li-NMC Temperature Modelling Based on Realistic Internal Resistance

Lithium-ion battery (LIB) produce heat when it is put under charging and discharging process. The heat generated during charging and discharging are directly related to the internal in the battery. This heat generation will cause the battery temperature to rise. The operating temperature for LIB is significantly important because its affect the performance and health of the battery. Gathering battery thermal behavior through experiment is a time consuming, high cost and a fussy process. The process can be made easier through battery thermal modelling. The purpose of this study is to provide a thermal battery model that can predict the battery thermal behavior at wide range of temperature by using realistic internal resistance value from experiment. In this study, a Nickel-Manganese-Cobalt Lithium-ion battery with capacity 40 Ah was discharged with 120 A (3C) and 160 A (4C) current continuously to heat up the battery until a set of targeted temperature achieved. The battery is then discharged with 40 A (1C) pulse current, and the voltage response is measured. The process was repeated until 80°C. From the voltage response data, the internal resistance for the battery was calculated and used as the main input in the thermal model based on heat generation equation to predict the battery temperature. The result shows that the developed thermal model managed to precisely predict battery thermal behaviour with a low average relative error of around 0.634 % to 5.244%. The significance of this study is to provide a battery model that can predict battery thermal behavior precisely at wide range of temperature. This information is important in designing a better battery management system (BMS) to prolong the battery lifetime, slowing degradation rate and avoid safety risk.

Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization

AbstractLithium‐ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lithium‐ion battery thermal fault diagnosis model based on deep learning algorithms is presented, which includes three parts: autoencoder denoising network, coarse mask generator, and mask precise adjustment. Autoencoder denoising network can reduce data noise during thermal imaging acquisition, improve the anti‐interference ability of diagnostic models, and ensure the accuracy of thermal runaway diagnosis. A two‐stage diagnostic structure is then formulated by the coarse mask generator and mask precise adjustment, which enable quick identification, categorisation, and localisation of thermal fault battery cells. According to the test results, the segmentation boundary is more distinct and is capable of matching the original image's level. The recognition accuracy of the thermal diagnosis model for faulty batteries is close to 100%. After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.

A Study of Thermal Runaway Mechanisms in Lithium-Ion Batteries and Predictive Numerical Modeling Techniques

While thermal runaway characterization and prediction is an important aspect of lithium-ion battery engineering and development, it is a requirement to ensure that a battery system can be safe under normal operations and during failure events. This study investigated the current existing literature regarding lithium-ion battery thermal runaway characterization and predictive modeling methods. A thermal model for thermal runaway prediction was adapted from the literature and is presented in this paper along with a comparison of empirical data and predicted data using the model. Empirical data were collected from a Samsung 30Q 18650 cylindrical cell and from a large 20 Ah pouch cell format using accelerated rate calorimetry. The predictive model was executed in a macro-enabled Microsoft Excel workbook for simplicity and accessibility for the public. The primary purpose of using more primitive modeling software was to provide an accurate model that was generally accessible without the purchase of or training in a specific modeling software package. The modes of heat transfer during the thermal runaway event were studied and are reported in this work, along with insights on thermal management during a thermal runaway failure event.

Multi-objective optimization of liquid cold plate with v-shaped ribs for lithium-ion batterys

The performance of lithium-ion batteries is affected by the operational temperature significantly for the NEVs, and should be below 338 K and 5 K, respectively in the actual project. An efficient thermal management system is essential for the battery, as it would ensure the safe operation and increase the battery life. In this study, the LCP with V-shaped ribs is applied to improve the heat transfer characteristics for guaranteeing the safe operational temperature of the battery. Based on the battery thermal models, the accuracy of numerical simulation through classical experimental correlation is verified, and is adopted to investigate the effects of different design factors on the heat dissipation of the battery, including the ribbed shaped, the distance between adjacent ribs and the inlet velocity of the coolant. The maximum temperature and the temperature difference of the battery and the pressure drop of the channel are taken as the design objectives. An orthogonal test and an entropy weighted-TOPSIS method are used to optimize the results with multi-objective analysis, then the optimal case of design parameters is obtained. The optimal case for the LCP is the ribbed shape of Model 2, the distance between the adjacent ribs of 30 mm and the inlet velocity of 0.3 m/s. A good balance is achieved between the heat dissipation of the battery pack and the pressure drop of the channel. The optimal case can reduce the maximum temperature and the temperature difference of the battery by 7.41K and 4.94K compared with the unoptimized cases, meanwhile the pressure drop is also effectively controlled.

Research and application of heat recovery and automation monitoring system for new energy vehicle motor equipment

In order to improve the energy efficiency of pure electric vehicles, the author proposes a thermal energy recovery and automation monitoring system for motor equipment based on new energy vehicles. Based on the analysis of the battery thermal management model, a series heat exchange model between the motor and the battery was established. An integrated thermal management system control strategy based on motor waste heat recovery was proposed for electric vehicles in low temperature environments, and simulation and validation were conducted at different environmental temperatures (-10~0 ?). The simulation results show that this configuration can shorten the heating time by 111-343 seconds compared to traditional battery thermal management systems during the heating stage. During the insulation stage, frequent activation of positive temperature coefficient thermistor can be avoided and the battery temperature can be maintained around 20?C, which is not only conducive to extending the battery life but also reduces the comprehensive energy consumption by 4.39-7.70%. On the other hand, this configuration can reduce the impact of low temperature on comprehensive energy consumption from 9.77-2.07%. It is proved that heat recovery can effectively alleviate the range anxiety caused by ambient temperature.

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

(Invited) Methods for the Parameterisation of Battery Thermal Models

Thermal properties are fundamental parameters in every battery cell model. They govern how heat moves through the cell or dissipates once it has been produced. Modelling the movement of heat in batteries is essential for safety and also for accurate predictions of temperature. Electrical/electrochemical models of the cell are highly sensitive to temperature making accurate thermal properties an essential requirement. However, measuring these properties is extremely difficult due to the complex structure inside a cell. Previous measurement methods suffered from fundamental flaws and often battery thermal models are either over simplified or overly complex. This presentation will present the challenges faced before novel methods are presented which address this. This includes a novel method developed for measuring thermal conductivity which uses state-of-the-art heat flux sensors to reduce errors from up to 50% down to 5.6%. These novel methods represent the future for thermal parameterisation of lithium-ion batteries which will lead to accurate electrical/electrochemical models and improved thermal safety. Figure 1

(Invited) Estimating OCP-Temperature Dynamics for Determining Lithium-Ion Battery Entropy Coefficients

The open-circuit potential (OCP) of a li-ion cell is a function of both state-of-charge and temperature. Characterising its dependence on temperature is important for thermal simulations as the derivative of the OCP with temperature acts as a reversible heat source term in the model. Known as the entropy coefficient, this derivative is a thermodynamic quantity that varies over the cell’s state-of-charge (SoC). The potentiometric approach [1] is widely used to characterise the entropy coefficient for a given SoC. This involves applying step changes in the temperature and allowing the cell OCP to reach (or approach) a steady-state value, which can take several hours (>8 hours), for a given SoC of interest.Robust techniques are required that can significantly reduce the experimental time and quantify the entropy coefficient over the full SoC window, leading to higher fidelity battery thermal models. In this work, the dynamics between the OCP and cell temperature are exploited via system identification techniques to derive the entropy coefficient. This is achieved by estimating the underlying kernel function between the cell OCP and temperature dynamics. The kernel function is a non-parametric estimate of the dynamics from which the entropy coefficient can be determined.In this work temperature signals are designed with multiple frequency components and applied to a cell via Peltier elements. The corresponding OCP response is then analysed, in the frequency domain, to estimate the kernel function. Unlike the potentiometric method, which drives the battery to steady-state and necessitates long experimental durations, the kernel function can be estimated under transients, facilitating faster characterisation. The figure demonstrates comparable results of the entropy coefficient, over the full SoC interval, when estimated via the kernel function and the potentiometric approach. The approach, compared to the potentiometric method, however, brings around a two to three times reduction in experimental time per SoC and gives insight into the OCP and temperature dynamics. The signal design, kernel estimation and entropy coefficient estimation procedures are detailed in this talk. Schmidt, J. P., Weber, A., et al., Electrochimica Acta 2014, 137, 311-319. DOI 10.1016/j.electacta.2014.05.153 Figure 1

Li-Ion Pil Termal Parametre Tanımlama ve Çekirdek Sıcaklık Tahmini

Battery core and surface temperature are crucial for the thermal management and safety usage of Li-ion batteries. They affect the cell's physical properties and strongly correlate with some of its key states, such as the battery state of charge (SOC) and state of health (SOH). Therefore, an accurate estimate of the battery core and surface temperature will enhance the performance and prolong the battery's life. This study proposes an estimation system of the battery core and surface temperature. A simplified pseudo-two-dimensional model is introduced to capture the battery SOC, core and surface temperature that will be used later in this study to model and validate the results' accuracy. Then, a two-state thermal battery model (TSM) is presented and studied. The recursive least square (RLS) algorithm is adopted to identify the thermal parameters of the battery. Next, the TSM is validated using COMSOL Multiphysics simulation software and the thermal parameters are then fed to the Kalman filter (KF) to estimate the battery core temperature. Finally, the accuracy of the battery core temperature estimated results are validated with a root mean square error of 0.037K.

Analysis of ternary hybrid nanofluid in microchannel-cooled cylindrical Li-ion battery pack using multi-scale multi-domain framework

For cylindrical Li-ion battery packs, the battery thermal management system can be properly designed and analyzed from an energy-exergy viewpoint during charging and discharging to ensure the proper functionality of batteries; however, not been explored yet. Hence, cylindrical Li-ion battery pack with novel-designed serpentine microchannel-cooled thermal management system are analyzed. Applying the Multi-Scale Multi-Domain Newman, Tiedemann, Gu, and Kim model, an electrochemical model is used for the 3D battery thermal modeling. Effects of circular and rectangular cross-sectional shaped microchannels and using ternary hybrid nanofluids [THNF1 (Al2O3 (0.5%) + Cu (0.5%) + MWCNT (1%)/water) and THNF2 (Al2O3 (0.5%) + Cu (0.5%) + Graphene (1%)/water] are investigated at a 3C-discharge rate. Results indicate that the use of ternary hybrid nanofluid and microchannel cross-sectional shape has a strong influence. Maximum temperature, temperature uniformity, pumping power, heat transfer coefficient to pressure drop ratio, and entropy generation are the key factors. The present simulation can predict individual battery thermal behavior during charging and discharging. The suggested design successfully lowers the cell temperature to 305.24 K by using THNF2 and the temperature difference to 5.23 K of the 26,650-type cylindrical cell. Rectangular cross-sectioned microchannel is found to be more effective. The thermal control of batteries is greatly enhanced by using ternary hybrid nanofluid. The suggested technique will improve the battery life.

Correction: Liebig et al. Parameterization and Validation of an Electrochemical Thermal Model of a Lithium-Ion Battery. Batteries 2019, 5, 62

Mr. O. Zahid was not included in the acknowledgement section with respect toillustrations created from geometric and thermal battery cell data he generated during his master’s thesis study independent of the original publication [...]

Model-based battery thermal parameter optimization using symbolic regression

Lithium-ion battery packs are commonly used in electromobility and distributed energy storage. However, keeping both cell temperature and inter-cell temperature differences within operating ranges is important to avoid a negative impact on performance and lifespan. For this reason, modeling the thermal behavior of battery packs in different operating conditions is crucial for both design and practical use.In this work, we optimize the parameters of a battery thermal model using a symbolic regression method based on evolutionary algorithms. We obtain power-type expressions for the drag coefficient, friction factor, and heat transfer correlation (Nusselt number), as a function of the Reynolds number and the cell spacing factor, plus the Prandtl number in the case of the Nusselt number. The model is adjusted with CFD simulations of battery modules containing up to 102 cells to obtain the steady-state temperature distribution. Compared to CFD simulations, the proposed model gets a mean absolute percentage error of 2.39 % in estimating the temperature in a 53-cell battery pack. The adjusted parametric thermal model is validated with experimental data for cooling down a battery pack and a single cell, obtaining a mean absolute percentage error of 1.26 % and 4.68 %, respectively, in estimating the dynamic temperature balance.The adjusted parametric thermal model and the mathematical expressions obtained for the drag coefficient, friction factor, and Nusselt number, can be useful for designing battery thermal management systems in electromobility and distributed energy storage.

Hybrid battery thermal management system coupled with paraffin/copper foam composite phase change material

A battery thermal management system (BTMS) using a composite phase change material (CPCM) is proposed for both heat dissipation under fast charging conditions and preheating in low temperature environments. Based on the validated battery thermal model and CPCM model, a parametric study was performed to determine the optimal conditions. As a result, in the proposed concept, the highest maximum temperature and maximum temperature difference are 39.3 °C and 3 °C under 3C charging conditions at room temperature. This is achieved by decreasing the total operating time of the liquid cooling system by 35.2% compared to that of the conventional BTMS, and after the cycle, the latent heat is fully recovered for use in a continuous cycle. Also, under low temperature conditions of −10 °C, the final maximum temperature is 29.2 °C after preheating in the proposed BTMS. The temperature difference is 2.5 °C, which is significantly lower than when applying the existing liquid heating method. Therefore, the proposed BTMS is advantageous in terms of both the cooling performance under fast charging conditions and preheating under low temperature conditions.