Browsing by Subject "Lithium-ion batteries"

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    Lithium-ion battery module performance improvements by using nanodiamond-FE3O4 water/ethylene glycol hybrid nanofluid and fins
    (Springer Science and Business Media B.V., 2022) Dilbaz F.; Selimefendigil F.; Öztop H.F.
    Control of heat released during charge/discharge processes of lithium-ion batteries is very important for the improvement of efficiency of lithium-ion batteries. In this study, the thermal performance of a 20 Ah rectangular type battery pack is analyzed with two different cooling fluids, namely water and nanodiamond-Fe3O4 water/ ethylene glycol (ND- Fe3O4 W/EG) hybrid nanofluid. The cooling system has 5 to 25 number of fins while the Reynolds number is taken between 100 and 800. The volume fraction of the nanoparticles is between 0 and 2% while the discharge rates of 3C, 4C and 5C are considered. The findings suggest that increasing the Reynolds number and the nanoparticle volume ratio improves the temperature distribution and lowers the maximum temperature in the battery pack. When compared to water with the same Re number, ND-Fe3O4 + W/EG hybrid nanofluid with 2% volume ratio at 800 Re number provides improvement of maximum temperature by 23.1% while 70.35% improvement in temperature difference is achieved. Using higher number of fins improves the performance. There is a reduction of 13.8% of the temperature difference in the battery pack with 25 fin model as compared to 5 fin model. © 2022, Akadémiai Kiadó, Budapest, Hungary.
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    A review on soft computing and nanofluid applications for battery thermal management
    (Elsevier Ltd, 2022) Can A.; Selimefendigil F.; Öztop H.F.
    This study is about applications of nanofluids and various soft computing algorithms on designs of battery thermal management systems and their potential performance enhancement in cooling. Brief information on Li-ion batteries, energy storage process and cooling techniques such as passive, active and hybrid cooling techniques are presented. Basic knowledge on nanofluids and soft computing methods are explained to deep understanding the following chapters. Potential of using nanofluids on thermal management of battery packs and effect on their life cycles and performance improvements are discussed. Application of the most common soft computing methods in battery thermal management systems is presented. Li-ion batteries are a promising solution to energy storage issue with appropriate thermal management designs such as presented in this review. When different active and hybrid cooling battery thermal management systems are operated with nanofluids, their performances are increased. Different machine learning methods have been successfully used in battery thermal management systems and outputs from the modeling have been considered for further performance enhancement and optimization studies. Even though, they are excellent tools assisting in high fidelity simulations or expensive experimental testing of systems, deep learning and other advanced machine learning methods may be considered for future studies. Exergetic performance analysis of nano enhanced thermal management along with the cost of using nanofluids is needed as the extension of the current studies. © 2022 Elsevier Ltd
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    Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods
    (Springer Science and Business Media Deutschland GmbH, 2023) Taş G.; Bal C.; Uysal A.
    In this study, the GWO-BiLSTM method has been proposed by successfully estimating the SOC with the BiLSTM deep learning method using the hyper-parameter values determined by the GWO method of the lithium polymer battery. EV, HEV, and robots are used more healthily with successful, reliable, and fast SOC estimation, which has an important place in the Battery Management System. In studies using deep learning methods, it is important to solve the problems of underfitting, overfitting, and estimation error by determining the hyper-parameters appropriately. Thus, this study aims to solve an important problem by investigating the problem of determining the hyperparameter values for the deep learning method with metaheuristic optimization methods. This study was designed to compare the prediction success of the BiLSTM method trained with the optimal hyperparameter values obtained by the GWO method with cutting-edge deep learning methods trained with hyperparameter values obtained by trial and error. The success of the proposed method was verified by comparing the cutting-edge data-based deep learning methods and the BiLSTM method with the SOC estimation MAE, MSE, RMSE, and Runtime(s) metrics. According to the findings obtained during the hyperparameter determination studies, it takes longer time to determine the hyperparameters by trial and error than to determine the hyperparameters by metaheuristic optimization method when estimating lithium battery SOC with the deep learning method. Also, the GWO-BiLSTM method was the most successful method with an RMSE of 0.09244% and an R2 of 0.9987 values according to the average results of SOC estimation made with the lithium polymer battery data set, which was created by experiments performed at different discharge levels and is new in the literature. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    Combined Utilization of Cylinder and Different Shaped Alumina Nanoparticles in the Base Fluid for the Effective Cooling System Design of Lithium-Ion Battery Packs
    (MDPI, 2023) Selimefendigil F.; Dilbaz F.; Öztop H.F.
    It is important to consider the thermal management of lithium-ion batteries to overcome their limitations in usage and improve their performance and life cycles. In this study, a novel cooling system for the thermal management of lithium-ion battery packs is proposed by using an inner cylinder in the cooling channel and different-shaped nanoparticles in the base fluid, which is used as the cooling medium. The performance improvements in a 20 Ah capacity battery are compared by using a water–boehmite alumina (AlOOH) nanofluid, considering cylinder-, brick-, and blade-shaped nanoparticles up to a solid volume fraction of 2%. The numerical analysis is conducted using the finite element method, and Reynolds numbers between 100 and 600 are considered. When the efficacy of the coolants utilized is compared, it is apparent that as the Reynolds number increases, both cooling media decrease the highest temperature and homogenize the temperatures in the battery. The utilization of the cylinder in the mini-channel results in a 2 °C temperature drop at Re = 600 as compared to the flat channel. A boehmite alumina nanofluid with a 2% volume fraction reduces the maximum temperature by 5.1% at Re = 200. When the shape effect of the nanofluid is examined, it is noted that the cylinder-shaped particle improves the temperature by 4.93% as compared to blade-shaped nanoparticles and 7.32% as compared to brick-shaped nanoparticles. Thus, the combined utilization of a nanofluid containing cylindrical-shaped nanoparticles as the cooling medium and a cylinder in the mini-channel of a battery thermal management system provides an effective cooling system for the thermal management of the battery pack. The outcomes of this work are helpful for further system design and optimization studies related to battery thermal management. © 2023 by the authors.
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    Single and binary nickel, copper, and zinc-based nanosized oxides as anode materials in lithium-ion batteries
    (Springer, 2024) Egesoy E.; Kap Ö.; Bardak F.; Horzum N.; Ataç A.
    The demand for portable power sources with higher energy density and longer lifespan has prompted researchers to focus on developing better electrode materials for lithium-ion batteries (LIBs). Metal oxide nanoparticles have potential due to their low cost, high surface-area-to-volume ratio, strong reactivity, excellent size distribution, high theoretical capacities, and eco-friendly synthesis methods. However, there is still room for improvement in capacity retention and rate performance. To cope with this entail, the cycle performance of LIBs has been initially investigated utilizing single metal oxide anode materials including NiO, CuO, and ZnO nanostructures. Subsequently, binary oxides of Ni–Cu, Ni–Zn, and Cu–Zn have been synthesized to examine whether the binary structures boost the battery performance. NiCuO is the optimum anode material combining the benefits of NiO with the highest initial discharge capacity of 691 mAh g - 1 and the highest retention rate of CuO (49% after 30 cycles). © 2024, The Author(s).
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    THERMAL MANAGEMENT OF LITHIUM-ION BATTERY PACKS BY USING CORRUGATED CHANNELS WITH NANO-ENHANCED COOLING
    (Begell House Inc., 2024) Selimefendigil F.; Can A.; Öztop H.F.
    In this study, a cooling system using corrugated cooling channels and Al2O3–Cu/water hybrid nanofluid is offered as the battery thermal management system (BTMS) for prismatic Li-ion batteries. A computational model built based on the finite element approach uses hybrid nanofluid at solid volume fractions ranging from 0 to 2% at various Reynolds numbers. The cold plates are corrugated and have a variety of square grooves positioned between prismatic Li-ion battery cells. The maximum temperature decreases as the volume fraction of solid nanoparticles and the number of corrugated cooling channels increases. When cases of using lowest and highest number of cooling channels are compared, maximum temperature reduction is found as 3.07 K when using water and 1.86 K when using Al2O3–Cu/ water hybrid nanofluid (at the largest solid volume fraction). The number of square grooves in the cooling channels does not have any significant impact on the temperature drop when using nanofluid at the highest solid volume fraction. © 2024 by Begell House, Inc.
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    Review of battery state estimation methods for electric vehicles - Part I: SOC estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    This study presents a comprehensive review of State of Charge (SOC) estimation methods for Lithium-Ion (Li-Ion) batteries, with a specific focus on Electric Vehicles (EVs). The growing interest in EVs and the need for efficient battery management have driven advancements in SOC estimation techniques. Various approaches, including data-driven techniques, advanced filtering methods, and machine learning algorithms have been explored to enhance SOC estimation accuracy. The integration of artificial intelligence and hybrid models has shown promising results in improving SOC estimation performance. However, challenges remain in dealing with non-linear battery behavior, temperature variations, and diverse operating conditions. Researchers are continuously studying to improve the robustness and adaptability of SOC estimation methods to address these challenges. The primary objective of this study is to provide an up-to-date summary of the latest advancements in SOC estimation, offering insights into innovative approaches and developments in this field. All existing SOC methods, their advantages, challenges, and usage rates have been comprehensively examined with a specific focus on EV battery management systems. As the EV market continues to expand, accurate SOC estimation will remain essential for optimal battery management and overall EV performance. Future research will focus on refining existing algorithms, exploring new data-driven techniques, and integrating advanced sensor technologies to achieve real-time and reliable SOC estimation in EVs. © 2024 Elsevier Ltd
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    Comparisons of different cooling systems for thermal management of lithium-ion battery packs: Phase change material, nano-enhanced channel cooling and hybrid method
    (Elsevier Ltd, 2024) Dilbaz F.; Selimefendigil F.; Öztop H.F.
    Heat produced during the charging/discharging cycle must be dissipated for lithium-ion batteries to operate efficiently. Consequently, three distinct li-ion battery cooling systems were devised in this research, including phase-changing material (PCM), liquid-assisted, and hybrid, to allow lithium-ion batteries to run at the optimal operating temperature. To assess the efficiency of BTMS, the highest temperature and variation in temperature were examined. Without cooling system, simulations of the 20 Ah capacity battery pack were performed at various discharge rates (2C, 3C, and 4C). After that, an effective thermal management technique was identified by simulating PCM, liquid-assisted, and hybrid BTMS. The efficacy of PCM and BTMS was investigated at three different discharge rates. Water and Al2O3 nanofluid cooling medium thermal performance was investigated for liquid-supported BTMS at four distinct Reynolds numbers (Re) (250, 500, 750, and 1000), three distinct volume ratios (0.5 %, 1 %, and 2 %), and four distinct nanoparticle geometric shapes (Oblate spheroid, block, cylinder, and platelet). The influence of cooling channels on the thermal characteristics on PCM was investigated utilizing four various Re values and three distinct volume ratios, as well as the cooling effectiveness of hybrid BTMS. When the findings were analyzed, it emerged that hybrid BTMS improved the highest temperature by 28 %, while PCM and liquid-assisted cooling techniques enhanced peak temperature by 26 % and 27 %, correspondingly. However, when the temperature difference was analyzed, it was determined that only the hybrid and PCM reduced it to less than 5 °C, which is a suitable temperature difference. Paraffin can be cooled more efficiently by lowering the liquid stage distribution in the solid stage and the melting start time utilizing the hybrid cooling technique. Because of this, it has been determined that hybrid BTMS is the optimal cooling approach for the battery module. © 2024 Elsevier Ltd
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    NUMERICAL ANALYSIS FOR THE IMPACTS OF USING NANO-ENHANCED PCM ON THE THERMAL MANAGEMENT OF BATTERY MODULE
    (Serbian Society of Heat Transfer Engineers, 2024) Selimefendigil F.; Cakmak F.A.; Oztop H.F.
    The temperature and temperature differences in the battery module rise as a result of the high heat output produced by lithium-ion batteries during operation. This can reduce the operating safety of the battery and reduce the battery life. As a result, the temperature of the batteries must be controlled well by thermal management. Thermal control of batteries employs both active and passive techniques. In this study, PCM, which is a passive cooling system, was used. It has been observed that by placing PCM around the battery, it effectively reduces the peak temperature during the end of discharge in the battery cell. The RT-27 and nano-doped RT-27 with suitable melting range were used as PCM. Four different situations were investigated at 0.3C and 0.5C discharge conditions. These are battery models coated with only the battery, RT-27, coated with nano-RT-27, and coated with RT-27 and nano-RT-27, respectively. The peak temperature was found to be higher when the battery module without PCM was compared to the others. The battery module coated on both surfaces with RT-27 and nano-RT-27 performed better than the other modules. At 0.3 C-Rate, the peak temperature reduces by 1.8 K while it is 4.4 K at 0.5C-Rate. © 2024 Society of Thermal Engineers of Serbia.
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    Review of battery state estimation methods for electric vehicles-Part II: SOH estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    State of Health (SOH) significantly determines the performance and durability of EV batteries, with Battery Management System (BMS) playing a crucial role in enhancing their efficiency and operational cycle life. This comprehensive review, the second part of our series on Battery State Estimation Methods for Electric Vehicles, provides an in-depth exploration of SOH estimation methods. SOH, which encompasses a battery's overall health, capacity, and aging characteristics, plays a fundamental role in making informed decisions, conducting proactive maintenance, and ensuring the safe and reliable operation of EVs. Diverse SOH estimation methods, ranging from data-driven to model-based approaches, address the multifaceted challenges associated with battery aging, including electrochemical processes, temperature variations, usage patterns, and external factors. In recent years, data-driven methods, especially those rooted in machine learning and artificial intelligence, have gained prominence. These methods facilitate the discovery of complex models and correlations, encompassing battery degradation and using datasets to train algorithms. Machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL), have shown significant promise in estimating SOH by learning from historical data and adapting to varying operational conditions. The studies highlighted in this review demonstrate significant advancements in SOH estimation techniques, leading to improved accuracy, efficiency, and adaptability. These advances contribute to the development of more reliable BMSs for EVs and battery energy storage systems. © 2024 Elsevier Ltd
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    Analysis of melting behavior of layered different phase change materials for a cylinder insert into a channel
    (Elsevier Ltd, 2024) Öztop H.F.; Akbal Ö.; Biswas N.; Selimefendigil F.
    Phase change materials (PCMs) have diverse fields of application due to higher thermal inertia. The present study explores the melting behavior in dual-layered different PCMs in a cylindrical lithium-ion battery module placed in a long channel under the hot air stream. The main cylinder is packed with paraffin wax and covered PCM is chosen as calcium chloride hexahydrate (CaCl2-6H2O). The material between two PCMs is considered highly conductive and impermeable. The involved mathematical equations in two-dimensional forms are numerically simulated the applying the finite volume technique. The primary important parameters of melting behavior are the difference in temperature and inlet air velocity. The analysis is carried out for the effective parameters such as the inlet air temperature (Thot = 100 °C and 120 °C) and air velocity (սair = 2.5 and 5.0 m/s). The results revealed that an increase in air velocity reduces the highest and average temperature of the PCM. The changes in melting time of PCMs decline with time. Higher inlet air temperature also causes fast melting of the PCMs over time. The analysis revealed that the application of PCMs is undue when there is no phase change process. The analysis also revealed that an increment in the inlet air velocity causes a decrease in the average temperature. For the same air inlet temperature (Thot) higher air velocity leads to a slower rate of the melting process Therefore, melting time is less by ∼ 30 % (for the inner layer) and 32 % (for the outer layer) with decreasing air velocity. The findings of this study will help the designer to improve the battery thermal management system by modulating the temperature. © 2024 Elsevier Ltd