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Lithium‐ion batteries are among the most commonly used batteries to produce power for electric vehicles, which leads to the higher needs for battery thermal management system (BTMS). There are many key concerning points for the users of these batteries, which include reliability, safety, life cycle, and the operating temperature of the batteries. It is known through review that water is the best coolant for batteries, in which the maximum temperature was 43.3°C while the temperature of the coolant was 30°C during the discharge rate of battery pack at 4 C. An effective cooling system is necessary in prolonging the battery life, which controls the temperature difference between the batteries and the peak temperature of the battery. This review paper aims to summarize the recent published papers on battery liquid‐cooling systems, which include: battery pack design, liquid‐cooling system classification, and coolant performance. Furthermore, this study discusses other factors related to the recent studies, such as the properties and applications of different liquid coolants (oil and water) under the classification of liquid‐cooling system and the difference between passive and active, indirect and direct, and external and internal cooling systems are discussed. Moreover, this paper investigates the effect of temperature on the performance of battery in three aspects: low, high, and differential temperatures. Moreover, the study provides a systematic review of liquid‐based systems for direct and indirect contact modes.
Omer Kalaf; Davut Solyali; Mohammed Asmael; Qasim Zeeshan; Babak Safaei; Alyaseh Askir. Experimental and simulation study of liquid coolant battery thermal management system for electric vehicles: A review. International Journal of Energy Research 2020, 45, 6495 -6517.
AMA StyleOmer Kalaf, Davut Solyali, Mohammed Asmael, Qasim Zeeshan, Babak Safaei, Alyaseh Askir. Experimental and simulation study of liquid coolant battery thermal management system for electric vehicles: A review. International Journal of Energy Research. 2020; 45 (5):6495-6517.
Chicago/Turabian StyleOmer Kalaf; Davut Solyali; Mohammed Asmael; Qasim Zeeshan; Babak Safaei; Alyaseh Askir. 2020. "Experimental and simulation study of liquid coolant battery thermal management system for electric vehicles: A review." International Journal of Energy Research 45, no. 5: 6495-6517.
Lithium ion batteries (LiBs) are considered one of the most suitable power options for electric vehicle (EV) drivetrains, known for having low self-discharging properties which hence provide a long life-cycle operation. To obtain maximum power output from LiBs, it is necessary to critically monitor operating conditions which affect their performance and life span. This paper investigates the thermal performance of a battery thermal management system (BTMS) for a battery pack housing 100 NCR18650 lithium ion cells. Maximum cell temperature (Tmax) and maximum temperature difference (ΔTmax) between cells were the performance criteria for the battery pack. The battery pack is investigated for three levels of air flow rate combined with two current rate using a full factorial Design of Experiment (DoE) method. A worst case scenario of cell Tmax averaged at 36.1 °C was recorded during a 0.75 C charge experiment and 37.5 °C during a 0.75 C discharge under a 1.4 m/s flow rate. While a 54.28% reduction in ΔTmax between the cells was achieved by increasing the air flow rate in the 0.75 C charge experiment from 1.4 m/s to 3.4 m/s. Conclusively, increasing BTMS performance with increasing air flow rate was a common trend observed in the experimental data after analyzing various experiment results.
Akinlabi A. A. Hakeem; Davut Solyali. Empirical Thermal Performance Investigation of a Compact Lithium Ion Battery Module under Forced Convection Cooling. Applied Sciences 2020, 10, 3732 .
AMA StyleAkinlabi A. A. Hakeem, Davut Solyali. Empirical Thermal Performance Investigation of a Compact Lithium Ion Battery Module under Forced Convection Cooling. Applied Sciences. 2020; 10 (11):3732.
Chicago/Turabian StyleAkinlabi A. A. Hakeem; Davut Solyali. 2020. "Empirical Thermal Performance Investigation of a Compact Lithium Ion Battery Module under Forced Convection Cooling." Applied Sciences 10, no. 11: 3732.
Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.
Davut Solyali. A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus. Sustainability 2020, 12, 3612 .
AMA StyleDavut Solyali. A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus. Sustainability. 2020; 12 (9):3612.
Chicago/Turabian StyleDavut Solyali. 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus." Sustainability 12, no. 9: 3612.
A battery thermal management system (BTMS) is arguably the most vital component of an electric vehicle (EV), as it is responsible for ensuring the safe and consistent performance of lithium ion batteries (LiB). LiBs are considered one of the most suitable power options for an EV drivetrain. Owing to lithium's atomic number of three (3) and it being the lightest element of the metals, lithium is able to provide fantastic energy-to-weight characteristics for any lithium-based battery. LiBs are also known for having low self-discharging properties and hence provide long life cycle operation. To obtain a maximum power output from LiBs, it is necessary to critically monitor the operating conditions of LiBs, particularly temperature, which is known to directly affect the performance and life of LiBs. The temperature rise present around LiBs is caused by the heat generation phenomena of lithium ion cells during charge and discharge cycles. In this study, an investigation is made into one of the major categories of a BTMS, used in making the EV powertrain much more efficient and safe. Specifically, this study investigates and reviews air-cooled BTMS techniques (passive and active) and design parameter optimization methods (either via iteration or algorithms) for improving various BTMS design objectives. In particular, this study investigates minimizing the change in temperature among cells (ΔTmax) in a battery pack (BP). The data are classified, and results from recent studies on each method are summarized. It is found that despite features such as extreme simplicity, ease of implementation, and the relatively low cost of naturally air-cooled BTMS, it is almost impossible for the methods to provide adequate cooling conditions for the high energy density LiBs used in EVs. A shift in focus from a naturally air-cooled BTMS to a forced air-cooled BTMS is observed from the amount of studies found on the topics during the time scope of this study. Parameter configuration optimization techniques for the air-cooled BTMS are discussed and classified, and optimization algorithms applied by researchers to improve objectives of the BTMS are identified.
A.A. Hakeem Akinlabi; Davut Solyali. Configuration, design, and optimization of air-cooled battery thermal management system for electric vehicles: A review. Renewable and Sustainable Energy Reviews 2020, 125, 109815 .
AMA StyleA.A. Hakeem Akinlabi, Davut Solyali. Configuration, design, and optimization of air-cooled battery thermal management system for electric vehicles: A review. Renewable and Sustainable Energy Reviews. 2020; 125 ():109815.
Chicago/Turabian StyleA.A. Hakeem Akinlabi; Davut Solyali. 2020. "Configuration, design, and optimization of air-cooled battery thermal management system for electric vehicles: A review." Renewable and Sustainable Energy Reviews 125, no. : 109815.
Computer-based models and simulations are critical to the design, development, and optimization of smart manufacturing systems required for Industry 4.0. Modeling and Simulation technologies are essential to address the challenges in the adoption of Industry 4.0 today, such as the creation of smart manufacturing systems. Recently many researchers have contributed to modeling and simulation of smart factories in Industry 4.0, also known as Factory 4.0. This paper presents a systematic literature review of recent developments in modeling, simulation, and optimization of Smart Factories. It indicates the most frequent contexts, problems, methods, tools, related to simulation and optimization of smart factories. This paper fills this gap by identifying and analyzing research on simulation of smart factories.
Zeki Murat Cinar; Qasim Zeeshan; Davut Solyali; Orhan Korhan. Simulation of Factory 4.0: A Review. Lecture Notes in Management and Industrial Engineering 2020, 204 -216.
AMA StyleZeki Murat Cinar, Qasim Zeeshan, Davut Solyali, Orhan Korhan. Simulation of Factory 4.0: A Review. Lecture Notes in Management and Industrial Engineering. 2020; ():204-216.
Chicago/Turabian StyleZeki Murat Cinar; Qasim Zeeshan; Davut Solyali; Orhan Korhan. 2020. "Simulation of Factory 4.0: A Review." Lecture Notes in Management and Industrial Engineering , no. : 204-216.
This paper presents a technical assessment of wind power potential for Selvilitepe site in Northern Cyprus. The wind speed data was collected for 10 min intervals between years 2007 and 2014 at this site. Weibull distribution method using 3 different algorithms called maximum likelihood, least squares and WAsP was used for the statistical analysis of the measured data. Power law exponent method was used to create diurnal and monthly averaged wind speed variations at the heights of 50 m, 80 m and 90 m. Based on the determined standard deviation of the wind speed, turbulence category of this site is calculated and categorized. Shear profile and surface roughness of this site has also been analyzed and determined.
Davut Solyali; Mustafa Altunç; Süleyman Tolun; Zafer Aslan. Wind resource assessment of Northern Cyprus. Renewable and Sustainable Energy Reviews 2016, 55, 180 -187.
AMA StyleDavut Solyali, Mustafa Altunç, Süleyman Tolun, Zafer Aslan. Wind resource assessment of Northern Cyprus. Renewable and Sustainable Energy Reviews. 2016; 55 ():180-187.
Chicago/Turabian StyleDavut Solyali; Mustafa Altunç; Süleyman Tolun; Zafer Aslan. 2016. "Wind resource assessment of Northern Cyprus." Renewable and Sustainable Energy Reviews 55, no. : 180-187.
Davut Solyali; Miles A. Redfern. Why Should Cyprus Exploit the Solar Power to Match its Peak Demand? 2021, 1 .
AMA StyleDavut Solyali, Miles A. Redfern. Why Should Cyprus Exploit the Solar Power to Match its Peak Demand? . 2021; ():1.
Chicago/Turabian StyleDavut Solyali; Miles A. Redfern. 2021. "Why Should Cyprus Exploit the Solar Power to Match its Peak Demand?" , no. : 1.
Davut Solyali; M.A. Redfern. Have wind turbines stop maturing? 2021, 1 .
AMA StyleDavut Solyali, M.A. Redfern. Have wind turbines stop maturing? . 2021; ():1.
Chicago/Turabian StyleDavut Solyali; M.A. Redfern. 2021. "Have wind turbines stop maturing?" , no. : 1.
Davut Solyali; Miles A. Redfern. CASE STUDY OF CYPRUS: WIND ENERGY OR SOLAR POWER? 2021, 1 .
AMA StyleDavut Solyali, Miles A. Redfern. CASE STUDY OF CYPRUS: WIND ENERGY OR SOLAR POWER? . 2021; ():1.
Chicago/Turabian StyleDavut Solyali; Miles A. Redfern. 2021. "CASE STUDY OF CYPRUS: WIND ENERGY OR SOLAR POWER?" , no. : 1.