The life and efficiency of electric vehicle batteries are susceptible to temperature. The impact of cold climate dramatically decreases battery life, while at the same time increasing internal impedance. Thus, a battery thermal management system (BTMS) is vital to heat and maintain temperature range if the electric vehicle’s batteries are operating in a cold climate. This paper presents an induction heater-based battery thermal management system that aims to ensure thermal safety and prolong the life cycle of Lithium-ion batteries (Li-Bs). This study used a standard simulation tool known as GT-Suite to simulate the behavior of the proposed BTMS. For the heat transfer, an indirect liquid heating method with variations in flow rate was considered between Lithium-ion batteries. The battery and cabin heating rate was analyzed using the induction heater powers of 2, 4, and 6 kW at ambient temperatures of −20, −10, and 0 ∘C. A water and ethylene glycol mixture with a ratio of 50:50 was considered as an operating fluid. The findings reveal that the thermal performance of the proposed system is generally increased by increasing the flow rate and affected by the induction heater capacity. It is evident that at −20 ∘C with 27 LPM and 6 kW heater capacity, the maximum heat transfer rate is 0.0661 ∘C/s, whereas the lowest is 0.0295 ∘C/s with 2 kW heater capacity. Furthermore, the proposed BTMS could be a practical approach and help to design the thermal system for electric vehicles in the future.
The requirement for energy is increasing worldwide as populations and economies develop. Reasons for this increase include global warming, climate change, an increase in electricity demand, and paucity of fossil fuels. Therefore, research in renewable energy technology has become a central topic in recent studies. In this study, a solar-assisted house heating system with a seasonal underground thermal energy storage tank is proposed based on the reference system to calculate the insulation thickness effect, the collector area, and an underground storage tank volume on the system performance according to real weather conditions at Jeju Island, South Korea. For this purpose, a mathematical model was established to calculate its operating performance. This mathematical model used the thermal response factor method to calculate the heat load and heat loss of the seasonal underground thermal energy storage tank. The results revealed that on days with different weather conditions, namely, clear weather, intermittent clouds sky, and overcast sky, the obtained solar fraction was 45.8%, 17.26%, and 0%, respectively. Using this method, we can save energy, space, and cost. This can then be applied to the solar-assisted house heating system in South Korea using the seasonal underground thermal energy storage tank.
The boiling crisis or critical heat flux (CHF) is a very critical constraint for any heat-flux-controlled boiling system. The existing methods (physical models and empirical correlations) offer a specific interpretation of the boiling phenomenon, as many of these correlations are considerably influenced by operational variables and surface morphologies. A generalized correlation is virtually unavailable. In this study, more physical mechanisms are incorporated to assess CHF of surfaces with micro- and nano-scale roughness subject to a wide range of operating conditions and working fluids. The CHF data is also correlated by using the Pearson, Kendal, and Spearman correlations to evaluate the association of various surface morphological features and thermophysical properties of the working fluid. Feature engineering is performed to better correlate the inputs with the desired output parameter. The random forest optimization (RF) is used to provide the optimal hyper-parameters to the proposed interpretable correlation and experimental data. Unlike the existing methods, the proposed method is able to incorporate more physical mechanisms and relevant parametric influences, thereby offering a more generalized and accurate prediction of CHF (R2 = 0.971, mean squared error = 0.0541, and mean absolute error = 0.185).
The fast evolving Electric vehicles (EVs) have become popular due to their zero-emission, fuel economy and better technology. However, the performance and life of batteries are very sensitive to temperature, it is important to maintain the proper temperature range. The battery thermal management system (BTMS) plays an important role in the performance of EVs. In this context, this study is conducted to evaluate the thermal performance of a battery with a parallel system using an induction heater. The GT-Suite software is used for simulation and evaluation. Mixture of water and ethylene glycol 50:50 is used as a working fluid and controlled by pump and valves. The heating rate of battery was analyzed by changing the capacity of induction heater 2, 4 and 6[Formula: see text]kW and the flow rate of fluid was 2, 3, 5, 7, 10 and 27 LPM. The simulation work predicts that the battery heating rate increases with the increase in fluid flow. The study concluded that the battery heating rate is maximum with a flow rate of 27 LPM which is the highest amount of LPM, indicating that the rise in flow rate causes the increase in heating rate of the system which is also affected by induction heater capacity.
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