Thermal Management of Lithium-ion Battery Modules for Electric Vehicles

This research is particularly focused on studying thermal management of lithium-ion (Li-ion) battery modules in electric vehicles by using active, passive and hybrid active-passive methods. The thermal behavior prediction of batteries is performed by a novel electrochemical-thermal model. Different approaches such as single- and double-channel liquid cooling, pure passive by using phase change materials (PCM), and hybrid active-passive thermal management systems are investigated. Various cooling system configurations are examined to expand understanding of effect of each approach on the battery module thermal responses during a standard driving cycle. It is observed that the temperature distribution of Li-ion batteries is strongly influenced by the electrical and thermal operating conditions and simplified bulk models cannot precisely predict the thermal behavior of these batteries. Additionally, the PCM-based passive systems show advantages such as compactness and simplicity over the active liquid cooling systems. However, these systems suffer from non-uniform temperature distribution due to inherently low thermal conductivity of organic PCM. An effort has been made to enhance the thermal conductivity of a paraffin wax by adding various carbon-based nanoparticles. The results revealed that the thermal conductivity of the base PCM can be improved by about 11 times when using 10% mass fraction of graphite nanopowder. The heat transfer in the nano-enhanced PCM samples showed that the presence of nanoparticles drastically repress the natural convection in the melted nanocomposites. Among the battery thermal management systems studied, the air assisted hybrid cooling system provides the best temperature distribution uniformity in the module while keeping the batteries temperature within the safe limits. Furthermore, this work attempted to recognize the most influential parameters on the temperature distribution in the battery module. It is seen that the thickness of cooling plates and PCM layers in active and hybrid systems has a significant effect on the thermal behavior of the batteries

Ranking of critical success factors in reverse logistics by TOPSIS

Environmental issues and governmental regulations have pushed companies to be more environmentally conscious and socially responsible. Sustainable production plays an important role in the global markets and companies practicing sustainable manufacturing processes for protecting the environment can increase their competitiveness. Reverse logistics is one of the sustainable approaches, which returns back used product from point of consumption to the point of origin due to recovery and product reuse. The identification of critical success factors for reverse logistics is essential to facilitate reverse logistics organization in implementing it. In this work, critical success factors (CSFs) in reverse logistics were identified based on the critical success factors in supply chain management. A survey was conducted among reverse logistics experts to identify the factors. Then, the factors were ranked by TOPSIS, showed that transportation as the most important factor followed by process planning and resource efficiency. However, innovation was the lowest ranked

A novel application for energy efficiency improvement using nanofluid in shell and tube heat exchanger equipped with helical baffles

Hydrothermal characteristics of the water–Al₂O₃ nanofluid are numerically evaluated in shell-and-tube heat exchanger equipped with helical baffles using the two-phase mixture model. Heat transfer and pressure drop increase by increasing nanoparticle concentration and baffle overlapping, and decreasing helix angle. At smaller helix angles, changing the overlapping is more effective on the convective heat transfer coefficient and the pressure drop. Neural network is used for modeling, and based on the test data, the model predicts the convective heat transfer coefficient and the pressure drop with MRE (Mean Relative Error) values of about 0.089% and 0.65%, respectively. In order to obtain conditions of effective parameters which cause maximum heat transfer along with minimum pressure drop, optimization is performed on the neural network model using both two-objective and single-objective approaches. 15 optimal states obtain from two-objective optimization. The results obtained from single-objective optimization indicate that even when a low pressure drop is significantly important for designer, nanofluids with high concentrations can be employed. Meanwhile, when both high heat transfer and low pressure drop are important, a small helix angle can be used. In addition, using large overlapping is recommended only when the heat transfer enhancement is considerably more important than the reduction of the pressure drop

Improving the temperature measurement in hydro-processing reactors

The world is going to replace renewable and green fuels with fossil fuels to reduce the environmental issues and global warming effects. Bio-based feedstock is a biological source to produce fuel and considered as an alternative that can supersede fossil-based resources in the future. Co-processing is a transition towards green fuel which through a mixture of fossil and bio-based feedstocks are processed. In co-processing, the biomass is blended with fossil-based feed and upgraded through hydro-treating in a catalytic reactor. Since biomass contains high amount of oxygen, the process is highly exothermic releasing heat and causing temperature rise inside the reactor. Hence, reactor temperature needs to be monitored properly to prevent serious accident and retain the required quality of the product. In petro-refineries, use of temperature measurement systems is a need and usually problematic in hydro-processing reactors. When introducing alternative or biomass feedstocks to the process, the problem will be more highlighted due to new reactants and different reactions. The following work has expounded the need for measuring temperature in exothermic reactions. Reactions and products, main hardware and equipment has been described to express the need for temperature monitoring systems. This thesis has considered different approaches and methods in measuring the temperature in reactors mentioning their advantages and disadvantages. Challenges stemmed from the new reactants and new reactions by introducing bio-based feedstocks were identified. The material selection is crucial as almost all available temperature measurement systems has direct contact with the reactants and catalyst. Some widely-used materials in oil and gas industry were compared to choose the proper one for the application. The possible solutions reducing the problematic issues were recommended for design, procurement and installation of the temperature measurement system

Numerical Investigation of Nanofluid Forced Convection in Channels with Discrete Heat Sources

Numerical simulation is performed to investigate the laminar force convection of Al2O3/water nanofluid in a flow channel with discrete heat sources. The heat sources are placed on the bottom wall of channel which produce much thermal energy that must be evacuated from the system. The remaining surfaces of channel are kept adiabatic to exchange energy between nanofluid and heat sources. In the present study the effects of Reynolds number (Re=50,100,200,400, and 1000), particle volume fraction (=0 (distilled water), 1 and 4%) on the average heat transfer coefficient (h), pressure drop (Δ), and wall temperature () are evaluated. The use of nanofluid can produce an asymmetric velocity along the height of the channel. The results show a maximum value 38% increase in average heat transfer coefficient and 68% increase in pressure drop for all the considered cases when compared to basefluid (i.e., water). It is also observed that the wall temperature decreases remarkably as Re and ϕ increase. Finally, thermal-hydraulic performance (η) is evaluated and it is seen that best performance can be obtained for Re=1000 and =4%

Prenatal and Postnatal Hair Steroid Levels Predict Post-Partum Depression 12 Weeks after Delivery

Within three to six months after delivery, 13%-19% of women suffer from post-partum depression (PPD), understood as a dysfunctional adaptation to the postpartum condition and motherhood. In the present cross-sectional study, we compared the hair steroid levels of women 12 weeks before and after delivery and with or without PPD.; The present study was a cross-sectional study conducted twelve weeks after delivery. At that time, 48 women (mean age: 25.9 years) with PPD and 50 healthy controls (mean age: 25.2 years) completed questionnaires on depressive symptoms. Further, at the same time point, 6 cm lengths of hair strands were taken, providing samples of hair steroids 12 weeks before and 12 weeks after delivery in order to analyze hair steroids (cortisol, cortisone, progesterone, testosterone, and dehydroepiandrosterone (DHEA)).; Compared to those of women without PPD, hair steroid levels (cortisol, cortisone, progesterone) were significantly lower in women with PPD both before and after delivery. Lower prenatal cortisone and progesterone levels predicted higher depression scores 12 weeks after delivery. Lower prenatal levels of cortisol and progesterone and higher levels of DHEA, and postnatal lower levels of cortisol, cortisone, and progesterone, along with higher levels of DHEA predicted PPD-status with an accuracy of 98%.; PPD is associated with blunted hair cortisol, cortisone, and progesterone secretions both pre- and postpartum. Such blunted steroid levels appear to reflect a stress responsivity that is less adaptive to acute and transient stressors. It follows that prenatally assessed low hair cortisol and progesterone levels, along with high DHEA levels, are reliable biomarkers of post-partum depression 12 weeks after delivery

Slip and hall current effects on Jeffrey fluid suspension flow in a peristaltic hydromagnetic blood micropump

The magnetic properties of blood allow it to be manipulated with an electromagnetic field. Electromagnetic blood flow pumps are a robust technology which provide more elegant and sustainable performance compared with conventional medical pumps. Blood is a complex multi-phase suspension with non-Newtonian characteristics which are significant in micro-scale transport. Motivated by such applications, in the present article a mathematical model is developed for magnetohydrodynamic (MHD) pumping of blood in a deformable channel with peristaltic waves. A Jeffery’s viscoelastic formulation is employed for the rheology of blood. A twophase fluid-particle (“dusty”) model is utilized to better simulate suspension characteristics (plasma and erythrocytes). Hall current and wall slip effects are incorporated to achieve more realistic representation of actual systems. A two-dimensional asymmetric channel with dissimilar peristaltic wave trains propagating along the walls is considered. The governing conservation equations for mass, fluid and particle momentum are formulated with appropriate boundary conditions. The model is simplified using of long wavelength and creeping flow approximations. The model is also transformed from the fixed frame to the wave frame and rendered non-dimensional. Analytical solutions are derived. The resulting boundary value problem is solved analytically and exact expressions are derived for the fluid velocity, particulate velocity, fluid/particle fluid and particulate volumetric flow rates, axial pressure gradient, pressure rise and skin friction distributions are evaluated in detail. Increasing Hall current parameter reduces bolus growth in the channel, particle phase velocity and pressure difference in the augmented pumping region whereas it increases fluid phase velocity, axial pressure gradient and pressure difference in the pumping region. Increasing the hydrodynamic slip parameter accelerates both particulate and fluid phase flow at and close to the channel walls, enhances wall skin friction, boosts pressure difference in the augmented pumping region and increases bolus magnitudes. Increasing viscoelastic parameter (stress relaxation time to retardation time ratio) decelerates the fluid phase flow, accelerates the particle phase flow, decreases axial pressure gradient, elevates pressure difference in the augmented pumping region and reduces pressure difference in the pumping region. Increasing drag particulate suspension parameter decelerates the particle phase velocity, accelerates the fluid phase velocity, strongly elevates axial pressure gradient and reduces pressure difference (across one wavelength) in the augmented pumping region. Increasing particulate volume fraction density enhances bolus magnitudes in both the upper and lower zones of the channel and elevates pressure rise in the augmented pumping region