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Hydroplaning is a phenomenon that occurs when a layer of water between the tire and pavement pushes the tire upward. The tire detaches from the pavement, preventing it from providing sufficient forces and moments for the vehicle to respond to driver control inputs such as breaking, accelerating, and steering. This work is mainly focused on the tire and its interaction with the pavement to address hydroplaning. Using a tire model that is validated based on results found in the literature, fluid–structure interaction (FSI) between the tire-water-road surfaces is investigated through two approaches. In the first approach, the coupled Eulerian–Lagrangian (CEL) formulation was used. The drawback associated with the CEL method is the laminar assumption and that the behavior of the fluid at length scales smaller than the smallest element size is not captured. To improve the simulation results, in the second approach, an FSI model incorporating finite element methods (FEMs) and the Navier–Stokes equations for a two-phase flow of water and air, and the shear stress transport k–ω turbulence model, was developed and validated, improving the prediction of real hydroplaning scenarios. With large computational and processing requirements, a grid dependence study was conducted for the tire simulations to minimize the mesh size yet retain numerical accuracy. The improved FSI model was applied to hydroplaning speed and cornering force scenarios.

The new generation of lithium-ion batteries (LIBs) possesses considerable energy density that arise the safety concern much more than before. One of the main issues associated with LIB safety is the heat generation and thermal runaway in LIBs. The importance of characterizing the heat generation in LIBs is reflected in numerous studies. The heat generation in LIBs can be related to energy efficiency as well. In this work, the heat generation in LIB is predicted using two different approaches (physics-based and machine learning-based approaches). A validated multiphysics-based and neural network-based models for commercial LIBs with lithium iron phosphate/graphite (LFP/G), lithium manganese oxide/graphite (LMO/G), and lithium cobalt oxide/graphite (LCO/G) electrodes are used to predict the heat generation toward shaping the LIB energy efficiency contours, illustrating the effect of the nominal capacity as a key parameter in the manufacturing process of the LIBs. The developed contours can provide the energy systems designers a comprehensive view over the accurate efficiency of LIBs when they need to incorporate LIBs into their devices. In addition, the effect of temperature on charge/discharge energy efficiency of LFP/graphite LIBs is obtained, and the performance of three typical LIBs in the market at a very low temperature is compared, which have a wide range of applications from consumer applications such as electric vehicles (EVs) to industrial applications such as uninterruptible power sources (UPSes).

The formation of water drops as a Newtonian fluid and formation of a shear-thinning non-Newtonian fluid, Carboxyl Methyl Cellulose (CMC) from a capillary into different bulk fluids are experimentally investigated. A high speed camera is used to visualize the images of the drops and an image-processing code employed to determine the drop properties from each image. It was found that the properties of the water drops when they are drooped into the liquids bulk fluids such as toluene and n-hexane are almost the same while they differed substantially when they were drooped into the air bulk fluid. It is shown that during the formation of water drop in all three kinds of bulk fluids, the drop forms from the inner diameter of the needle and it moves toward the outer diameter. In addition, the properties of a low-concentration CMC in the air was almost identical to the properties of water drops, however the high-concentration CMC shows significant variations as compared to the both low-concentration CMC and water drops.

Heat generation in lithium-ion batteries (LIBs), different in nominal battery capacity and electrode materials (battery chemistry), is studied at various charge and discharge rates through the multiphysics modeling and computer simulation. The model is validated using experimental results obtained in lab and the results reported by other researchers in literature. All sources of heat generation – including reversible heat generation and irreversible heat generation due to activation, concentration, and ohmic (electronic and ionic) polarizations – in LIBs with graphite/LiFePO4, graphite/LiMn2O4, and graphite/LiCoO2 electrode materials are quantified and the effects of the battery nominal capacity at various charge and discharge rates are studied. This study determines which sources of heat generation are significant and which sources of heat generation are negligible at different LIBs design and operating conditions. The contribution of LIB’s different components including cathode, anode, separator, and current collectors in heat generation in LIBs is also determined. The results of this study assists battery engineers and researchers to characterize the thermal behavior of LIBs and have a much more exact prediction over the battery heat generation, leading to designing effective thermal management systems and providing accurate battery management systems for different applications of LIBs as well as cell design optimization and operation.

In electric and hybrid-electric aircraft, the battery systems are usually composed of up to thousands of battery cells connected in series or parallel to provide the voltage and power/energy requirements. The inconsistent cells could affect the battery pack and its performance or even endanger electric and hybrid-electric aircraft security; thus, the early fault diagnosis of the battery system is essential. A well-designed battery management system along with a set of reliable voltage and current sensors is required to properly measure and control the cells operational variables in a large battery pack. In this study, based on the battery working mechanism, a new, fast, and robust fault diagnostic scheme is proposed for a lithium-ion battery (LIB) pack that can be employed for applications such as electric and hybrid-electric aircraft. In this method, some faults such as the overcharge, overdischarge occurring in LIB packs can be detected and isolated, based on some predefined factors gained from the battery models in healthy, overcharge, and overdischarge conditions. Finally, the effectiveness of the proposed fast fault diagnosis scheme is experimentally validated with LIBs under a typical flight cycle.

This research presents different scenarios of hybrid photovoltaic-geothermal heat pump system that can provide the requirements of the heating and cooling load consumption in a residential building during the year. One of the major purposes of the present study is to explore the optimum contribution of the solar and ground source energies in which the generation energy costs are reasonable with respect to the total costs, lifetime, and energy losses. For this purpose, a numerical dynamic model consisting of different solar photovoltaic (PV) panels of polycrystalline, monocrystalline, and thin-film cells, batteries, inverter, and ground source heat pump (GSHP) was developed using the engineering equation solver and TRNSYS software. In order to reach the best performance of the system, multi- and single-objective optimizations of the life cycle cost and irreversibility were carried out using the particle swarm optimization algorithm considering all effective parameters of the system including surface area, slope, direction of PV panels and battery capacity. Moreover, the optimization tool which was developed in MATLAB software, was adopted for data interaction between TRNSYS and MATLAB. The results indicate that among various scenarios, the optimized hybrid polycrystalline PV system with an area of 35 m2 and a solar fraction of 31% can be cost-effective up to a 24% inflation rate. Also, the payback period of the optimized hybrid system compared to the regular GSHP is 3 years with the average global costs, while this value is 10 years for Iran’s economic situation.

Intelligent tires can be used in autonomous vehicles to insure the vehicle safety by monitoring the tire and tire-road conditions using sensors embedded on the tire. These sensors and their wireless communication systems need to be powered by energy sources such as batteries or energy harvesters. The deflection of tires during rotation is an available and reliable source of energy for electric power generation using piezoelectric energy harvesters to feed tire self-powered sensors and their wireless communication systems. The aim of this study is to design, analyze, and optimize a rainbow-shaped piezoelectric energy harvester mounted on the inner layer of a pneumatic tire for providing enough power for microelectronics devices required for monitoring intelligent tires. It is shown that the designed piezoelectric energy harvester can generate sufficient voltage, power, and energy required for a tire pressure monitoring system (TPMS) with high data transmission speed or three TPMSs with average data transmission speed. The effect of the vehicle speed on the voltage and electric energy generated by the designed piezoelectric is also studied. The geometry and the circuit load resistance of the piezoelectric energy harvester are optimized in order to increase the energy harvesting efficiency. It is shown that the optimized rainbow piezoelectric energy harvester can reach the highest power generation among all the strain-based energy harvesters that partially cover the inner layer of the tire.

Hydroplaning is a phenomenon which occurs when a layer of water between the tire contact patch and pavement pushes the tire upward. The tire detaches from the pavement, preventing it from providing sufficient forces and moments for the vehicle to respond to driver’s control inputs such as breaking, acceleration and steering. This work is mainly focused on the tire and its interaction with the pavement to address hydroplaning. Fluid Structure Interactions (FSI) between the tire-water-road surfaces are investigated through two approaches. In the first approach, the coupled Eulerian-Lagrangian (CEL) formulation was used. The drawback associated with the CEL method is the laminar assumption and that the behavior of the fluid at length scales smaller than the smallest element size is not captured. As a result, in the second approach, a new Computational Fluid Dynamics (CFD) Fluid Structure Interaction (FSI) model utilizing the shear-stress transport k-ω model and the two-phase flow of water and air, was developed that improves the predictions with real hydroplaning scenarios. Review of the public literature shows that although FEM and CFD computational platforms have been applied together to study tire hydroplaning, developing the tire-surrounding fluid flow CFD model using Star-CCM+ has not been done. This approach, which was developed during this research, is explained in details and the results of hydroplaning speed and cornering force from the FSI simulations are presented and validated using the data from literature.

In this study, the low-temperature energy efficiency of lithium-ion batteries (LIBs) with different chemistries and nominal capacities at various charge and discharge rates is studied through multi-physics modeling and computer simulation. The model is based on the irreversible heat generation in the battery, leading to the charge/discharge efficiency in LIBs with graphite/LiFePO4, graphite/LiMn2O4, and graphite/LiCoO2 electrode materials in which the effects of the battery nominal capacity at various charge and discharge rates are studied. Using characterized sources of the heat generation in the LIB leads to providing a battery efficiency plot at different operation condition for each LIBs. The results of this study assist the battery engineers to have much more accurate prediction over the efficiency of the LIBs at low temperatures.

In this work, formation water drops as a Newtonian fluid in different bulk fluids are investigated. A MATLAB code has been developed to process the images taken via a high speed camera in the lab to measure the contact angle of drop, as well as the drop’s diameter and volume at different stages of formation. It is found that the water drop shows similar behavior when they shaped in the liquid phase bulk fluid with different properties while the drop formation’s behavior is substantially different when water drops are formed in the gas bulk fluid. In addition, it is tried to predict the frequency of drop formation at different flow rates with regard to the inertial and surface tension forces applied to the dispread fluid.Copyright © 2018 by ASME

A well-designed battery management system along with a set of voltage and current sensors is required to properly measure and control the battery cell operational variables for Hybrid Electric Aircrafts (HEAs). Some critical functions of the battery including State-Of-Charge (SOC) and State-Of-Health (SOH) estimations, over-current, and over-/under-voltage protections are mainly related to current and voltage sensor measurements. Therefore, in case of battery faults occur in HEA, designing a reliable and robust diagnostic procedure is essential. In this study, for Li-ion batteries, a new and fast fault diagnosis technique via collecting data is proposed. Finally, the effectiveness of the proposed diagnostic method is validated, and the results show how overcharge, over-discharge and sensor faults can be accurately detected.

In this work, the energy efficiency of the lithium-ion batteries (LIB) with graphite anode and LiFePO4 cathode (G/LFP) at different nominal capacities and charge/discharge rates is studied through multiphysics modeling and computer simulation. After characterizing all the heat generation sources in the cell, the total heat generation in LIBs is calculated and the charge/discharge efficiency plots at different temperatures are obtained. Since G/LFP LIBs have a wide range of applications in passenger and commercial electric vehicles (EVs), the result of this study assist engineer toward more efficient battery pack design.

Ambient energy harvesting using piezoelectric transducers is becoming popular to provide power for small microelectronics devices. The deflection of tires during rotation is an example of the source of energy for electric power generation. This generated power can be used to feed tire self-powering sensors for bicycles, cars, trucks, and airplanes. The aim of this study is to optimize the energy efficiency of a rainbow shape piezoelectric transducer mounted on the inner layer of a pneumatic tire for providing enough power for microelectronics devices required to monitor tires. For this aim a rainbow shape piezoelectric transducer is adjusted with the tire dimensions and excited based on the car speed and strain. The geometry and load resistance effects of the piezoelectric transducer is optimized using Multiphysics modeling and finite element analysis.