Relative densities and heat capacity ratios have been measured for selected aqueous systems. These measurements have been used to calculate apparent molar volumes and heat capacities. Densities of aqueous sodium bromide have been measured from 374 to 522 K and 10.00 to 30.00 MPa using a recently developed high temperature and pressure vibrating tube densimeter. These data have been used to test the utility of an automated high temperature and pressure densimetric data analysis program. Apparent molar volumes and heat capacities of several aqueous rare earth sulphate systems at 298.15 K and 0.10 MPa have been reported, and discussed in terms of ionic contributions. Single ion partial molar volumes and heat capacities for aqueous trivalent rare earth species have been estimated in a review of apparent molar data from the literature and through the use of semi-empirical Debye-Huckel equation. These singles ion properties have subsequently been used to estimate the single ion properties of the monosulphate and disulphate rare earth complex species. Rigorous relaxation calculations are presented in a discussion of apparent molar heat capacities, where relaxation contributions are shown to be negative. Apparent molar volumes and densities for aqueous L-histidine, L-phenylalanine, L-tyrosine, L-tryptophan, and L-dopa have been used to estimate reported partial molar properties have been added to several reported properites for other amino acids and peptides to construct an additivity scheme that utilises the revised Helgeson, Kirkham, and Flowers (HKF) equations of state for neutral organic species. A volumetric study of aqueous glycine, L-serine, and glyclylglycine has been conducted at temperatures from 298 K to 423 K and pressures from 0.10 to 30.00 MPa. These data have been used to evaluate HKF coefficients in a discussion of peptide stability at elevated temperatures and pressures. / xvii, 220 leaves : ill. ; 28 cm.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium-ion batteries are widely used for portable electronics due to high energy density, mature processing technology and reduced cost. However, their applications are somewhat limited by safety concerns. The lithium-ion battery users will take risks in burn or explosion which results from some internal components failure. So, a practical method is required urgently to find out the failures in early time. In this thesis, a new method based on temperature difference between internal point and surface (TDIS) of the battery is developed to detect the thermal failure especially the thermal runaway in early time. A lumped simple thermal model of a lithium-ion battery is developed based on TDIS. Heat transfer coefficients and heat capacity are determined from simultaneous measurements of the surface temperature and the internal temperature in cyclic constant current charging/discharging test. A look-up table of heating power in lithium ion battery is developed based on the lumped model and cyclic charging/discharging experimental results in normal operating condition. A failure detector is also built based on TDIS and reference heating power curve from the look-up table to detect aberrant heating power and bad parameters in transfer function of the lumped model. The TDIS method and TDIS detector is validated to be effective in thermal runaway detection in a thermal runway experiment. In the validation of thermal runway test, the system can find the abnormal heat generation before thermal runaway happens by detecting both abnormal heating power generation and parameter change in transfer function of thermal model of lithium ion batteries. The result of validation is compatible with the expectation of detector design. A simple and applicable detector is developed for lithium ion battery catastrophic failure detection.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.