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) / There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults.