Lithium-ion batteries (LIBs) play a pivotal role in advancing transportation electrification, offering a crucial solution to address climate change and fossil fuel depletion, but the current energy density of LIBs remains unsatisfying, limiting electric transportation range. To address this limitation, extensive efforts focus on developing novel electrode materials, including high-voltage cathodes and high-specific-capacity electrodes. However, the pursuit of higher energy densities introduces safety concerns due to the higher possibility of thermal runaway and flammable nature of conventional liquid electrolytes. In this doctoral thesis, I will present several innovative strategies for high-performance lithium battery systems aimed at enhancing the mileage of electric transportation without compromising or even enhancing safety. The first part (Chapter 3) discusses a novel design for structural batteries. Structural batteries are the energy storage devices with enhanced mechanical properties integrated as structural components in vehicles to reduce vehicle weights and increase mileage. Through the development of a scalable and feasible tree-root-like lamination at the electrode/separator interface, an 11-fold enhancement in the flexural modulus of pouch cells is achieved, and the underlying mechanism is revealed by finite element simulations. This lamination has a minimal impact on the electrochemical performance of LIBs and the smallest reported specific energy reduction of ~3% in structural batteries. The prototype "electric wings" showcases stable flight for an aircraft model, highlighting the effectiveness and scalability of engineering interfacial adhesion in developing structural batteries with superior mechanical and electrochemical properties. The second part (Chapter 4) presents a design rule for polymer electrolytes to enhance lithium metal battery safety. Lithium metal batteries are attractive for electric transportation due to their high energy densities, but their application is hindered by the safety concerns from dendrite growth. In this work, we observe that if the compositions of polyethylene oxide (PEO) electrolytes are near the boundary between amorphous and polymer-rich regions, concentration polarization in electrolytes will induce a phase transformation and create a PEO-rich phase at the electrode surface. This new phase is mechanically rigid with a Young’s modulus of ∼1-3 GPa so that it can suppress lithium dendrites, which allows Li/PEO/LiFePO₄ cells with such a phase transformation demonstrate superior lithium reversibility without dendrites for 100 cycles. The third part (Chapter 5) proposes an innovative cathode design for all-solid-state Li-S batteries (ASSLSBs) which have ultra-high energy densities and enhanced battery safety. However, conventional cathode designs of filling sulfur in carbon hosts suffer from accelerated decomposition of electrolytes and sulfur detachment, leading to significant capacity loss. As a solution, I propose that nonconductive polar hosts allow long cycling life of ASSLSBs via stabilizing the adjacent electrolytes and bonding sulfur/Li₂S steadily to avoid detachment. By using a mesoporous SiO₂host filled with 70 wt.% sulfur as the cathode, we demonstrate steady cycling in ASSLSBs with a capacity reversibility of 95.1% in the initial cycle and a discharge capacity of 1446 mAh g-1 after 500 cycles at C/5.

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.