Physics-based electrochemical models of lithium-ion cells require knowledge of electrode open-circuit potential (OCP) as a function of stoichiometry. To determine the OCP relationships for a cell built from unknown active materials, we might run low-rate constant-current laboratory tests on half cells built from harvested electrodes to collect related discharge and charge data. However, processing data from these tests must overcome three problems: the “data-quality problem,” the “missing-data problem,” and the “inaccessible-lithium problem.” This paper introduces a simple histogram-based method to overcome the data-quality problem and compares five different approaches to overcome the missing-data and inaccessible-lithium problems. These methods rely in part on a physics-based thermodynamic model for multiple-species multiple-reaction (MSMR) systems, which is flexible enough to accommodate different cell chemistries and simple enough to be utilized in real-time battery management systems. The five methods are validated in simulations and are then applied to physical half-cell data to produce OCP estimates for graphite and NMC electrodes from a commercial cell.

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Double-layered ZnO/silicon nitride microbridges were fabricated for microbridge tests. In a test, a load was applied to the center of the microbridge specimen by using a microwedge tip, where the displacement was recorded as a function of load until the specimen broke. The silicon nitride layer in the structure served to enhance the robustness of the specimen. By fitting the data to a theory, the elastic modulus, residual stress, and tensile strength of the ZnO film were found to be 137 ± 18 GPa, −0.041 ± 0.02 GPa, and 0.412 ± 0.05 GPa, respectively. The analysis required the elastic modulus, internal stress, and tensile strength of the silicon nitride layer. They were measured separately by microbridge tests on single-layered silicon nitride microbridges. The measured tensile strength of the ZnO films represents the maximum tolerable tensile stress that the films can sustain when they are used as the functional component in devices.

Battery-management systems require mathematical models of their cells to be able to compute estimates of state-of-charge, stateof-health, state-of-power or -function, and state-of-energy. These models may be empirical in nature, like equivalent-circuit models. Alternately, they may be based on physical principles, and we believe that such physics-based models provide benefits over empirical models in terms of their ability to be leveraged by battery-management-system algorithms to enhance battery safety, lifetime, and performance. A roadblock to using physics-based models is the difficulty in determining their parameter values to model a physical cell accurately and inexpensively. This paper proposes a method to identify a subset of the physics-based model parameter values that can be performed with the kind of equipment found in many battery labs and which does not require teardown of the cell. It applies a very short duration pulse to the cell to measure instantaneous resistance as a function of state of charge and amplitude. These resistances are then fit to a reduced-complexity physical model to determine model parameter values. The method is first applied to a virtual cell in simulation to explore its features and limitations, and is then applied to estimate parameter values for a commercial automotive lithium-ion cell.