This article presents a comparative experimental study of the electrical, structural, and chemical properties of large‐format, 180 Ah prismatic lithium iron phosphate (LFP)/graphite lithium‐ion battery cells from two different manufacturers. These cells are particularly used in the field of stationary energy storage such as home‐storage systems. The investigations include 1) cell‐to‐cell performance assessment, for which a total of 28 cells are tested from each manufacturer; 2) electrical charge/discharge characteristics at different currents and ambient temperatures; 3) internal cell geometries, components, and weight analysis after cell opening; 4) microstructural analysis of the electrodes via light microscopy and scanning electron microscopy; 5) chemical analysis of the electrode materials using energy‐dispersive X‐ray spectroscopy; and 6) mathematical analysis of the electrode balances. The combined results give a detailed and comparative insight into the cell characteristics, providing the essential information needed for system integration. The study also provides complete and self‐consistent parameter sets for the use in cell models needed for performance prediction or state diagnosis.

No abstract

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.