Analysis of Lithium‐Ion Battery State and Degradation via Physicochemical Cell and SEI Modeling

The quality of lithium-ion batteries is affected by the formation of the solid electrolyte interphase (SEI). For a better understanding of its effect on cell performance and aging, fast and economically scalable SEI diagnostics are indispensable. Battery models promise to extract hardly accessible interfacial and bulk properties of the SEI from electrochemical impedance spectra and discharge data. The common analysis of only one measurement, often with empirical models, impedes a precise localization of degradation-related and performance-limiting processes. This work offers a solution by combining physicochemical SEI and cell modeling for the joint analysis of both measurement types. Our analysis highlights the minor importance of the SEI ionic conductivity for cell performance along with a significant improvement and notable effect of its interfacial properties along aging. Such a detailed understanding of the initial SEI and its evolution over time could enable, e. g., a knowledge-based optimization of the cell formation process

Assessment of Core-Shell and Agglomerate Particle Design for All-Solid-State Batteries

All-solid state lithium polymer batteries are promising next-generation batteries with high safety and energy density. Their success depends on an improved design with a tailored cathode manufacturing process. To facilitate a knowledge-driven optimal design of cathode, a model-based analysis on the impact of the cathode particle structure on the electrochemical cell performance is conducted. During production of solid-state cathodes, small active material particles such as lithium-iron phosphate tend to form large agglomerates with inner electrolyte-filled pores which have significant effect on transport properties within a secondary particle. Therefore, a battery cell model with secondary particles and optionally with a core-shell structure is developed and evaluated. Discharge performance is shown to be stronger impacted by changing the electrolyte fraction inside the particle than by changing the size of the electrolyte core within the secondary particle. A core-shell structure has a positive impact on the discharge performance and should be preferred for high power application. In contrast, cells with homogeneous agglomerate particles show better performance at low discharge rates. Thus, they are recommended for high energy and low power applications. The results of this study highlight the potentials of tailored production process for next-generation batteries

State-of-Health Diagnosis of Lithium-Ion Batteries Using Nonlinear Frequency Response Analysis

Estimation of the State-of-Health (SOH) of Lithium-ion Batteries (LIBs) is commonly conducted using in-situ measurement methods, such as Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) as well as impedance based techniques. In this study, we present an alternative method for SOH estimation: The nonlinear dynamic measurement method Nonlinear Frequency Response Analysis (NFRA) is shown to be able to estimate capacity fade of LIBs due to loss of active material. Capacity loss correlates with the quotient of the root mean square of the second and the third harmonic response for different excitation amplitudes in the frequency range sensitive to electrochemical reactions at approximately 1 Hz. The results of the experimental cycle-aging study are validated and further analyzed by using a reaction model containing Butler-Volmer kinetics with a dynamic charge balance of the electrode. Simulations show that the NFR quotient and capacity fade due to loss of specific surface area correlate exactly. We identify the NFR quotient as a reliable, easily measurable parameter for the diagnosis of the SOH of LIBs. Therefore, this study reveals a novel approach for SOH estimation of LIBs based on dynamic analysis with NFRA