Michael A. Danzer

Lithium‐ion batteries (LIBs) require stable high‐voltage cathode materials to meet escalating energy storage demands. LiNi _0.5 Mn _1.5 O ₄ (LNMO) offers high energy density while employing earth‐abundant manganese but suffers from degradation due to manganese dissolution and electrolyte decomposition at elevated voltages. This work presents a facile water‐based process to coat LNMO particles with nanometer‐thin niobate nanosheets via electrostatically driven self‐assembly. As nanosheets of large aspect ratio (≈2000) are obtained by a spontaneous delamination yielding uniform thickness (1.8 nm), the coating consistently covers LNMO surfaces. Electrochemical testing in full‐cells showed improved cycle stability, enhanced rate capability, and reduced self‐discharge for coated LNMO compared to pristine material. Post‐cycling analyses revealed suppressed manganese migration to the anode, evidencing the coating's effectiveness as a Li‐ion‐conductive, chemically inert permselective membrane that mitigates detrimental side reactions. The modified LNMO/graphite full cells exhibit excellent cycling stability, retaining 91% of its initial capacity after 500 cycles at 1C, as compared to 42% for the pristine material. Additionally, the coated material shows an increased rate capability, with a capacity of 101 mAh g ⁻¹ at 5C as compared to 84 mAh g ⁻¹ of the uncoated. This scalable, cost‐effective, coating strategy extends cathode lifespan and offers a promising route for advancing high‐voltage LIB technology.

Electrochemical impedance spectroscopy requires linear and time-invariant measurement conditions, i.e. a small, mean-free excitation signal and cells in a relaxed state. Superimposed DC loads violate these LTI requirements and introduce non-stationarity that distorts the measured system response.This work presents a correction framework that compensates for these effects. This enables DC-superimposed impedance measurements to be corrected such that they match LTI spectra at the same SoC and temperature. The approach is validated experimentally on a 21700 lithium-ion battery and complemented by physics-based Doyle–Fuller–Newman simulations. Voltage and SoC drift are identified as dominant distortion sources and corrected using time- and frequency-domain correction methods.After correction, DC-superimposed and stationary impedance spectra coincide from 10 kHz to 300 mHz for DC loads of up to C/2 across the majority of the investigated SoC range. A LTI map defines the minimum excitation frequency above which the impedance remains independent of current offsets. Simulations attribute any remaining deviations outside this map to the diffusion-induced depletion of the lithium-ion surface concentration.Within these limits, impedance parameterizations derived under steady-state conditions can be transferred directly to operando conditions. Prospectively, the framework provides a validation tool for impedance feature selection in battery management functions under load.

The Coulombic efficiency is a critical metric for evaluating lithium-ion battery performance anddegradation. While high-precision cycling systems theoretically enable ppm-level repeatability, theyremain susceptible to systematic measurement errors that introduce significant bias. This workpresents a comprehensive methodology designed to improve both trueness and precision of Coulombicefficiency by explicitly identifying, quantifying, and correcting dominant systematic error sources,including current-measurement offsets, polarity-dependent asymmetries, and thermal instabilities.Our approach combines high-precision shunt-based current sensing with a custom-built time intervalmeasurement unit to provide an external timing reference, which effectively eliminates measurementartefacts arising from sampling gaps and imprecise cycle termination. Through detailed mathematicalanalysis, we demonstrate that while symmetric errors cancel out in Coulombic efficiency calculation,non-symmetric sources require targeted mitigation. The effectiveness of our approach is validatedusing a dedicated H-bridge setup, enabling accuracy assessment independently of the cycler, followedby long-term cycling experiments on five commercial lithium-ion cells. Our results show that highaccuracyCoulombic efficiency determination significantly reduces measurement dispersion and biascompared to conventional cyclers, providing the required measurement quality to resolve subtledegradation effects and improve lifetime estimation accuracy.

Model-based development provides a systematic approach for investigating fuel cell (FC) and fuel cell system (FCS) operation, with particular emphasis on understanding reaction kinetics, transport phenomena, and aging mechanisms as functions of operating conditions. Characterizing these processes by physical models requires effective and efficient parameter identification, i.e., the derivation of precise parameter information with minimal experimental effort. Conventional strategies for parameter identification in FC models have so far suffered in particular from high time and resource requirements, leading to considerable bottlenecks in FC model development and system characterization. Therefore, this paper proposes a generic, systematic workflow for equally effective and efficient parameter identification in nonlinear, parametric models through structural and practical identifiability analysis based on the Fisher information matrix. The approach takes into account existing prior knowledge about parameter values, thereby enabling the calculation of optimal experimental designs. The methodology is universally applicable to differentiable parametric models. We demonstrate its practical applicability for a proton exchange membrane FC under realistic measurement conditions using a zero-dimensional model with six unknown parameters. Compared to conventional Latin Hypercube Sampling, the proposed approach achieves parameter identification with a 74% reduction in uncertainty or, conversely, saves 84% of the experimental effort required to achieve the same accuracy. The framework enables experimenters to conduct measurement campaigns that are both maximally effective and efficient. All methodological implementations, data, and demonstration results are made publicly available to facilitate reproducibility and community adoption.

The fabrication of solid-state batteries (SSBs) without the need for high-temperature sintering significantly requires less energy. The powder aerosol deposition (PAD) method enables direct film formation at room temperature, bypassing the conventional thermal densification step. This study presents an overview of recent advancements in the cycling properties of SSBs fabricated via PAD. Employing LiNi _0.82 Mn _0.07 Co _0.11 O ₂ (NMC) as cathode active material (CAM) and Li ₇ La ₃ Zr ₂ O ₁₂ (LLZO) as solid electrolyte, a capacity of 141 mAh g ⁻¹ with 90 % capacity retention over 25 cycles is demonstrated with no thermal post-treatment applied after film fabrication. Based on these results, the role of LLZO as catholyte in the cathode layer is investigated. The findings suggest an electrochemical instability between nickel-rich NMC and LLZO during cycling. To enhance the capacity, the aggregated results are used to discuss various strategies for optimizing cathode layer design, particularly with respect to the composition and choice of the CAM.

Electrochemical impedance spectroscopy (EIS) is a widely used technique for characterizing lithium-ion batteries. Conventional EIS assumes that the battery operates as a linear time-invariant system. However, maintaining stationarity is challenging, particularly when a superimposed direct current (DC) bias is applied. This study systematically investigated the validity of impedance measurements with varying DC bias and excitation amplitudes, analyzing the resulting non-linear and non-stationary effects. To assess the validity of impedance spectra, we employed validation techniques including the Kramers-Kronig test, total harmonic distortion, and Lissajous analysis. Additionally, we conducted electrochemical simulations based on a pseudo-two-dimensional model to replicate and further interpret the observed effects. Our results demonstrate that DC bias introduces significant impedance drift, leading to compromised validity of impedance spectra. These effects are detected throughout all investigated validation techniques, indicating a high reliability and confidence in the techniques. By combining experimental measurements, simulations, and validation methods, this study provides deeper insights into the impact of DC bias on EIS. These findings are particularly relevant for the application of EIS in battery management systems, where reliable state estimation and fault diagnostics are essential. The results emphasize the need for real-time impedance validation to ensure accurate diagnostics in dynamic battery operation.