Accurate estimation of the state-of-health (SOH) is essential in prognostics and health management for Battery management system in vehicle application. Algorithms using short-term data from flexible voltage ranges are gaining significant attention since partial cycling is a common case in real-world applications. To this end, a data-driven model using short-term charging history is proposed. The model integrates Incremental capacity analysis curve classification with time series forecasting based on long–short term memory for SOH estimation with a short state of charge (SOC) variation. Three datasets with different cell chemistries and degradation trajectories are used for validation. Results show that the proposed model achieves accurate SOH estimation with the mean absolute error and root mean squared error between 1% and 2%. The model is highlighted by its ability to process inputs from flexible voltage ranges and to provide accurate SOH estimation with SOC changes of less than 10%. The model can also be adapted to different cell chemistries and aging behaviors.

Ru/B₄C multilayer mirrors are used for hard X-ray monochromators with moderate spectral resolution and high integral flux. To overcome the problem of large compressive stress inherent in Ru/B₄C multilayers, a reactive sputtering technique using a mixture working gas of argon and nitrogen with different partial pressures was tested, and the fabricated multilayers had a period of 3 nm. The intrinsic stress was essentially reduced after nitridation and relaxed to zero value at approximately 15% partial pressure of nitrogen in the working gas. Interface roughness was slightly increased which can be caused by the polycrystalline structure inside the nitridated samples. More importantly, the nitridated multilayers showed an enhanced reflectance (67% at 8.04 keV photon energy) as compared with the one fabricated with pure Ar (54%). The structure analysis with transmission electron microscopy and X-ray photoelectron spectroscopy demonstrated that nitrogen incorporated into a multilayer structure was mostly located in the B₄C layers forming BN compounds, which suppressed the diffusion of boron, stabilized the interfaces and enhanced the reflectance.

The capacity of Lithium-ion batteries degrades over the time, making accurate prediction of their Remaining Useful Life (RUL) crucial for maintenance and product lifespan design. However, diverse aging mechanisms, changing working conditions and cell-to-cell variation lead to the inhomogeneous cell lifespan and complicated life prediction. In this work, a data-driven algorithm based on stacked Long Short Term Memory (LSTM) encoder–decoders is proposed for RUL prediction. The encoder and upstream decoder form an autoencoder framework for feature extraction. The encoder and the downstream decoder form the encoder–decoder framework for RUL prediction. To enhance generalization during training, the Maximum Mean Discrepancy (MMD) loss is included in the autoencoder framework. The similarity of aging patterns is analyzed during splitting source and target datasets through k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The Euclidean metric with accumulated Equivalent Cycle Number (ECN) sequence during aging shows better performance for similarity-based data splitting than the Dynamic Time Wrapping (DTW) distance metric based on capacity fading trajectory. The experimental results indicate that the proposed algorithm can provide accurate RUL prediction using 5% fading data and shows good generalization with Coefficient of Determination (R²) score of 0.98.

Electrochemical impedance spectroscopy (EIS) is an established technique for characterizing degradation in lithium-ion batteries. Compared with laboratory EIS, online EIS (OEIS) is constrained by a limited frequency window and measurement error, which challenge reliable state-of-health (SoH) estimation. This work proposes a low-complexity framework for SoH estimation using health indicators (HIs) derived from an impedance triangle within the OEIS frequency window down to 1 Hz. The impedance triangle is constructed from three characteristic points: the ohmic, peak, and transition points. The characteristic frequency HIs and geometry-based HIs derived from the impedance triangle are compared with a normalized impedance ratio and a model-based charge transfer resistance as reference HIs. Among these HIs, the peak frequency and the ohmic-to-transition distance exhibit complementary sensitivity over different state-of-charge (SoC) and SoH ranges and are fused recursively. Validation is performed with an open-source aging dataset of 21 cells with diverse aging trajectories under three conditions: noise-free data, proportional Gaussian noise, and a measured OEIS noise profile derived from an Analog Devices CN0510 system. Under matched SoC conditions over the range of 20%–80%, the framework achieves a median SoH root mean square error (RMSE) of 1.05% under Gaussian noise and 0.97% under OEIS noise, with higher accuracy than SoH estimation based on charge transfer resistance and substantially lower computational cost. Under SoC mismatch, the median RMSE stays below 2% over the SoC range from 20% to 65%. These results show the potential of the framework for embedded deployment in battery management systems.