Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction of battery SOH have caused widespread concern in academic and industrial communities. In this paper, a method is proposed to build an accurate SOH prediction model for battery packs based on multi-output Gaussian process regression (MOGPR) by employing the initial cycle data of the battery pack and the entire life cycling data of battery cells. Firstly, a battery aging experimental platform is constructed to collect battery aging data, and health indicators (HIs) that characterize battery aging are extracted. Then, the correlation between the HIs and the battery capacity is evaluated by the Pearson correlation analysis method, and the HIs that own a strong correlation to the battery capacity are screened. Finally, two MOGPR models are constructed to predict the HIs and SOH of the battery pack. Based on the first MOGPR model and the early HIs of the battery pack, the future cycle HIs can be predicted. In addition, the predicted HIs and the second MOGPR model are used to predict the SOH of the battery pack. The experimental results verify that the approach has a competitive performance; the mean and maximum values of the mean absolute error (MAE) and root mean square error (RMSE) are 1.07% and 1.42%, and 1.77% and 2.45%, respectively.

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions.

In this study, a novel anode-supported solid oxide fuel cell (SOFC) comprising a Bi₂O₃-doped NiO-ScSZ anode and an ScSZ electrolyte was successfully fabricated via a low-temperature co-sintering process at 1300 °C. The incorporation of 3 wt% Bi₂O₃ effectively promoted the sintering of both the anode support and electrolyte layer, resulting in a dense, gas-tight electrolyte and a mechanically robust porous anode support. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses confirmed the formation of phase-pure, highly crystalline ScSZ with an optimized microstructure. Electrochemical performance measurements demonstrated that the fabricated cells achieved excellent power density, reaching a peak value of 0.861 W cm⁻² at 800 °C under humidified hydrogen fuel conditions. The cells maintained stable performance under dry methane operation, with a maximum power density of 0.624 W cm⁻² at 800 °C, indicating resistance to carbon deposition. Gas chromatographic analyses further revealed that the Bi₂O₃-doped NiO-ScSZ anode facilitated earlier and more stable electrochemical oxidation of methane-derived species compared with the conventional NiO-YSZ system, even under conditions of an elevated methane partial pressure. These findings demonstrate that Bi₂O₃ co-doping, combined with low-temperature co-sintering, provides an effective approach for fabricating high-performance intermediate-temperature SOFCs with enhanced structural integrity and electrochemical stability. The developed methodology presents a promising pathway toward achieving cost-effective and durable SOFC technologies.

Against the backdrop of large-scale integration of renewables into the power grid, frequent load-following operation of thermal power units substantially increases the difficulty of controlling boiler NOx emissions. Accurate forecasting of boiler NOx emissions is crucial for guiding efficient and clean operation under such flexible operating conditions. However, under frequent load-following conditions, NOx dynamics are highly nonlinear and non-stationary, making it challenging to achieve accurate prediction using only time-domain information. To address these issues, we propose DTTE-Net, a time–frequency feature fusion framework for predicting SCR-inlet NOx concentration in coal-fired boilers. DTTE-Net consists of three components: a time-domain branch, a frequency-domain branch, and a gated feature fusion module. The time-domain branch captures short-term fluctuations and long-range temporal dependencies, while the frequency-domain branch extracts complementary spectral representations to enhance the characterization of non-stationary fluctuations. The gated feature fusion module then adaptively integrates the two-domain features by using a gated mechanism and produces the NOx concentration forecast. In addition, a Gaussian kernel-based loss is introduced to improve robustness to nonlinear error structures. Experiments on real distributed control system data from a 660 MW ultra-supercritical coal-fired unit show that DTTE-Net outperforms existing baseline models, achieving lower forecasting errors and higher R².

This paper proposes a new Asymmetric Permanent Magnet Vernier Motor (A-PMVM) for low-speed high-torque applications. Unlike conventional symmetric V-shaped PMVMs (SV-PMVMs), the A-PMVM features irregular U-shaped magnet arrays composed of asymmetric V-shaped magnets. Finite element analysis confirms its superior performance: 10.6% higher torque (19.67 N·m vs. 17.78 N·m), 22% reduced PM volume (37,500 mm³ vs. 48,000 mm³), and 53% lower cogging torque (0.32 N·m vs. 0.68 N·m peak-peak). While exhibiting higher initial torque ripple ratio (8.65%), multi-objective optimization suppresses torque ripple ratio by 5.32% (from 8.65% to 8.19%), reduces cogging torque 12.5% (from 0.32 N·m to 0.28 N·m), and enhances torque by 0.76% (from 19.67 N·m to 19.82 N·m). The optimized A-PMVM achieves a significant reduction in cogging torque and torque ripple ratio, demonstrating significant potential for applications like wind turbines and electric vehicles. Additionally, this paper confirms that the proposed motor maintains consistent performance during both clockwise and counterclockwise operation.