Zheyu Wang

To address the issue of signal aliasing when multiple particles pass through a metallic particle sensor, which can lead to misidentification of particle count, we employ numerical simulation methods for an in-depth investigation. We developed a mathematical model of a three-coil inductive metal particle sensor to explore the signal variations induced by the passage of a single particle. We utilized micro-element simulation analysis to dissect the signal generated by a single particle, elucidating the underlying change process. Focusing on dual ferromagnetic particles as the subject of study, we conducted simulations and demodulation of the induced voltage under various combinations of sizes and spacings to investigate the influence patterns of dual adjacent ferromagnetic particles on the sensor's induced signal. Further research into the peak signals of different diameter particles at a constant spacing revealed that, for a given spacing, the ratio of peak signals between particles of varying diameters and those of a single particle remains relatively consistent. We then extended the scope of the study to simulate multiple adjacent particles, decomposed multi-particle signals based on the characteristics of dual-particle signals, and proposed a method for identifying the number of closely spaced particles. Additionally, using a single-particle signal model, our analysis of multi-particle signals demonstrated that the diameter, quantity, and spacing of particles can be identified to some extent by examining the distances and magnitudes of peaks and troughs in the multi-particle signal. Our findings provide theoretical support and technical references for the accurate identification of multiple particles by inductive sensors.

This study develops an advanced electrochemical model integrated with the Teaching-Learning Based CollectiveIntelligence (TLBCI) algorithm to investigate degradation mechanisms in solid oxide fuel cells (SOFCs), with afocused analysis on nickel (Ni) agglomeration/oxidation at the anode and yttria stabilized zirconia (YSZ)agglomeration at the cathode. Key model parameters are directly extracted from experimental data, enablingaccurate performance prediction. The model systematically evaluates the impact of temperature fluctuations onlong-term SOFC degradation. Compared to conventional methods (Kalman filters, particle filters) and datadriven approaches (Long Short-Term Memory networks (LSTM), Echo State Networks (ESN)), the proposedmechanism-based model achieves superior accuracy, lower Mean Squared Error (MSE), and enhanced predictivecapability in both short- and long-term forecasts. Furthermore, the work provides an in-depth analysis of elec-trochemical performance decay, including the evolution of overpotential components and material properties.This comprehensive degradation framework advances the understanding of SOFC longevity and provides atheoretical foundation for optimizing cell design, improving reliability, and enhancing operational effi-ciency—thereby supporting their commercial and industrial deployment (e.g., in distributed generation andbackup power systems). The findings offer critical insights for boosting SOFC performance under real-worldoperating conditions.

State of charge (SOC) is a key index of lithium-ion batteries, and the equivalent circuit model is widely used in engineering to estimate the SOC of batteries, but its estimation accuracy is poor under complex dynamic conditions. To solve this problem, an improved equivalent circuit model (IECM) is first proposed, which considers the long-term polarization effect due to solid-phase diffusion of lithium-ion, and corrects the voltage by the lithium-ion concentration difference. Then, the parameters of IECM at different ambient temperatures are identified for temperature compensation. Finally, a variable scale unscented Kalman filter (VSUKF) is proposed to improve the dynamic performance of SOC estimation by changing the sampling interval distribution through predicted error. The accuracies of terminal voltage and SOC estimation are compared and validated under different working conditions and temperatures. The results show that the maximum root mean square error (RMSE) and the mean absolute error (MAE) of the predicted terminal voltage of the proposed IECM are 38.3 mV and 15.2 mV, and the maximum RMSE and the MAE of the SOC estimation results of VSUKF are 1.70% and 1.46%. This results show that the proposed model and method have high accuracy and can provide some technical support for SOC online estimation.