Yujie Wang

Accurate state-of-health estimation is critical for the safe and reliable operation of Lithium-ion batteries, but developing robust data-driven models is often hindered by data privacy concerns and the distributed nature of the data. Federated learning (FL) offers a privacy-preserving solution, yet its performance is severely challenged by the statistical heterogeneity and data imbalance inherent in real-world distributed battery data. Furthermore, the opaque nature of conventional FL models impedes the cross-institutional trust required for collaborative training. To address these limitations, this article proposes a novel multiphysics-informed adaptive personalized federated learning (MP-APFL) framework. At the client level, we first develop a multiphysics-informed, multistage aging partial differential equation (PDE) that combines analytical models for key degradation mechanisms, such as solid electrolyte interphase formation, thermal effects, and lithium plating-induced “knee points,” with a data-driven term to account for complex unmodeled dynamics. Subsequently, this governing PDE is embedded as a soft constraint within a multiphysics-informed neural network via an adaptively weighted composite loss function that synergistically enforces physical consistency, maintains parameter realism, and ensures trend plausibility by penalizing unphysical jumps while permitting valid recovery, guiding each local model to learn physically plausible and interpretable aging dynamics. At the system level, we design MP-APFL to employ a privacy-preserving hybrid architecture with a shared encoder and personalized multiphysics components. This is orchestrated by an adaptive strategy featuring Information value-driven client prioritization for efficient client selection and physics-guided and stability-aware encoder aggregation for robust model fusion. Comprehensive experiments on laboratory-generated and public datasets demonstrate that MP-APFL significantly outperforms prominent FL benchmarks, particularly in highly heterogeneous and imbalanced settings.

System configuration represents the core decision stage in system design and the physical basis for dynamic power distribution. This study develops a bi-level optimization framework to address multi-timescale coupling in fuel cell hybrid power system configuration for unmanned aerial vehicles. Under multiple performance constraints, the upper-level system configuration layer employs the self-organizing surrogate-assisted non-dominated sorting differential evolution algorithm to balance hydrogen utilization efficiency, lightweight design, and endurance requirements while ensuring operational safety and fault tolerance. The lower-level energy management layer applies dynamic programming to evaluate upper-level solutions. Configuration results for the DJI T10 prototype indicate a fuel cell rated power of 2.5 kW, battery energy of 395.16 Wh, and hydrogen tank volume of 12 L. Comparative experiments demonstrate the framework’s effectiveness in reducing computational burden while maintaining applicability to various multirotor unmanned aerial vehicle configurations and mission requirements, exhibiting strong generalization capability. Applicability boundary analysis confirms that fuel cell hybrid power systems are well-suited for heavy-load, long-endurance missions under both current and future technology scenarios.

External short circuits (ESCs) in lithium-ion batteries can cause rapid temperature rise and safety issues. This study proposes an online EIS-based method for diagnosing and assessing the severity of ESC faults. By integrating real-time electrochemical impedance spectroscopy (EIS) measurements with characteristics of external short circuits, the approach enables dynamic fault evaluation. Prior to a fault, EIS data are used to fit a fractional-order model and extract parameters. During an ESC event, voltage–current data are fitted via nonlinear least-squares to a double-exponential model to update key parameters, while time-domain features including peak temperature and voltage drop are simultaneously extracted. These parameters and features are fed into a regression model that predicts fault duration as a severity indicator. An ESC evaluation index is then derived and classified into three severity levels: mild, moderate, and severe. Experimental results demonstrate that the method achieves high accuracy and clear severity grading, proving suitable for real-time ESC assessment in battery management systems.