With respect to the accurate tracking control for unmanned surface vehicles (USVs), this paper proposes a novel hierarchical control structure consisting of a velocity planner and a tracking controller. With full consideration of control accuracy and tracking safety, a model predictive control planning method based on dynamic artificial potential field method (DAPF-MPC) is proposed, aiming to generate the current optimal reference tracking velocity and achieve online obstacle avoidance guidance. Simultaneously, a fixed-time generalized super-twisting controller based on an extended state observer (ESO-FiTGST) is proposed to restrict the convergence time and tackle the challenges resulting from the uncertain disturbances and model parameters. Noticeably, the design process of the proposed control law and its rigorous stability analysis, especially for the computable fixed-time convergence property, are detailed. Ultimately, the simulation and experimental results demonstrate the superiority and feasibility of the proposed method, providing a reliable and effective reference solution for tracking control tasks of USVs in aquatic scenarios with complex obstacles.
To address the operational challenges of solar-assisted multi-source heat pump systems under fluctuating environments, this study proposes a physics-data integrated Adaptive Model Predictive Control (AMPC) framework. The methodological resides in the synergetic coupling of high-fidelity thermodynamic physical models with stochastic data-driven predictors. This hybrid architecture enables the framework to synchronize Ground Source Heat Pumps, Air Source Heat Pumps, and solar thermal collectors by capturing complex non-linear thermal couplings that pure data-driven models often overlook. During the forecasting phase, multiple machine learning algorithms are evaluated. Results indicate that Long Short-Term Memory achieves superior performance for summer cooling loads with an R2 of 0.990, while LightGBM proves most effective for handling the stochastic fluctuations of winter heating loads, yielding an R2 of 0.96. Regarding control strategy optimization, sensitivity analysis identifies a 12-h prediction horizon and a 3-h control range as the optimal configuration. This setup achieves an 8.3% energy reduction while maintaining a computational response time of 19.6 s, ensuring realtime feasibility. Full-season comparative experiments demonstrate that the proposed AMPC framework offers significant advantages over conventional PID control. The AMPC demonstrates superior robustness by reducing indoor temperature fluctuations by over 65% and lowering the MAE by at least 50% across all seasons. Ultimately, the system secures seasonal energy savings of 12.0% in summer and 8.7% in winter while consistently maintaining high levels of thermal comfort. These findings highlight the potential of AMPC in enhancing the stability and economy of multi-source energy systems, providing an effective technical solution for low-carbon building operations.
Diverse and controllable scenario generation (e.g., wind, solar, load, etc.) is critical for robust power system planning and operation. As AI-based scenario generation methods are becoming the mainstream, existing methods (e.g., Conditional Generative Adversarial Nets) mainly rely on a fixed-length numerical conditioning vector to control the generation results, facing challenges in user conveniency and generation flexibility. In this paper, a natural-language-guided scenario generation framework, named LLM-enabled Frequency-aware Flow Diffusion (LFFD), is proposed to enable users to generate desired scenarios using plain human language. First, a pretrained LLM module is introduced to convert generation requests described by unstructured natural languages into ordered semantic space. Second, instead of using standard diffusion models, a flow diffusion model employing a rectified flow matching objective is introduced to achieve efficient and high-quality scenario generation, taking the LLM output as the model input. During the model training process, a frequency-aware multi-objective optimization algorithm is introduced to mitigate the frequency-bias issue. Meanwhile, a dual-agent framework is designed to create text-scenario training sample pairs as well as to standardize semantic evaluation. Experiments based on large-scale photovoltaic and load datasets demonstrate the effectiveness of the proposed method.
Potential-induced degradation (PID) is a serious disaster for photovoltaic (PV) power plants. However, this problem has never been completely solved, especially in the early stage with high recoverability. In this paper, for the first time, it is found that the leakage current and reverse current of PID-affected PV modules change in a linked manner with the progression of PID. In order to explain this phenomenon, a fine model was first built to analyze leakage current density flowing through PV modules. Then, the impedance spectra of ethylene-vinyl acetate for the PID-affected and PID-free modules were measured by impedance spectroscopy. The result indicates that there are no significant differences in leakage current for PID-affected and PID-free modules. Finally, a PID-recovery experiment was carried out. The changing trends of leakage current and reverse current for PID-affected and PID-free modules were investigated. An important finding is that, for PID-affected modules, there exists the same varying trends for leakage current density and reverse current density. However, for PID-free modules, there is no identical varying trend for leakage current density and reverse current density. This reveals that the possible degradation mechanism leading to cell performance loss in PID-affected modules originates from sodium-decorated stacking faults formed within the pn junction under high system voltage. The obtained results give us a hint that the varying trends for leakage current density and reverse current density could be considered as an accurate indication for detecting PID at an early stage.
To address heat accumulation, localized hot spots, and non-uniform temperature distribution in large-capacity lithium iron phosphate energy storage battery modules under high ambient temperature and high-rate charge/discharge conditions, this study proposes a fin-enhanced phase change material (PCM)-air hybrid thermal management structure for a 100 Ah prismatic lithium iron phosphate battery and a 2P18S energy storage battery module. First, the battery thermal model is validated using single-cell experimental data reported in the literature. Subsequently, a three-dimensional transient fluid-solid coupled heat transfer model is established by considering transient battery heat generation, PCM solid-liquid phase change, air-side flow and heat transfer, and temperature-dependent thermophysical properties. User-defined functions are employed to implement the transient heat source and temperature-dependent material properties. Under identical boundary conditions, the thermal management performances of three configurations, namely Fin-Air, PCM-Air, and Fin-PCM-Air, are compared. The effects of ambient temperature (20 degrees C, 25 degrees C, and 30 degrees C) and inlet air velocity (1 m/s, 2 m/s, and 3 m/s) on the maximum module temperature, temperature uniformity, PCM liquid fraction evolution, and flow field distribution are quantitatively analyzed. The results show that, compared with the Fin-Air system without PCM and the PCM-Air system without fins, the Fin-PCM-Air configuration reduces the maximum module temperature by 1.57% and 0.25%, respectively, at an ambient temperature of 30 degrees C and an inlet air velocity of 3 m/s. After four charge-discharge cycles, the peak maximum temperature of the module is approximately 38.56 degrees C, and the peak maximum temperature difference remains below 3.6 K, indicating good temperature uniformity and latent heat buffering capability. In addition, the air velocity trade-off analysis indicates that increasing the inlet air velocity can improve cooling performance but also increases the air-channel pressure drop and fan power consumption. Therefore, the Fin-PCM-Air structure is more suitable for high-thermal-load conditions, and its practical application should comprehensively consider cooling benefits, additional mass, manufacturing cost, and long-term reliability. This study provides a reference for the design and engineering application of hybrid thermal management structures for large-capacity energy storage battery modules.
