Predicting subsurface temperatures is critical for comprehending ocean dynamics and climate shifts. This study presents an incremental stable/dynamic (SD) disentanglement learning framework merging data-driven methods with physics-based insights. It separates stable and dynamic temperature modes to untangle intricate spatiotemporal interactions. To accommodate ongoing data influx, we introduce a recursive evolution approach for updating stable representations, employing orthogonal-triangular decomposition (QR) to capture incremental information. Moreover, a retrospective learning algorithm, guided by temporal changes and ocean temperature correlations, is employed to track the dynamic behavior adaptively. The subsurface temperature fields can be efficiently reconstructed and predicted after model convergence. Extensive experiments validate the model across various depths (−2.5 to −800 m) and times (from May 1964 to December 2021), achieving robust performance metrics: root mean square error (RMSE) of 0.1362, mean absolute error (MAE) of 0.0901, accuracy (ACC) of 0.9911, and coefficient of determination ($R^{2}$) between predictions and observations of 0.9998. Comparative analysis underscores the proposed method’s interpretability, adaptability, and overall performance superiority. Temperature anomaly analysis accurately identifies subsurface decadal oscillations in the low- and mid-latitude Pacific regions.

Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This paper presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.

Temperature is a key factor affecting the safety of the Lithium-ion (Li-ion) battery. Therefore, real-time thermal fault diagnosis is becoming more and more prominent, as battery faults can lead to local overheating and thermal runaway in severe cases. This article proposes a Chebyshev–Galerkin-based thermal fault detection and localization framework for the pouch-type Li-ion battery under limited sensing. First, the Chebyshev function is used to construct the spatial basis functions with global and orthonormal properties. Under the time–space (T-S) separation framework, the time coefficients can be derived through the Galerkin method using six sensors. Then, by decomposing the time coefficients using the independent component analysis, the temporal and spatial reference statistics can be formed for real-time fault detection. Finally, considering the detected fault snapshots, the thermal fault location can be identified by finding the maximum contributed position through T-S synthesis. Simulations and experiments demonstrate the effectiveness of the proposed method.

Control-oriented thermal models are essential for onboard temperature monitoring of lithium-ion batteries in automobile applications. This work develops a Galerkin-spectral model for the 3-D thermal diffusion in pouch cells. First, a full-order model that depicts the battery thermal phenomenon is introduced from a physical point of view. Considering different physical properties in each area, we apply a space decomposition approach to decouple the interactive thermal effects between the cell core and tabs. Then, mild approximations are made to generate a more succinct model governed by the partial and ordinary differential equations. Finally, a low-order representation is extracted from the original infinite-dimensional system by employing spectral expansion on the spatiotemporal variable. Experimental and simulation studies indicate satisfactory reduced-order performance and practical validity of the proposed model.

The thermal effect has a significant impact on the performance and durability of lithium-ion batteries. This article proposes a systematic approach for fast modeling of the distributed battery thermal process. In this method, the time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the T/S separation, an incremental-learning-based regulator is first employed for the recursive update of spatial basis functions, which can represent the most recent spatial complexity. Then, a corresponding temporal model with incremental adaptive characteristics is developed to capture the temporal nonlinearity. Under such a fully adaptive modeling pattern, the desired temperature distribution can be reconstructed with high efficiency and flexibility. Experimental studies indicate that the proposed method can achieve satisfactory modeling performance while its computational efficiency is outstanding compared to peer methods.

Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This article proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method.