Search Results (127)

Models of thermal runaway propagation in lithium-ion batteries are widely used for thermal safety analysis. Current methods, primarily lumped-parameter and 3D models, face challenges in balancing accuracy with computational efficiency. Three-dimensional models offer high accuracy at high computational cost, while lumped-parameter models are faster but less accurate. For instance, the battery shell is included in lumped-parameter models but often omitted in 3D models. This study focuses on a 37 Ah ternary lithium-ion battery, with Li(NiCoMn)_1/3O₂ as the cathode material and graphite as the anode material. The propagation of thermal runaway in the battery array is triggered by nail penetration. A lithium-ion battery thermal runaway propagation model is proposed, combining the lumped-parameter method with 3D modeling. The model primarily describes the heat transfer characteristics of the shell using a series connection of thermal capacitance and several thermal resistances. The shell temperature is then calculated by weighting the temperatures associated with the thermal capacitance and thermal resistances using specific weight coefficients. The joint model is detailed and applied to study thermal runaway propagation in one- and two-dimensional battery arrays. For the one-dimensional array, based on the three-dimensional simulation data and calculation time, the joint model shows only a 1.32% average deviation in propagation time compared to full 3D simulation, while maintaining good temperature agreement. It also reduces solution time by 70.22%. These findings confirm that the proposed model effectively enhances both the efficiency and accuracy of thermal runaway simulations, supporting improved safety analysis for lithium-ion battery systems. Full article

This study presents a numerical investigation of biogas–natural gas co-firing for greenhouse heating, integrating lumped-parameter energy balance, multi-objective optimization, and life cycle assessment (LCA) for a Syrian coast case study (48 dairy cows, 100 m² greenhouse). Five blends (0–100% biogas) were evaluated using a zero-dimensional model implemented in MATLAB R2024a (The MathWorks, Inc., Natick, MA, USA) and verified with Python (version 3.11, Python Software Foundation, Beaverton, OR, USA). The 70% biogas–30% natural gas blend exhibited the most favorable trade-off among conditionally feasible scenarios (requiring external biogas sourcing) with a model-predicted system thermal efficiency of 84.5% (LHV basis) and a model-estimated thermal NOx reduction of 75–85%, which represents a mathematical extrapolation beyond the experimentally validated range of 0–50% biogas and excludes prompt NOx (5–20% of total) and should be interpreted as an indicative trend requiring experimental confirmation. For self-sufficient operation using only on-site biogas production (24 m³ day⁻¹), the maximum achievable blend is 32% biogas, offering a 13.8% cost reduction and a 13.5% GWP reduction. Pure biogas achieves a 41.5% GWP reduction and 48.5% lower daily operating costs under the assumption of expanded on-site production capacity but requires 3.3 times the current production volume. Multi-objective optimization reveals stakeholder-specific optima ranging from 50% to 91% biogas, with a robust compromise region of 65–75%. All predictions for NOx emissions above 50% biogas are mathematical extrapolations requiring experimental validation. For farms without access to external biogas markets, the 32% blend (self-sufficient optimum) is the currently implementable solution, offering a 13.8% cost reduction. For farms with access to regional biogas markets, the 70% blend represents the conditional techno-economic optimum, achieving a 15.3% cost reduction but requiring 29.12 m³ day⁻¹ of external biogas procurement. Full article

Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems. Full article

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS). Full article

Proton exchange membrane water electrolysis is a promising technology for large-scale green hydrogen production due to its high efficiency, compact design, and rapid dynamic response. However, its commercialization is strictly limited by high material costs, durability issues, and complex multiphysics coupling within the membrane electrode assembly. This work provides a comprehensive and critical review of key physicochemical processes and advanced predictive modeling approaches for PEMWEs. To capture recent paradigm shifts, we introduce an innovative multi-dimensional classification framework—incorporating spatial resolution, temporal dynamics, and methodological paradigms—to critically evaluate lumped-parameter, continuum, microscale, and multiscale models, explicitly defining their applicability bounds and inherent limitations. The fundamental mechanisms governing electrode kinetics, membrane water transport, and gas–liquid two-phase flow are analyzed, establishing state-of-the-art quantitative benchmarks for microstructural parameters and advanced 3D flow field topologies under high-current-density and high-pressure regimes. Furthermore, we systematically examine model validation rigor, typical prediction errors, and the critical failure of static models in capturing dynamic property shifts during extreme bubble breakthrough. Recent breakthroughs integrating in situ diagnostics, pore-scale simulations, density functional theory, and Physics-Informed Neural Networks are extensively discussed. Future efforts must prioritize mechanical–electrochemical–thermal coupling, transient degradation prognostics, and machine learning-driven predictive digital twin technologies to overcome current empirical limitations and accelerate the gigawatt-scale deployment of PEMWE systems. Full article

Lithium-ion battery (LiB) ageing is a critical challenge that requires in-depth investigation to extend the useful life of electric vehicles (EVs). This phenomenon drastically impacts cell performance and is primarily influenced by environmental factors and operating conditions, such as charge/discharge rates and the State of Charge (SoC) during rest periods. This study investigates the impact of vehicle operational duty cycles on battery pack (BP) longevity through combined experimental and numerical evaluations. To this end, a lumped electro-thermal BP model was developed and validated at the single-cell level. Furthermore, a capacity fade model, customized for the specific cell chemistry and capacity, was implemented based on the literature benchmarks. The analysis considers user-related parameters, including driving style, charging strategies, and ambient temperatures. The results suggest that aggressive driving significantly accelerates BP ageing when combined with conservative charging strategies in warm climates. Additionally, adopting high DoD values can reduce useful life by up to 30%, while high temperatures can double the rate of capacity fade. Regarding C-rates, fast-charging operations predominantly impact degradation when non-conservative strategies are employed, particularly in cold environments. Full article

In this paper the design, modelling, and performance assessment of a miniaturised dual-purpose optical instrument for small satellites are presented. The instrument can function as a star tracker and as a space-debris detection camera. The system integrates commercial off-the-shelf components, i.e., a CMOS sensor, a processing unit and lens assembly, together with a custom three-vane optical baffle optimised for stray-light suppression. A complete numerical evaluation was conducted through optical ray-tracing, lumped-parameter thermal modelling, and structural finite-element analysis to validate the instrument prior to hardware testing. Optical simulations confirmed effective stray-light suppression and acceptable Point Source Transmission behaviour, enabling signal-to-noise ratio performance suitable for star and debris detection up to ∼5.8 mag. The resulting instrument, with a mass of approximately 172 g and dimensions of 105 mm × 52 mm × 52 mm, demonstrates a compact, low-cost, and multifunctional solution for small-sized platforms. Future work includes environmental testing and on-orbit demonstration to prepare the system for flight qualification. Full article

Online temperature estimation of key components (windings and magnets) in permanent magnet synchronous motors (PMSMs) has emerged as a critical technology for ensuring the safe operation of PMSMs, preventing insulation degradation, and avoiding the demagnetization of magnets. Because of such advantages, online temperature estimation is attracting growing attention from fields with stringent reliability requirements, such as electric vehicles, as well as electrified railway transportation and more/all-electric aircraft, where similar high-reliability demands exist. This paper gives a comprehensive review of the latest and most effective solutions in the online temperature estimation methods for PMSMs. It analyzes the principles, application progress, and limitations of existing methods, including electrical model-based approaches, thermal model-based approaches, and data-driven approaches, in which process the advantages and challenges of different methods are compared. And an outlook on the future application of this technology are summarized. Full article

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets. Full article

The independent application of conventional electrical or thermal models is, generally, not adequate to model the interdependence between temperature distribution, heat transfer mechanisms, and the electrical performance of Photovoltaic (PV) generators. In this context, coupled thermal–electrical modeling approaches have recently gained increasing importance to accurately simulate the PV performance. This work presents a comprehensive and systematic analysis of electrical, thermal, and coupled thermal–electrical models developed for PV modules. Electrical models are classified into analytical/physical, semi-empirical, and empirical classes, highlighting their assumptions, parameter requirements, computational complexity, and applicability at cell, module, and system levels. Thermal modeling approaches are reviewed by distinguishing lumped parameter and thermal network models from spatially distributed numerical methods. Particular emphasis is placed on the ability of these models to represent non-uniform temperature distributions and transient operating conditions. Furthermore, this review critically examines state-of-the-art coupled thermo-electrical models, focusing on different coupling strategies, feedback mechanisms, and levels of spatial resolution. The advantages and limitations of each modeling approach are discussed in relation to accuracy, computational cost, and suitability for performance prediction, fault analysis, and reliability assessment. Finally, current research gaps and future directions are identified, providing a structured framework to guide the selection of the most appropriate model and the development of more accurate and physically consistent PV modeling strategies under complex and realistic operating conditions. Full article

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online. Full article

This paper presents a mathematical approach for analyzing the thermal behavior of a dual-permanent-magnet consequent-pole magnetically geared machine. The analytical method, referred to as harmonic modeling, employs a complex Fourier series and the Cauchy product to obtain solutions to the partial differential equations governing the temperature distribution in electrical machines. Unlike lumped-parameter thermal networks that provide only average quantities, the proposed approach enables the prediction of spatial temperature distributions. The machine is further investigated under various operating conditions, including different convection coefficients and loss levels. An 11-pole, 18-slot prototype was evaluated by comparison with finite element method (FEM) simulations. The results demonstrate that the proposed method agreed well with the FEM results, with errors below 10%, while requiring less than 2 s per calculation compared with approximately 20 s for FEM simulations. Full article

To solve slot temperature accumulation in high thrust density permanent magnet linear synchronous motors (PMLSMs), this paper proposes an additive manufacturing (AM)-based variable cross-section winding design for coating-cooled tapered PMLSMs. Integrating the magnetic circuit features of tapered PMLSMs and AM windings’ technical merits, the motor’s operating mechanism and electromagnetic distribution are analyzed. With the coating cooling structure as the thermal management foundation, simulation reveals the motor’s temperature distribution under water cooling, defining core slot thermal management requirements. A novel cross-section winding design is then presented: first, a lumped-parameter thermal network model quantifies the coupling between the winding cross-sectional area and slot heat source distribution; second, a greedy algorithm optimizes the winding cross-section globally to reduce the slot hot-spot temperature and suppress temperature rise. Validated by a fabricated tapered PMLSM stator prototype and static temperature-rise experiments, the results confirm that winding cross-section reconstruction optimizes heat distribution effectively, offering a new approach for temperature rise suppression in high thrust density PMLSMs. Full article

Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article

A semi-empirical lumped parameter model for the extraction process of traditional Chinese medicine based on thermal equilibrium was established in this work. In this model, the effect of heat dissipation was considered. Differential equations was solved using numerical methods. Key model parameters such as the overall heat transfer coefficient and heat dissipation coefficient were obtained by fitting measured data. In the laboratory scale, Ginkgo biloba leaves were used as the liquid-solid extraction object to systematically investigate the effects of liquid-to-solid ratio, extraction temperature, solvent ratio, and slice particle size on the temperature changes during the extraction process. The average determination coefficient (R²) of the model fitting was 0.9955, and the R² value for the prediction group was 0.9950. In the laboratory scale, extraction experiments of Xiaochaihu Decoction were conducted, and the performance of the model was verified. Furthermore, the model was applied to the mixed decoction process of five medicinal materials (Bupleurum, Glycyrrhiza, Scutellaria, Codonopsis, and Jujube) in industrial-scale for the production of Xiaochaihu capsules. The temperature change curves of three extraction tanks were all fitting well. The fitting results indicated abnormal heat transfer performance in Tank No. 1, providing a prompt for equipment maintenance and process optimization for the enterprise. A feasible method for temperature calculation and abnormal identification in the industrial process of traditional Chinese medicine extraction was provided in this work. Full article