Search Results (775)

This study presents an electrothermal coating approach for synthesizing cell-imprinted polymers (CIPs) on metallic microwires through localized resistive heating-induced polymerization. Imprinted polymers (IPs) are robust, cost-effective synthetic affinity materials widely used in sensing applications. However, conventional fabrication methods, including bulk and suspension polymerization, often lack spatial control, producing non-specific polymerization, heterogeneous coatings, and reduced sensor reproducibility. Electrochemical polymerization provides improved spatial control but requires specialized instrumentation and restricts monomer selection. Here, applying direct current (DC) to metallic microwires immersed in a prepolymer solution generated localized Joule heating, enabling controlled in situ polymerization and uniform coatings while minimizing undesired bulk polymerization. By optimizing the applied current and polymerization time, CIP coatings with tunable thicknesses were fabricated on gold-coated microwires. Under optimized conditions, ~6 µm thick coatings were imprinted using Salmonella templates. Scanning electron microscopy revealed bacteria-shaped cavities consistent with template removal and the formation of imprinted cavities. Rebinding experiments demonstrated enhanced bacterial capture, with CIP-coated microwires achieving ~70% capture efficiency, compared to 22% for bare microwires and 33% for non-imprinted polymer (NIP) controls. These results support the effectiveness of the proposed method for localized polymerization and demonstrate the enhanced capture of the template species by CIP-coated microwires relative to bare microwires and NIP-coated controls. Full article

Extremely fast charging (XFC) of high-energy-density lithium-ion batteries is fundamentally constrained by the intrinsic waveform characteristics of conventional variable-current profiles (VCPs), limiting further reductions in charging time while maintaining electro-thermal safety. In the present study, a computational electro-thermally constrained optimization framework is developed in which a square-wave VCP is reformulated using a finite Fourier series to improve XFC performance. An electro-thermal numerical model is employed to evaluate the charging behavior of the resulting Fourier series-based square wave (F-square wave) with the number of harmonic terms ranging from N = 1 to 100. The optimal charging performance is achieved at N = 10, reducing the charging time from 940 to 878 s (6.6%) and satisfying the U.S. DOE 15-min XFC target (900 s). The performance enhancement originates from two complementary effects: the Gibbs overshoot, which locally increases the charging current near the allowable current limit, and the finite-series approximation, which smooths the current transition before and after the waveform discontinuity. Rather than treating the Gibbs overshoot associated with Fourier approximation as an undesirable numerical artifact, this study demonstrates that it can be computationally exploited as a controlled perturbation to accelerate charging while maintaining electro-thermal safety. Although the Fourier perturbation slightly increases the terminal voltage risk near the waveform discontinuity, all electrical and thermal constraints remain satisfied throughout the charging process. These findings demonstrate that finite Fourier perturbation provides an effective computational design strategy for overcoming the intrinsic waveform limitations of discontinuous charging profiles and advancing electro-thermally constrained XFC of lithium-ion batteries. Full article

Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrative review—conducted with a systematic search strategy following PRISMA reporting guidelines—of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems and the development of functional surface materials including superhydrophobic coatings (SHC); composites with conductive nanofillers (graphene, carbon nanotubes); and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a projected reduction in anti-icing system energy consumption of at least 40% relative to conventional continuous heating. This estimate is based on the most conservative published evidence: pulsed electrothermal de-icing achieves 40–60% savings versus continuous anti-icingSHC-assisted hybrid heating reduces IPS power by more than 80% on static aerofoils; and rotary-wing pulsed systems reduce mean consumption by 60–75% relative to continuous operation. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms. Full article

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an $R^2$ value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications. Full article

Surface icing poses a significant risk to unmanned aerial vehicles (UAVs) and compact aerospace platforms, where limited onboard power and space require efficient anti-/de-icing surfaces. In this study, micro/nanostructures were fabricated on TC4 titanium alloy (Ti–6Al–4V) surfaces by femtosecond laser processing at different scanning speeds. The effects of scanning speed on surface morphology, wettability, static freezing, dynamic droplet behavior, and electrothermal de-icing performance were systematically investigated. Increasing the scanning speed induced nonlinear changes in microstructure height and surface roughness, while variations in ablation intensity caused nonuniform material redistribution. The surface processed at 250 mm/s showed the best anti-icing performance, with a water contact angle of 157.5 ± 0.5° and a maximum freezing delay 21.5 times longer than untreated TC4. During electrothermal de-icing, melting initiated at discrete ice–substrate contact points, forming coalesced meltwater films, while interfacial stress concentration promoted crack propagation and rapid ice detachment. Compared with untreated surfaces, ice detachment time (250 mm/s) achieved complete ice detachment at approximately 152 s, whereas ice on the untreated surface remained adhered after 270 s of continuous heating, representing a de-icing time reduction of at least 44%. These results demonstrate that combining laser-fabricated microstructures with electrothermal heating effectively reduces real ice–substrate contact, providing an enhanced anti-/de-icing strategy for lightweight, long-endurance UAV applications under identical electrical input. Full article

In this paper, a fault diagnosis method based on temperature rise detection is proposed for power converters in switched reluctance motor drive systems used in electrical transportation equipment. First, the total power losses of all power devices are calculated and recorded under different operating conditions in both healthy and faulty states. A finite-element electrothermal model is then established to characterize the relationship between fault-induced power-loss redistribution and variations in the temperature rise of the converter devices. Based on the power-loss analysis, temperature rise is used as a key characteristic, and a corresponding fault diagnosis method is proposed. To account for the influence of operating conditions on the diagnostic criterion, three independent backpropagation neural network (BPNN) models are developed to predict fault-specific temperature-rise thresholds using rotor speed, load torque, and ambient temperature as inputs. During diagnosis, the real-time temperature evolution of the power diodes is compared with the selected thresholds to detect power converter faults. Finally, experimental results demonstrate the validity of the proposed fault diagnosis method. Full article

The conductor temperature of cross-linked polyethylene (XLPE) high-voltage direct-current (HVDC) cables governs ampacity assessment and insulation life management, yet it cannot be measured in service, and finite-element simulation is too expensive for real-time use. This paper presents a physics-informed neural network (PINN) that embeds the transient heat-conduction equation, a temperature-dependent Joule source, and the boundary and initial conditions into the training loss of a neural surrogate. Three ingredients adapt the framework to power cables: a layer-aware material mapping over the eight heterogeneous cable layers; an electro-thermal coupling through the temperature dependence of the conductor conductivity, handled during training by a convergent Picard-type evaluation of the Joule source; and a condition-embedding input treating the load current and ambient temperature as continuous parameters so that a single network covers the admissible current–ambient envelope of the studied cable configuration. Validated against finite-element references under fifteen operating conditions, the model attains a root-mean-square error of $0.0024$ K (mean over five training seeds) on a held-out condition relative to a finite-element reference whose mesh-discretization error a refinement study bounds at about $0.04$ K while reducing the governing-equation residual by approximately 28-fold relative to an identically sized data-driven network at statistically indistinguishable pointwise accuracy. The physics prior also renders degradation under training-data reduction more graceful and improves extrapolation to unseen ambient temperatures, whereas current extrapolation remains the most challenging transfer. The differentiable surrogate identifies the load current and the unmeasurable conductor hotspot from ten surface sensors within seconds, at below 9 ms per ${10}^5$ queries. A loss-weight sensitivity study and a three-dimensional cable-end-effect case on a second material configuration are also reported. All reference data are numerical; experimental cable-loop validation remains for future work. Full article

Dielectric barrier discharge (DBD) plasma actuators have attracted increasing attention for anti-icing and de-icing applications due to their ability to combine surface heating and plasma-induced flow control. Their electro-thermal behavior is strongly influenced by exposed electrode material, geometry, and dielectric configuration, often leading to localized hot-spot formation and reduced operational stability. However, the impact of exposed electrode material and electrode thickness remains poorly understood, representing a significant gap in understanding the electrical and thermal response of these devices. This study presents an experimental electro-thermal investigation of DBD plasma actuators employing copper, aluminum, and hybrid copper–aluminum exposed electrodes. Copper and aluminum were selected as exposed materials because they present two contrasting electrical–thermal extremes. The actuators were tested using dielectric barrier thicknesses of 1 mm and 2 mm, considering both standard and enlarged (10 times) exposed electrode thickness. The electrical diagnostics show that aluminum electrodes promote stronger and more uniformly distributed microdischarges due to enhanced discharge initiation, but at the expense of increased power consumption. In contrast, copper electrodes exhibit lower power demand but lead to concentrated current density and localized thermal hot spots. Motivated by this electrical–thermal trade-off, a hybrid electrode was developed to combine the high electrical stability of copper with the discharge uniformity of aluminum. The hybrid configuration demonstrates intermediate power consumption and significantly improved thermal uniformity, effectively mitigating hot spot formation. These results highlight the importance of exposed electrode electrical properties in the electrical and thermal characterization of DBD plasma actuators and identify the hybrid configuration as a promising solution for future thermally driven ice-mitigation applications. Full article

Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) are emerging as the technology of choice for next-generation power conversion systems, offering switching speeds, on-state resistance, and power density unattainable with silicon or even silicon carbide (SiC) devices. However, the fast switching transients that make GaN attractive also make it a demanding source of electromagnetic interference (EMI), so credible electromagnetic compatibility (EMC) analysis requires an accurate functional device model. This paper addresses the functional electrothermal modeling of a commercial 650 V GaN HEMT (GS66504B) as a prerequisite to EMC validation. The manufacturer-supplied Level 3 SPICE model is evaluated against experimental static (I-V) and dynamic (C-V) measurements. Significant discrepancies motivate an optimization methodology in which an initial manual procedure is superseded by a fully automated pipeline coupling LTspice with a Genetic Algorithm in MATLAB R2025b. A forward/reverse and dual-temperature-segment strategy reduces the mean absolute relative error to below 7% (forward I-V) and 13% (reverse I-V) over 25–100 °C, while a dedicated two-stage C–V optimization reduces the reverse-transfer capacitance error from 95.4% to 2.89%. The resulting compact, unified, and fully validated model underpins the ongoing EMC validation phase, where it will be combined with extracted parasitic and cable models in a DC-DC converter topology. Full article

Against the backdrop of accelerating transitions to sustainable energy systems, the optimal operation of microgrids and the high-efficiency integration of renewable energy face growing technical challenges, which highlight the necessity of tapping into flexible multi-energy resources to the fullest extent. Aiming at low-carbon microgrid systems with electro-thermal demands, this paper proposes a sustainable dispatch strategy that actively integrates waste heat recovery and building thermal inertia. First, a refined mathematical model of a Hybrid Energy Storage System (HESS) is developed, considering waste heat recovery processes from the electrolyzer and the fuel cell. Second, an optimal dispatch model considering the HESS and building thermal inertia (BTI) is constructed, the PMV index is adopted to quantify the adjustable margin of user thermal demand, and the objective function accounts for multiple economic and environmental indices, including energy procurement costs, equipment maintenance costs, and carbon emission trading costs. Case studies show that this strategy can effectively enhance the regulation flexibility of the system, significantly reducing comprehensive operational costs by up to 51.6% and improving local renewable energy accommodation while ensuring environmental sustainability and low-carbon operation. Full article

This study investigates the repetitive overload turn-off behavior of a 1200 V/15 A trench field-stop IGBT using a clamped inductive load circuit. The DC-link voltage is set to 600, 800, and 1000 V, while the external circuit configuration and the 2 ms pulse interval remain unchanged. The displayed sequences at 600 and 800 V exhibit no destructive failure. Under the 1000 V condition, 24 of 30 devices fail, and the cycle-to-failure ranges from 6 to 27. Before failure, the peak V_CE, turn-off current, and E_off reach 1248.1 V, 80.2 A, and 12.87 mJ, respectively. During the final destructive event, V_CE first recovers to approximately 1.16 kV and then collapses toward zero, while I_C re-grows to approximately 106.8 A. A statically validated two-dimensional simulation model shows pulse-to-pulse temperature accumulation, mobility reduction, expansion of the high-field and impact-ionization regions, and persistence of an electron-rich transport path near the trench-gate active region. Post-failure SEM reveals a filament-like damage trace, emitter-side Al damage, damaged trench-gate structures, and contiguous multi-cell ablation. The combined evidence indicates that repetitive heating progressively strengthens the coupling among carrier transport, electric-field concentration, avalanche generation, current localization, and self-heating, which leads to delayed localized electrothermal instability. An auxiliary RC-IGBT comparison further confirms that the repetitive overload turn-off boundary depends on the device technology and operating conditions. Full article

Accurate time-to-empty (TTE) estimation for smartphones remains challenging because battery electrochemical dynamics interact with highly variable device-level power demands. This study develops the Fusion Mechanism-Coordinated Dynamic Model (FM-CDM), a physics-informed theoretical framework that couples a second-order Thevenin equivalent-circuit model with component-level power consumption, electro-thermal feedback, battery state of health, and stochastic workload generation. TTE is formulated as the first time at which the terminal voltage reaches a 3.2 V cutoff, thereby distinguishing the shutdown condition from SOC = 0%. The algebraic coupling among device power, discharge current, and terminal voltage is resolved using the physically admissible solution of the constant-power load equation. Separate battery and processor thermal states are introduced to represent temperature-dependent internal resistance, battery heat generation, heat dissipation, and processor thermal throttling. Four representative workload classes—Standby, Light, Medium, and Heavy—are considered, with 500 Monte Carlo realizations used for each class to propagate workload and parameter uncertainty. Global sensitivity is evaluated using the Morris elementary-effects method. The illustrative numerical analysis shows that increasing component activity shortens model-estimated runtime and that voltage-triggered shutdown can occur at a nonzero residual SOC, particularly when temperature-dependent resistance and load-induced voltage drop become significant. The framework provides a transparent and reproducible basis for investigating smartphone battery depletion, uncertainty propagation, and mechanism-level energy-management strategies under explicitly defined reference conditions. Full article

To address the challenge of balancing junction temperature prediction accuracy and computational efficiency for high-power multi-chip IGBT modules in automotive applications during complex electro-thermal conversion processes, this study proposes an improved three-dimensional thermal network model based on equivalent power loss injection. Firstly, the effective heat conduction area of each packaging layer under actual heat flow distribution is extracted through three-dimensional finite element simulation, and the single-chip self-heating network parameters are constructed. Secondly, targeting the thermal cross-coupling effect among multiple chips, an elliptical thermal diffusion model is applied to accurately define the thermal coupling region, and a dynamic equivalent power loss compensation mechanism is introduced. Efficient decoupling of multi-heat-source interference is achieved without increasing the state-space dimension of the model. An experimental benchmarking results comparison indicates that the absolute error of junction temperature prediction by this model under steady-state operating conditions is 0.5 °C. Further comparative analysis under the full CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car) cycle verifies that the improved model not only overcomes the shortcomings of the traditional Foster model, which severely underestimates the transient peak junction temperature and alternating stress amplitude, but also effectively filters out non-physical overshoots caused by short-term ultra-narrow pulses, thus reasonably estimating the device’s maximum junction temperature within the real physical boundary. This method provides efficient theoretical support for accurate dynamic junction temperature predictions and reliability evaluations of electric vehicles under complex operating conditions. Full article

The electrical drive and the electrical motor share the same housing in Integrated Motor Drives (IMDs), which directly affects the reliability of failure-prone components in such a system. Existing studies are often conducted without considering system-level electrothermal reliability interactions in IMDs. This paper proposes a framework for electrothermal modeling for reliability analysis of IMDs during the early design phase. The framework is based on a back-to-back converter as an emulation platform adaptable to different high-speed electrical machines through software reconfiguration alone. It follows two stages: first, it converts the real-world mission profile, including high-speed operation, into load current commands and motor power loss. Secondly, the thermal network modeling accounts for thermal coupling between the components and the motor, which affects the junction temperature and the hot-spot temperature of the DC link capacitor. The parameters of the thermal network of the electrical motor can be the result of a multiphysics simulation or a real available motor. This framework enables reliability assessment considering motor thermal effects in the early design phase without requiring a physical motor prototype, while providing a fast and cost-effective approach for reliability evaluation. The experimental tests are performed to validate an electrothermal modeling framework capable of thermal modeling and reliability analysis. In addition, the reliability analysis of the power device is carried out by doing the simulation results using data from a real motor, selected for integrated power converter applications. The results demonstrate that the thermal interaction between the motor and the electrical drive causes an $11.5\%$ reduction in the predicted $B_{10}$ lifetime compared with the non-integrated configuration. The non-integrated configuration exhibits approximately 20,000$\mathrm{km}$ longer lifetime than the integrated configuration, highlighting the importance of considering motor-drive thermal coupling in IMD reliability assessment. Full article

As a widely used encapsulant for power electronic devices, silicone gel is continuously exposed to high voltage and high temperature during service, which seriously impairs the reliability and service lifetime of power modules. This work investigates the electrothermal coupling failure mechanism of conventional silicone gel under high-voltage and high-temperature environments; the influences of different pulse electric field edge times and aging stages on various properties of the material are investigated, including electrical treeing characteristics, breakdown field strength, amplitude of charge-excited molecular vibration, leakage current, and cone penetration. It is revealed that the failure mechanism of silicone gel is attributed to the synergistic effect between dynamic charge damage induced by the pulse edge electric field and the degradation of the solid–liquid two-phase structure at high temperatures. This research provides theoretical support and experimental basis for material composition modification, structural optimization, and improving the encapsulation life of power electronic devices. Full article