Search Results (40)

Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C to 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle’s power profile, as well as the evolution of SoC, SoH, and battery life cycle. The results demonstrate an accuracy of 95.3% for state of charge (SoC) estimation, with a mean absolute error of 4.7%. For state of health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for life cycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility. 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

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

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support. Full article

Efficient thermal management is a critical constraint for the performance, safety, and lifetime of electric vehicle (EV) batteries, particularly under transient high-power operation, where conventional dielectric coolants remain limited by the absence of thermal buffering. This Perspective examines PCM–dielectric hybrid coolants as a multiphase electro-thermal-fluid system, in which microencapsulated phase-change materials provide localized latent heat storage within a circulating insulating medium. Rather than proposing a new material concept, the work establishes a system-level engineering framework that links material properties, transport behavior, and electrical constraints to practical implementation. Key challenges, including dispersion stability, capsule durability under coupled stresses, dielectric reliability in heterogeneous media, and rheological limitations, are analyzed alongside quantitative design envelopes and validation pathways. A structured roadmap is presented, spanning multiphysics modeling, accelerated material qualification, system-level testing, and industrial integration, supported by techno-economic and lifecycle considerations. Full article

With the growing trend toward insulator hybridization, higher requirements are imposed on the synergistic improvement of interfacial durability and pollution flashover performance. Machining annular grooves at the green-body stage and embedding silicone rubber enables the construction of an embedded structure with improved durability, forming hydrophilic/hydrophobic alternating surfaces. However, the outdoor insulation characteristics of such hybrid surfaces remain insufficiently investigated, and their engineering feasibility requires further validation. In this study, a series of hydrophilic/hydrophobic alternating surfaces were fabricated, and artificial pollution tests were conducted. The results show that the AC pollution flashover voltage exhibits a saturated increasing trend as the hydrophobic interfaces become more dispersed. When twenty 4 mm wide hydrophobic interfaces were distributed along a 16 cm creepage distance, the flashover voltage was 12.4% higher than that of a fully hydrophobic surface. These results indicate that appropriate design of hydrophobic interface distribution can achieve excellent pollution flashover performance even at relatively low hydrophobic coverage (≤50%). High-speed imaging combined with infrared thermography reveals the discharge mechanism governed by hydrophobic interface distribution from an electro–thermal coupling perspective. The coexistence of multiple dry bands induced by discrete hydrophobic interfaces is identified as the key factor enhancing flashover withstand capability. A static pollution flashover model was established to quantitatively estimate the AC flashover voltage, confirming the external insulation feasibility of the embedded hybrid concept. Full article

Lithium-ion batteries in hybrid solar-grid systems experience complex electro-thermal dynamics and stochastic mode switching that threshold-based battery management systems fail to capture. This paper proposes a hybrid deviation detection framework that treats anomaly detection as a trajectory-consistency problem over a power-feasible Markov jump nonlinear system. A disturbance-robust invariant operating region is first established under explicit current bounds. A reachable-set equivalence is then derived, linking residual consistency to disturbance-augmented trajectory membership. Building on this structure, Isolation Forest empirically estimates the support of admissible electro-thermal trajectories, capturing nonlinear and mode-dependent behaviors not fully described by the analytical disturbance model. A unified sequential detection rule integrates structural constraint violations, model-based residual deviations, and empirical support inconsistencies into a coherent real-time monitor. The framework is validated on a hybrid solar-grid platform with a 6 W photovoltaic panel, a 3.7 V 1820 mAh lithium-ion battery, and a Raspberry Pi, collecting 3976 samples over four days. Results demonstrate early detection of depletion events and mode-transition anomalies before hard threshold violations, with zero false alarms during steady operation and an overall deviation rate of 4.8%, aligning with the configured contamination level. Early warning was observed at 20% state of charge, providing a 10% margin before the hardware threshold of 10%, while 88% of detected anomalies occurred in sequences, validating the persistence rule. Real-time inference required 47 ms per cycle with a 156 MB memory footprint, confirming edge deployment feasibility. Full article

Motor temperature prediction is critical for ensuring the reliability and safe operation of high-power-density electric drives. Since direct measurement of internal temperatures, especially rotor and magnet temperatures, is often impractical, virtual sensing has become an important research direction. This review provides a structured clarification of motor temperature prediction technologies. First, the physical foundations of motor thermal behavior are revisited, emphasizing multi-source loss generation, electro-thermal coupling mechanisms, and the dominant influence of time-varying boundary conditions. Second, existing estimation methodologies are systematically categorized into physics-based thermal models, observer- and identification-based approaches, and data-driven machine learning frameworks. Their mathematical principles, information bottlenecks, computational trade-offs, and deployment constraints are comparatively analyzed. Particular attention is given to hybrid and physics-informed methods, where reduced-order thermal networks, parameter adaptation, and learning-based residual correction are integrated to enhance robustness. Future developments should focus on uncertainty-aware estimation, lifecycle-adaptive modeling, and reliable temperature field inference under sparse sensing conditions. Full article

AC battery systems (ACBSs) based on multilevel converters (MLCs) have gained considerable attention in recent times for the provision of grid services due to high-power (HP) and high-energy (HE) capabilities. In a hybrid ACBS, multiple low-voltage ports provide DC interfaces for battery modules from the same or different chemistries, enabling flexible operation across a wide range of grid services. However, the design complexity increases substantially, due to (i) higher electrothermal coupling between heterogeneous battery modules and power electronic (PE) switches, (ii) grid compliance constraints and (iii) power quality requirements, which often leads to conservative oversizing and, consequently, increased total cost of ownership (TCO). To address these challenges, this paper proposes a co-design optimization framework for the sizing and selection of battery modules, PE components, and MLC architecture. A multi-fidelity modeling approach is presented to co-simulate the battery modules and MLC. The model captures electrochemical behavior, degradation dynamics, and power losses to enable accurate estimation of system-level energy efficiency. The framework then leverages a multi-objective nondominated sorting genetic algorithm (NSGA-II) to perform optimal cell-to-module sizing across different chemistries and MLC levels, while incorporating the inter-module balancing and AC power quality constraints. Comparative simulation studies show that the proposed co-design framework achieves life-cycle TCO reduction of 3.5%, 4.5% and 20% relative to non-hybrid (single chemistry) configurations based on LFP, NMC and LTO chemistries, respectively. The test results validate the effectiveness of the proposed co-design methodology for the optimal design of grid-tied AC battery systems. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Additive manufacturing technologies are no longer limited to rapid prototyping but are increasingly used for low-volume production of functional end-use components. Among advanced AM techniques, HP Multi-Jet Fusion (MJF) stands out for its high precision and efficiency. Polyamides, thanks to their balanced mechanical and thermal properties, are commonly used as building materials in this technology. However, these materials are notoriously difficult to bond with conventional adhesives. This study investigates the shear strength of bonded joints made from two frequently used MJF materials—PA12 and glass-bead-filled PA12—using four different industrial adhesives. Experimental procedures were conducted according to ASTM standards. Specimens for single-lap-shear tests were fabricated on an HP MJF 4200 series printer, bonded using a custom jig, and tested on a Zwick-Roell Z250 electro-mechanical testing machine. Surface roughness of the adherends was measured with a 3D optical microscope to assess its influence on bonding performance. The polyurethane-based adhesive (3M Scotch-Weld DP620NS) demonstrated superior performance with maximum shear strengths of 5.0 ± 0.35 MPa for PA12 and 4.4 ± 0.03 MPa for PA12GB, representing 30% and 17% higher strength, respectively, compared to epoxy-based alternatives. The hybrid cyanoacrylate–epoxy adhesive (Loctite HY4090) was the only system showing improved performance with glass-bead-reinforced substrate (16.5% increase from PA12 to PA12GB). Statistical analysis confirmed significant differences between adhesive types (F_3,24 = 31.37, p < 0.001), with adhesive selection accounting for 65.7% of total performance variance. In addition to the experimental work, a finite element-based numerical simulation was performed to analyze the distribution of shear and peel stresses across the adhesive layer using Siemens Simcenter 3D 2406 software with the NX Nastran solver. The numerical results were compared with analytical predictions from the Volkersen and Goland–Reissner models. Full article

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs. Full article

We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL^® 5.0 simulation of a LiFePO₄ particle produced voltage and temperature data across ambient temperatures (253–298 K) and discharge rates (1 C–20.5 C). Principal component analysis (PCA) reduced this dataset to five latent variables, which we then mapped to experimental voltage–temperature profiles of an A123 Systems 26650 2.3 Ah cell using a self-normalizing neural network (SNN). The resulting ROM achieves real-time prediction accuracy comparable to detailed models while retaining essential electrothermal dynamics. Full article