Search Results (127)

This study investigates a novel heat pipe integrated cooling system designed for thermal management of Tesla’s 4680 cylindrical lithium–ion batteries in electric vehicles (EVs). Through a comprehensive approach combining experimental analysis, 1-D AMESim simulations, and 3-D Computational Fluid Dynamics (CFD) modeling, the thermal performance of various wick structures and working fluid filling ratios was evaluated. The experimental setup utilized a triangular prism chamber housing three surrogate heater blocks to replicate the heat generation of 4680 cells under 1C, 2C, and 3C discharge rates. Results demonstrated that a blended fabric wick with a crown-shaped design (Wick 5) at a 30–40% filling ratio achieved the lowest maximum temperature (T_max of 47.0°C), minimal surface temperature deviation (ΔT_surface of 2.8°C), and optimal thermal resistance (R_th of 0.27°C/W) under 85 W heat input. CFD simulations validated experimental findings, confirming stable evaporation–condensation circulation at a 40% filling ratio, while identifying thermal limits at high heat loads (155 W). The proposed hybrid battery thermal management system (BTMS) offers significant potential for enhancing the performance and safety of high-energy density EV batteries. This research provides a foundation for optimizing thermal management in next-generation electric vehicles. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

The transition to electric vehicles (EVs) is accelerating due to global efforts to reduce greenhouse gas emissions and reliance on fossil fuels. Lithium-ion batteries (LIBs) are the predominant energy storage solution in EVs, offering high energy density, efficiency, and long lifespan. However, their adoption is overly involved with critical safety concerns, including thermal runaway and overheating. This review systematically focuses on the critical role of battery thermal management systems (BTMSs), such as active, passive, and hybrid cooling systems, in maintaining LIBs within their optimal operating temperature range, ensuring temperature homogeneity, safety, and efficiency. Additionally, the study explores the impact of integrating artificial intelligence (AI) and machine learning (ML) into BTMS on thermal performance prediction and energy-efficient cooling, focusing on optimizing the operating parameters of cooling systems. This review provides insights into enhancing LIB safety and performance for widespread EV adoption by addressing these challenges. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

This paper presents the results of the mathematical modeling and experimental studies of charging a traction lithium-ion battery of a passenger electric car using an integrated charger based on a traction voltage inverter. An original three-stage charging algorithm (3PT/PN) has been developed and implemented, which provides a sequential decrease in the charging current when the specified voltage and temperature levels of the battery module are reached. As part of this study, a comprehensive mathematical model has been created that takes into account the features of the power circuit, control algorithms, thermal effects and characteristics of the storage battery. The model has been successfully verified based on the experimental data obtained when charging the battery module in real conditions. The maximum error of voltage modeling has been 0.71%; that of current has not exceeded 1%. The experiments show the achievement of a realized capacity of 8.9 Ah and an integral efficiency of 85.5%, while the temperature regime remains within safe limits. The proposed approach provides a high charge rate, stability of the thermal state of the battery and a long service life. The results can be used to optimize the charging infrastructure of electric vehicles and to develop intelligent battery module management systems. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Building information modeling (BIM) and thermal imaging from drone flyovers present innovative opportunities for enhancing building energy efficiency. This study examines the integration of BIM models with thermal data collected using unmanned aerial vehicles (UAVs) to assess and manage energy performance throughout a building’s lifecycle. By leveraging BIM’s structured data and the concept of the digital twin, thermal analysis can be automated to detect thermal bridges and inefficiencies, facilitating data-driven decision-making in sustainable construction. The paper examines methodologies for combining thermal imaging with BIM, including image analysis algorithms and artificial intelligence applications. Case studies demonstrate the practical implementation of UAV-based thermal data collection and BIM integration in an educational facility. The findings highlight the potential for optimizing energy efficiency, improving facility management, and advancing low-emission building practices. The study also addresses key challenges such as data standardization and interoperability, and outlines future research directions in the context of smart city applications and energy-efficient urban development. Full article

Wireless electric vehicle charging (WEVC) is rapidly advancing as an enabling technology for convenient electrified transportation. The trend toward high-power WEVC systems is accelerating, which not only enhances charging speed and user convenience but also introduces new and complex safety challenges. These challenges are particularly acute at the coupler level, where electrical, thermal, and magnetic risks often interact. This review offers a comprehensive analysis of safety management technologies that are specific to WEVC, with an exclusive focus on coupler-related risks. System-level and coupler-level hazards associated with high-power operation are first examined, followed by an in-depth discussion of recent progress in passive safety materials, such as insulation, thermal dissipation, and electromagnetic shielding. Active safety management strategies are also reviewed in detail, including foreign object detection, live body detection, misalignment detection, and multifunctional detection schemes that integrate these capabilities. Emphasis is placed on the ongoing rapid iteration of safety technologies as power levels increase and on the necessity for solutions that are comprehensive, precise, orderly, and reliable. This review concludes by highlighting future research directions, such as data-driven safety management, intelligent sensor integration, regulatory evolution, and user-centered system design, aiming to support the safe and scalable deployment of WEVC in next-generation mobility. Full article

Efficient thermal management is critical for the performance, safety, and longevity of lithium-ion batteries, particularly in new energy vehicles. This paper presents the development and application of a NiCr/NiSi thin-film thermocouple fabricated via magnetron sputtering on a polyimide substrate, aiming to provide high-precision, fast-response internal temperature measurements for lithium-ion battery systems. The thermocouple demonstrates a Seebeck coefficient of approximately 40.95 μV/°C and a repeatability error of only 0.45%, making it highly suitable for capturing transient thermal events. The main innovation of this work lies in the comprehensive integration of simulation and experimental validation to optimize the thermocouple’s performance for lithium-ion battery applications. This includes static calibration, external short-circuit, and puncture tests, which collectively confirm the thermocouple’s reliability and accuracy. Additionally, the study explores the impact of ambient temperature variations on internal battery temperatures, revealing a nearly linear increase in internal temperature with rising ambient conditions. The findings offer valuable insights for improving battery thermal management systems, establishing early warning thresholds for thermal runaway, and enhancing the overall safety of lithium-ion battery applications. Full article

In this study, in order to overcome the limitations of existing electric vehicle (EV) thermal management systems (TMS), a highly integrated and coordinated operation strategy for EV thermal management was proposed. Specifically, an integrated architecture with a 10-way valve was established to replace traditional 3-way and 4-way valves to enhance the coupling between coolant circuits. Six operating modes were realized via the switching function of the 10-way valve, including the mode of waste heat recovery. A highly integrated TMS model was developed on the AMEsim2304 platform, followed by parameter matching. The accuracy of the model was validated through comparative analysis with laboratory and environmental chamber test results. Based on the designed highly integrated TMS, a classical fuzzy Proportional-Integral-Derivative Control (PID) control strategy was introduced to regulate the coolant circulation pump. Simulation analyses and experimental results demonstrated that the optimized system could reduce the battery pack heating time by approximately 300 s compared to the pre-optimized configuration. Moreover, the waste heat recovery could improve the cabin heating rate from 1.9 °C/min to 3.4 °C/min, representing a 43.7% enhancement. Furthermore, the output power of the high-pressure liquid heater remained low, resulting in a 10% reduction in overall heating energy consumption. Based on simulation and experimental analyses, this research can promote the progress of thermal management system technology for electric vehicles to a certain extent. Full article

The rising demand for lithium-ion batteries (LIBs), driven by the growing consumption of electronic devices and the expansion of electric vehicles, is leading to a concerning depletion of primary metal resources and a significant accumulation of electronic waste. This urgent challenge highlights the need for sustainable recovery methods to extract valuable metals from spent LIBs, aligning with circular economy principles. In this study, the preparation of spent batteries for the bioleaching process was achieved with minimal manipulation. This included a preliminary discharge to ensure safety in subsequent processes and a brief crushing to facilitate the access of leaching agents to valuable metals. Unlike most studies that grind batteries to obtain powders between 70 and 200 microns, our approach works with particles sized around 5 mm. Additionally, our preparation process avoids any thermal or chemical treatments. This straightforward pre-treatment process marks a significant advancement by reducing the complexity and cost of processing. A systematic study was conducted on various fractions of the large particle sizes, using Fe (III) produced through bio-oxidation by A. ferrooxidans and biogenically obtained H2SO4 from A. thiooxidans. The highest metal extraction rates were achieved using the unsorted fraction, directly obtained from the black mass after the grinding process, without additional particle separation. When treated with bio-oxidized Fe (III), this fraction achieved a 95% recovery of Cu, Ni, and Al within 20 min, and over 90% recovery of Co, Mn, and Li within approximately 30 min. These recovery rates are attributed to the combined reducing power of Al and Cu already present in the black mass and the Fe (II) generated during the oxidation reactions of metallic Cu and Al. These elements actively facilitate the reduction of transition metal oxides into their more soluble, lower-valence states, enhancing the overall metal solubilization process. The extraction was carried out at room temperature in an acidic medium with a pH no lower than 1.5. These results demonstrate significant potential for efficient metal recovery from spent batteries with minimal pre-treatment, minimizing environmental impact. Additionally, the simplified residue preparation process can be easily integrated into existing waste management facilities without the need for additional equipment. Full article

The thermal management system (TMS) of electric vehicles (EVs) plays a pivotal role in vehicle performance, driving range, battery lifespan, and passenger comfort. Precise control of compressor speed, informed by real-time sensor data, is essential for improving TMS efficiency and extending EV range. This study proposes a control strategy based on the PID Search Algorithm (PSA), ensuring optimal thermal management for an integrated battery and cabin TMS. A co-simulation platform combining AMESim and Simulink is developed for validation, utilizing various sensors to monitor system performance. Simulations are conducted under target temperatures of 20 °C and 25 °C to replicate various operating conditions. The optimized strategy is compared with the most commonly used PID controllers, fuzzy controllers, and PID fuzzy control strategies. The results demonstrate that the PSA-Optimized control strategy significantly outperforms the other three strategies. For a target of 25 °C, the PSA-Optimized control strategy shows a minimal temperature overshoot of 0.012 °C, with COP improvements of 0.06, 0.04, and 0.03 compared to the other three control strategies, respectively. For a target of 20 °C, the overshoot is further reduced to 0.010 °C, while the coefficient of performance (COP) increases by 0.14, 0.01, and 0.07 relative to the same benchmarks. Overall, the results indicate that the PSA-Optimized control strategy effectively utilizes sensor data to reduce cabin temperature overshoot, stabilize compressor speed fluctuations, slow the decay of the battery’s state of charge (SOC), and enhance the system’s COP. Full article

With the rapid development of electric vehicles, the safety and reliability of lithium-ion batteries (LIBs), as their core energy storage units, have become increasingly prominent. The variation in internal battery pressure is closely related to critical issues such as thermal runaway, mechanical deformation, and lifespan degradation. The non-uniform distribution of internal pressure may trigger localized hot spots or even thermal runaway, posing significant threats to vehicle safety. However, traditional external monitoring methods struggle to accurately reflect internal pressure data, and single-point external pressure measurements fail to capture the true internal state of the battery, particularly within battery modules. This limitation hinders efficient battery management. Addressing the application needs of electric vehicle power batteries, this study integrates thin-film pressure sensors into LIBs through the integrated functional electrode (IFE), enabling distributed in situ monitoring of internal pressure during long-term cycling. Compared to non-implanted benchmark batteries, this design does not compromise electrochemical performance. By analyzing the pressure distribution and evolution data during long-term cycling, the study reveals the dynamic patterns of internal pressure changes in LIBs, offering new solutions for safety warnings and performance optimization of electric vehicle power batteries. This research provides an innovative approach for the internal state monitoring of power batteries, significantly enhancing the safety and reliability of electric vehicle battery systems. Full article

This study addresses the critical challenges of conductor structure fusing, thermal management failure, and thermal runaway risks in lithium-ion batteries under extreme high-amperage discharge conditions. By integrating theoretical analysis, multiphysics coupling simulations, and experimental validation, the research systematically investigates the overcurrent capability of lithium battery conductor structures. A novel current–thermal structure coupled finite element model was developed to analyze the dynamic relationship between key parameters, specifically overcurrent cross-sectional area and contact area, and their influence on temperature gradient distribution. Experimental results confirm the model’s accuracy, revealing that under extreme high-amperage conditions, increasing the conductor cross-sectional area by 50% only marginally extends the battery’s current-carrying duration from 0.75 s to 0.8 s. This limited enhancement is attributed to rapid heat generation, which restricts the effectiveness of increasing the cross-sectional area alone. Instead, optimizing the conductor structure by modifying the heat conduction path, which involves a similar increase in the cross-sectional area and an additional 60% increase in contact area through the addition of a welding reinforcement structure, achieves thermal equilibrium. The optimized design achieves a current-carrying duration of 1.73 s, which is 230% of the duration of the traditional configuration. This work establishes a scalable framework for enhancing the thermal–electrical performance of lithium-ion batteries, providing a theoretical foundation for structural optimization and offering significant methodological support for advancing research in high-power battery design, with potential applications in electric vehicles, renewable energy systems, and industrial robotics. Full article

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R² > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R² values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management. Full article