High Power Demand Research Articles

A Multifunctional Surface Modifier Capable of Stabilizing 5.0 V Graphite Cathode via Reinforced Mechanical Strength and Preferential Anion Adsorption

AbstractWith the ever‐increasing demand for high power and high energy batteries, extensive researches devote to high voltage cathode materials. Graphite cathode‐based dual‐ion batteries (DIBs) possess the unique advantages of high working voltage (≈4.5−5.0) and high power, but still suffer from low coulombic efficiency and poor long‐term stability mainly resulted by the serious oxidative decomposition of electrolytes and significant structural deterioration of graphite cathode. From the perspectives of simultaneously reinforcing the mechanical strength of graphite cathode and suppressing the decomposition of electrolytes via a cathode/electrolyte interphase (CEI), polyacrylic acid (PAA) is adopted as the surface modifier of natural graphite (NG). The mechanical stability of graphite cathode is significantly improved by virtue of the bonding interaction between PAA and binder, which is validated through both theoretical calculation and experimental observation. In addition, PAA contributes to the formation of a LiF‐rich and homogeneous CEI through the preferential adsorption of anions, and effectively mitigates the cointercalation and decomposition of solvent. As the cathode material of DIBs, NG@PAA manifests fast charge/discharge capability and outstanding capacity retention of 73.9% after 8000 cycles. This work provides a surface modification strategy for optimizing the performance of electrode materials from multiple perspectives.

(Invited) Assessment of Extreme High-Power Demand for Evtol Li-Ion Batteries

Designing battery systems for electric vertical take-off and landing (eVTOL) platforms is challenging because they require high-power demands during crucial flight maneuvers. The eVTOL application needs exceptionally high discharge rates from the on-board lithium-ion batteries (LiBs) during several phases of its mission. However, LiBs have not been explored enough under high-power discharge scenarios, such as that of an eVTOL, because the current application spectrum in electric vehicles (EVs) and electronic devices does not require such high power.In a recent study, we simulated the initial take-off step of electric vertical takeoff and landing (eVTOL) vehicles powered by a lithium-ion battery subjected to an intense 15C discharge pulse at the beginning of the discharge cycle followed by a subsequent low-rate discharge. we conducted extensive electrochemical testing to assess the long-term stability of a lithium-ion battery under these high-strain conditions. The main finding is that despite the performance recovery observed at low rates, the re-application of high rates leads to drastic cell failure.These results highlight the eVTOL battery longevity challenge and emphasize the need for tailored battery chemistry designs for eVTOL applications to address both anode plating and cathode instability.

Operando XRD Observation of NMC Cathode for Reaction Distribution Analysis Under Fast Discharging

Introduction There is a demand for high output power of lithium-ion batteries (LIBs) as regenerative power sources for construction machinery. It is important to analyze electrode behavior during high C-rate discharge processes. The main reason why LIBs do not have sufficient capacity at high power output is thought to be that the discharge finishes when the topmost surface of the electrode reaches the cut-off potential, even if the other parts are only partially discharged.In this study, an attempt was made to infer the reaction distribution and fast response of the entire electrode by observing the relaxation process after discharge using laboratory XRD. Operando XRD measurements were performed for a positive electrode of active material LiNi1/3Mn1/3Co1/3O2 (NMC) while discharging at 0.1C, and a correlation was observed between SOC and the peak angle derived from 101 reflection. By contrasting this with known NMC crystal lattice changes during charging and discharging, compositional changes can be captured [1-3]. Based on this, the behavior of NMC electrodes of various electrode designs during the relaxation process after fast discharge was observed, and the results are reported. Experimental Laminated half-cells (30 mm square) were prepared using NMC electrodes with different electrode designs (thickness and porosity) for the positive electrode, Li metal for the negative electrode, and carbonate electrolyte containing 1.0 mol dm− 3 LiPF6 as the electrolyte solution. The electrodes were made with three electrode thickness levels of approximately 20 µm, 30 µm, and 40 µm (constant porosity), and three porosity levels of approximately 30%, 40%, and 50% (constant loading). After charging these cells to 100% SOC at 0.1C, they were installed in an X-ray diffraction system (SmartLab, Rigaku) and subjected to fast discharge with the charge/discharge device. Discharge conditions were as follows: DOD 50% capacity was discharged at 5C current, followed by 1.5 hours of Relaxation. At the same time, operando XRD measurements were performed to obtain the displacement of the peak top angle originating from the (101) plane of NMC. Result and Discussion The obtained (101) plane peak-top angles and cell voltage displacements are shown in Figure 1. In this case, X-rays were incident from the negative electrode side. It was observed that the peak angle became smaller as the cell discharged and gradually increased during the relaxation. This suggests that the reaction was preceded on the separator-side portion of cathode, leading to the end of discharge, and that the Li imbalance in the electrode was relaxed in the subsequent open circuit state.The same measurements were performed at different levels of NMC electrode thickness and porosity, and the peak angle displacement and relaxation time during the relaxation process were compared. The results showed that there was no clear dependence of peak angle displacement and relaxation time on thickness. On the other hand, the larger the porosity, the smaller the peak angle displacement and the larger the relaxation time. These results indicate that there is a clear correspondence between the electrode design and the reaction inhomogeneity in the electrode, and that this method can be used to evaluate the reaction distribution.In addition, the relationship between the angular displacement of the above XRD peak and the change in cell voltage behavior during the relaxation process was also discussed by fitting with an equivalent circuit. The correlation between the two results suggests the possibility of observing the reaction distribution inside the battery from the analysis of the cell voltage behavior.

Ion Transport Enhancement Mechanism in Polymer/Ceramic Composite Electrolyte with High-Entropy Li-Garnet

Given the growing demand for electrification, delivering safe, high power, and high energy in lithium-ion batteries is a key challenge. One possible approach is incorporating Li-ion conductive ceramics into a polymer electrolyte matrix. Benefiting from both components, composite electrolytes may deliver high safety, improved ionic conductivity, enhanced electrochemical stability, and feasible construction for solid-state batteries. Specifically, it is crucial to understand and verify the ion transport enhancement mechanism in such a composite electrolyte, as the conductivity in the electrolytes directly affects the overall cell performance and the electrolyte design principles for these systems may change.In this work, we investigate the ion transport enhancement mechanism in composites made with a single-ion conducting (SIC) polymer electrolyte with high-entropy Li-garnet (HE-LLZO) micron-sized powders. We use broadband dielectric spectroscopy (BDS), thermal gravimetric analysis (TGA), rheology measurement and proton nuclear magnetic resonance (1H NMR) to understand the ion transport kinetics in the composites. At 30 °C, 30 wt% HE-LLZO composites with SIC polymer showed almost 8-fold enhancement in conductivity. The addition of HE-LLZO leads to the different degree of polymerization compared to that of the pure polymer, implying that the substantial amount of unpolymerized monomer facilitated the conductivity in the composites. To further investigate the reaction mechanism in the composites, Fourier transform infrared (FT-IR) spectroscopy results will be presented along with 1H NMR data. Electrochemical properties and cell performance will be compared to further illustrate the effects of HE-LLZO on the composite. Acknowledgements This work was supported as part of the Fast and Cooperative Ion Transport in Polymer-Based Materials (FaCT), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences at Oak Ridge National Laboratory under contract DE-AC05-00OR22725.

(Keynote) Elevating Supercapacitors with Ultrafast Materials: Vital Contributors to Sustainable Energy Solutions

Supercapacitors have emerged as pivotal energy conversion-storage systems in contemporary renewable and sustainable nanotechnology, offering a cost-effective and eco-friendly alternative. With material properties being paramount to their performance, supercapacitors stand out from conventional batteries due to their exceptional high-power capabilities and longevity, making them versatile for various applications. Their remarkable features, such as high-power capabilities and extended cycle-life, make them highly attractive for advanced hybrid configurations, serving both mobile and stationary purposes across diverse applications. The drive to shape the next generation of energy technologies fuels the development of composite materials, integrating elements like nanocarbon/graphene and metal oxides to enhance electrochemical performance.Material selection plays a critical role in supercapacitors, as different materials used as electrodes and electrolytes significantly influence functionality and characteristics, particularly in determining thermal and electrical properties. Leveraging innovative techniques such as "Ultracentrifugation" and “Spray-Dry Synthesis,” nanoarchitecture electrode materials are engineered for improved performance and safety.Collaborations with Li-predoping technology experts aim to develop groundbreaking Li-ion-based energy facilitators like the "iE7" SuperRedox Capacitor, capable of seamlessly balancing high power and energy demands. By integrating advancements in material science, pre-doping techniques, and cell design, the vision extends beyond innovation to lead the transition towards solar regeneration, driving electrification, and decarbonization across extensive mobility and heavy-duty sectors.

Optimal battery management in PV + WT micro-grid using MSMA on fuzzy-PID controller: a real-time study

In modern energy systems, managing energy within a microgrid (MG) poses significant challenges due to the unpredictable nature of renewable energy sources. This article introduces a novel approach for optimal battery management in a photovoltaic–wind microgrid using a Modified Slime Mould Algorithm (MSMA) combined with a fuzzy-PID controller. The microgrid comprises a wind turbine (WT) generator, solar photovoltaic (PV) generator, and a battery energy storage system (BESS). The BESS plays a crucial role in meeting high power demand during outages, while the fuzzy-PID controller ensures accurate prediction of the battery’s state of charge (SOC). The proposed method’s performance is evaluated by comparing the MSMA-based fuzzy-PID controller with a PSO-based fuzzy-PID controller to establish its effectiveness. The optimal energy management of the BESS in the microgrid is achieved by fine-tuning the fuzzy-PID controller using the MSMA algorithm. Simulation results demonstrate that the battery management system (BMS) effectively optimizes charging and discharging based on renewable energy availability and load demand. The fuzzy-PID controller adjusts battery operation by minimizing the error between the desired and actual battery voltage. Performance validation has been conducted in RTS-lab using five distinct load scenarios—45 kW, 35 kW, 75 kW, 4.5 kW, and 12.5 kW, which confirming the effectiveness of the proposed control strategy for energy management.

A Comprehensive Review on Cryptographic Techniques for Securing Internet of Medical Things: A State-of-the-Art, Applications, Security Attacks, Mitigation Measures, and Future Research Direction

As healthcare becomes increasingly dependent on the Internet of Medical Things (IoMT) infrastructure, it is essential to establish a secure system that guarantees the confidentiality and privacy of patient data. This system must also facilitate the secure sharing of healthcare information with other parties within the healthcare ecosystem. However, this increased connectivity also introduces cybersecurity attacks and vulnerabilities. This comprehensive review explores the state-of-the-art in the IoMT, security requirements in the IoMT, cryptographic techniques in the IoMT, application of cryptographic techniques in securing the IoMT, security attacks on cryptographic techniques, mitigation strategies, and future research directions. The study adopts a comprehensive review approach, synthesizing findings from peer-reviewed journals, conference proceedings, book chapters, Books, and websites published between 2020 and 2024 to assess their relevance to cryptographic applications in IoMT systems. Despite advancements, cryptographic algorithms in IoMT remain susceptible to security attacks, such as man-in-the-middle attacks, replay attacks, ransomware attacks, cryptanalysis attacks, key management attacks, chosen plaintext/chosen ciphertext attacks, and side-channel attacks. While techniques like homomorphic encryption enhance security, their high computational and power demands pose challenges for resource-constrained IoMT devices. The rise of quantum computing threatens the efficacy of current cryptographic protocols, highlighting the need for research into quantum-resistant cryptography. The review identifies critical gaps in existing cryptographic research and emphasizes future directions, including lightweight cryptography, quantum-resistant methods, and artificial intelligence-driven security mechanisms. These innovations are vital for meeting the growing security requirements of IoMT systems and protecting against increasingly sophisticated threats.

Using Time-Series Databases for Energy Data Infrastructures

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.

An experimental and comparative study on passive and active PCM cooling of a battery with/out copper mesh and investigation of PCM mixtures

The carbon emission contribution to global warming accelerated both research on and transition to electric vehicles (EVs). Drivers demand high power, fast acceleration and less charging times. All these demands require high C rate charging/discharging demands from batteries. The rate of heat generation is exponentially proportional to C rates which decreases battery lifetime and may lead to thermal runaway. However, a battery thermal management system decreases thermal runaway risk and decelerates battery degradation via controlling battery temperature. In this paper, we first document the thermal conductivity enhancement via copper foam into phase change material (PCM) domain to uncover their possible use in EV thermal management applications. Maximum 15.93 times increment is achieved with a specific copper foam. Then, physical properties and behaviors of distinct PCM mixtures are documented. Homogeneity of mixtures is associated with the chemistry of PCMs and the mixture melting point is proportional to the volume weighted average of melting temperatures. The results document that the PCM with relatively lower melting point is beneficial when end of discharge temperatures considered, except for high discharge rate of 2C. Temperature uniformity across the battery increases with relatively higher melting point PCM. Experiments also document that the amount of PCM volume lost via insertion of copper foam yields higher end of discharge temperatures. Overall, both PCM and copper foam enhances temperature homogeneity and their benefit becomes more sensible during drive cycles relative to continuous charge/discharge use cases.

Development of an integrated energy and thermal planner for a series hybrid off-road autonomous tracked vehicle

Electrifying off-road vehicle powertrains enhances energy efficiency and auxiliary power generation but poses control challenges due to extreme temperatures, complex terrain, powerful cooling systems, and high-power demands. This paper presents the Integrated Energy and Thermal Planner (IETP), a unified approach to energy and thermal management for off-road series hybrid tracked vehicles. The IETP addresses challenges posed by extreme ambient temperatures, high-power demands, and complex non-linear thermal dynamics by integrating the control of thermal systems with energy planning. The synergistic operation of the ICE-Generator and thermal actuators reduces battery degradation by up to 29% compared to traditional separated energy and thermal management. Additionally, IETP improves fuel efficiency by at least 10% in power-demanding high-speed driving scenarios. Key contributions include the ’priority-speed’ formulation, which optimizes the ICE-Gen’s operating point in a computationally efficient manner, and a systematic sensitivity analysis to balance planning accuracy with hardware constraints. Real-time planner-in-the-loop application mitigates execution delays through a memory buffer and compensation strategy. Despite uncertainties in modeling and preview assumptions, the IETP demonstrates robustness, with future work aimed at further improving transient compensation and estimation routines. This integrated strategy enhances both the efficiency and durability of hybrid electric vehicles in extreme off-road environments.

Modelling the impact of reducing lubricant viscosity on a conventional passenger car fuel economy and wear protection

This study explores how reducing lubricant viscosity affects fuel economy in passenger cars through experimental characterization and semi-predictive modelling. Thus, three engine oils (0W-20, 5W-30, and 5W-40) were tested for their rheological and tribological properties. Data were introduced into a detailed GT-Suite model of a turbocharged gasoline SUV. Simulations over six different driving cycles, including European homologation and real-driving conditions, used a fast-running model (FRM) to assess engine friction and wear protection. Results showed that lower viscosity oils, especially 0W-20, significantly reduced frictional losses, enhancing fuel economy. However, lower viscosity also increased contact friction under high power demand, raising durability concerns. A comprehensive evaluation of lubricants performance, offering insights into the trade-offs between fuel economy improvements and potential durability risks.

High-Performance Dual-Redox-Mediator Supercapacitors Based on Buckypaper Electrodes and Hydrogel Polymer Electrolytes.

In recent years, the demand for solid, thin, and flexible energy storage devices has surged in modern consumer electronics, which require autonomy and long duration. In this context, hybrid supercapacitors have become strategic, and significant efforts are being made to develop cells with higher energy densities while preserving the power density of conventional supercapacitors. Motivated by these requirements, we report the development of a new high-performance dual-redox-mediator supercapacitor. In this study, cells were constructed using fully moldable buckypapers (BPs), composed of carbon nanotubes and cellulose nanofibers, as electrodes. We evaluated the compatibility of BPs with hydrogel polymer electrolytes, based on 1 mol L-1 H2SO4 and polyvinyl alcohol (PVA), supplemented with different redox species: methylene blue, indigo carmine, and hydroquinone. Solid cells were constructed containing two active redox species to maximize the specific capacity of each electrode. Considering the main results, the dual-redox-mediator supercapacitor exhibits high energy density of 32.0 Wh kg-1 (at 0.8 kW kg-1) and is capable of delivering 25.9 Wh kg-1 at high power demand (4.0 kW kg-1). Stability studies conducted over 10,000 galvanostatic cycles revealed that the PVA polymer matrix benefits the system by inhibiting the crossover of redox species within the cell.

Full-size experimental investigations on planetary gear journal bearings in high-power wind turbines

Abstract To satisfy the large-scale and high-power demands of wind turbines, planetary gear journal bearings (PGJBs) have been applied in large wind turbine gearboxes (WTGs), as an alternative to traditional rolling bearings, due to their higher reliability and smaller size. To simulate the actual lubrication behaviors of PGJBs and investigate their hydrodynamic performance, a full-size test rig for PGJBs was built in this paper. A multi-parameter detection system coupled with the ultrasonic testing method was developed. Four ultrasonic piezoelectric elements, eight thermistors, two pressure transducers, and one torque sensor were used to obtain the film thickness, oil temperature, oil pressure, and friction torque data of the test PGJB. The rated condition experiment was conducted to investigate the variation of measured lubrication performance with the operating time. Three-dimensional predictions of oil film pressure, temperature, and thickness were presented to analyze the steady-state lubrication characteristic at the rated condition. Moreover, a series of steady-state experiments were also carried out to simulate the normal operations of the test PGJB at different conditions. And the measured results were verified by the numerical predictions. The influence of rotational speed, input load, oil supply temperature, and oil supply flow on the hydrodynamic performance of the test PGJB were explored.

The implementation of a cost‐efficient damped AC testing methodology for transmission cables based on DC–AC conversion and distributed partial discharge detection

AbstractThis research introduces a novel damped alternating current (DAC) testing methodology, which integrates an enhanced DAC generator with a pulse‐based distributed partial discharge (PD) detection technique. The advanced DAC generator is designed without the need for high voltage (HV) solid‐state switches, thereby offering a cost‐effective solution for field‐testing voltage generation through a direct current–alternating current conversion approach. To meet the demand for high instantaneous power, a capacitor bank is employed as the power supply. Furthermore, the implementation of a distributed PD detection technique enhances sensitivity and eliminates limitations associated with cable length. To minimize the construction costs of the distributed PD‐detection system, a pulse synchronization technique has been employed. Efforts to reduce the system's weight were informed by simulations, resulting in the design and development of a prototype weighing 850 kg for a 64/110 kV power cable. The reduction in the number of HV solid‐state switches contributes to significant cost savings, amounting to tens of thousands of dollars, when compared to conventional DAC generators. Laboratory and field tests validated the effectiveness of the cost‐efficient DAC testing methodology.

Probabilistic Assessment of the Impact of Electric Vehicle Fast Charging Stations Integration into MV Distribution Networks Considering Annual and Seasonal Time-Series Data

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Performance Analysis of Multiple Energy-Storage Devices Used in Electric Vehicles

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases.

Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems

To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks.

ASIC Design and Implementation of 32 Bit Arithmetic and Logic Unit

Low power techniques are becoming more important as portable digital applications expand quickly and demand high speed and low power consumption. The ALU is the most crucial and essential component of a central processing unit, as well as numerous embedded systems and microprocessors. Designing a 32-bit ALU that combines an arithmetic unit and a logical unit is the task at hand. The logic unit will do logic operations AND, OR, XOR, and XNOR with the aid of the recommended CMOS technology, while the arithmetic unit will do the arithmetic operations addition, subtraction, increment, and buffering operation. The arithmetic unit is constructed using the 4x1 MUX, 2x1 MUX, and full adder, and the 4x1 MUX, required logic gates, and 4x1 MUX are employed to create the logical unit. Using Cadence Virtuoso Gpdk 180nm Technology, the results of the simulation of the 32-bit ALU, the ideal delay, and the Power were calculated.

Queuing‐based energy‐efficient processing algorithm for smart transportation through V2V communication

SummaryApplications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

MNSS: Neural Supersampling Framework for Real-Time Rendering on Mobile Devices.

Although neural supersampling has achieved great success in various applications for improving image quality, it is still difficult to apply it to a wide range of real-time rendering applications due to the high computational power demand. Most existing methods are computationally expensive and require high-performance hardware, preventing their use on platforms with limited hardware, such as smartphones. To this end, we propose a new supersampling framework for real-time rendering applications to reconstruct a high-quality image out of a low-resolution one, which is sufficiently lightweight to run on smartphones within a real-time budget. Our model takes as input the renderer-generated low resolution content and produces high resolution and anti-aliased results. To maximize sampling efficiency, we propose using an alternate sub-pixel sample pattern during the rasterization process. This allows us to create a relatively small reconstruction model while maintaining high image quality. By accumulating new samples into a high-resolution history buffer, an efficient history check and re-usage scheme is introduced to improve temporal stability. To our knowledge, this is the first research in pushing real-time neural supersampling on mobile devices. Due to the absence of training data, we present a new dataset containing 57 training and test sequences from three game scenes. Furthermore, based on the rendered motion vectors and a visual perception study, we introduce a new metric called inter-frame structural similarity (IF-SSIM) to quantitatively measure the temporal stability of rendered videos. Extensive evaluations demonstrate that our supersampling model outperforms existing or alternative solutions in both performance and temporal stability.