Small Battery Research Articles

An Approach for Estimating the Contributions of Various Real-World Usage Conditions towards the Attained Utility Factor of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets.

Implementation of optimal routing in heterogeneous wireless sensor network with multi‐channel Media Access Control protocol using Enhanced Henry Gas Solubility Optimizer

SummaryThe majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.

Plug-in charging or electric roads? Powering U.S. long-haul heavy-duty trucks in 2050

Abstract Pervasive plug-in fast chargers and/or electrified roadways (eRoads) might address the limited range, long recharging times, and reliance on greenhouse gas (GHG)-intensive, costly, and heavy batteries associated with electrifying long-haul heavy-duty trucks (HDTs). While these large-scale interventions shift environmental and cost burdens onto infrastructure, there is a lack of studies investigating how eRoads affect system-level GHG emissions, costs, material use, and peak electric grid power demands. We compare these aspects for the case of electrifying U.S long-haul HDTs (Class 8) in 2050 powered by combinations of plug-in fast chargers and eRoads. Our model accounts for battery downsizing, energy consumption, and truck operation patterns in quantifying life cycle impacts of batteries, plug-in chargers, eRoads, and hourly truck electricity demand. We find that plug-in fast chargers and eRoads reduce annualized 2050 HDT life cycle GHG emissions by 8% to 14% compared to using long-range batteries, which in turn have at least 50% lower emissions than diesel trucks. Conductive rails, overhead lines, and wireless eRoads (amortized across light- and heavy-duty vehicles) have lower system-wide costs than long-range batteries, plug-in fast chargers, or diesel trucks. Cost savings from smaller batteries, lower energy use and avoided recharging time offset high eRoads capital costs. While eRoads can reduce both system-level GHG and costs compared to diesel trucks, these reductions are sensitive to eRoads capital costs and losses from wireless power transfer and air resistance. eRoads require less lithium (87%) and copper (67%) than long-range batteries but increase regional peak power demands by up to 32%. Efficient wireless power transfer and aerodynamic pantographs enhance eRoads’ GHG and cost advantages, which may diminish if future batteries are more energy-dense, cheaper, or less GHG intensive. If successfully deployed, eRoads present opportunities for tighter integration between the transportation and electricity infrastructure systems, enabling optimized charging strategies to lower GHG emissions and costs.

Admittance-based equivalent circuit modeling of multi-patch piezoelectric energy harvesting plate

Abstract Harvesting energy from mechanical vibration using piezoelectric material is a common method to power small electronics and batteries. Implementation of multiple piezo-patches to plate-like structures increases the power capacity and frequency bandwidth of the energy harvester since multiple patches can capture more vibrational modes of the dense plate dynamics. To optimize the harvested electrical output using advanced harvesting circuits, equivalent circuit modeling (ECM) is a useful technique for representing the entire electromechanical system in circuit- simulator-software (LTspice). The ECM technique based on finite-element (FE) simulation has been used for plate-like structures with only one single-piezo-patch-harvester in the literature. However, when multiple patches are integrated into a plate-like structure, their coupling can cause charge cancelation, resulting in complex dynamics that cannot be handled by the existing ECM methods. This paper presents three new methodologies for extracting a multi-mode ECM of the harvesters (e.g.: multi-patch piezo-harvesters integrated into a plate), using an admittance-based system identification approach utilizing FE results. The first method incorporates the ECM of multi-patch harvester into a single ECM, so called OGC. It includes a circuit branch where multiple patches have only one ECM branch for each vibration mode, which requires less equivalent circuit elements and hence less computational cost. The second and third so-called respective ground uncoupled (RGU) and respective ground coupled (RGC) ECM methods generate circuit branches for each piezo-patch and for each vibration mode, whereby their electrical connection (e.g. parallel or series) is later configured in LTspice. The RGU method provides a general multi-patch modeling approach while RGC method provides ECM parameters for each patch simultaneously when they are all connected which is the case in network harvester analysis. The ECMs of parallel multi-patch harvesters are validated by system-level FE simulations. The proposed admittance-based ECM methods are reliable for deriving the system parameters of multi-patch harvesters.

Electrolyte Gradients and Capacity Drop in Large Flow Battery Tanks: From Experiments to Fluid-Dynamic Investigations

A study of the effects of electrolyte flow inside the tanks on electrical performance of an industrial-size Vanadium Redox Flow Battery (VRFB) is presented. The concentration of electrolytes feeding the stacks of VRFBs is crucial in determining the device voltage and overall power performance. Usually, the assumption of perfect mixing in the tanks is assumed, but the hydraulic behavior of large VRFB can deviate significantly from this assumption depending on the electrolyte flow rate, stochiometric factor and geometry of the tanks. In this study, the State of Charge (SoC) measurements have been analyzed, starting from the signal produced by an Open Circuit Voltage (OCV) cell. An experimental campaign has been conducted during charge/discharge roundtrip cycles at different current levels and constant stoichiometric factor. Building on the delay in the voltage response of the battery, a fluid-dynamic model was developed to study how the hydraulic behavior of electrolytes inside tanks influences the electrolyte concentration and, consequently, the SoC of the electrolyte that feeds the stack and the electrical time response of the battery. The model indicated that a stratification of charged electrolyte is responsible for this delay resulting in an initial voltage plateau. A different hydraulic behavior was found in each tank and in each charge/discharge phase. These results highlight the tank role in the industrial-scale VRFB performance. References Sanchez-Diez, E. Ventosa, M. Guarnieri, A. Trovò, C. Flox, R. Marcilla, F. Soavi, P. Mazur, E. Aranzabe, R. Ferret, “Redox flow batteries: status and perspective towards sustainable stationary energy storage”, J. Power Sources, 481, (2021) 228804.Kurilovich, A. Trovò, M. Pugach, K. J. Stevenson, M. Guarnieri. Prospect of Modeling Industrial Scale Flow Batteries – from Experimental Data to Accurate Overpotential Identification, Renew. Sust. Energ. Rev. 167, (2022) 112559.Trovò, V. Di Noto, J. E. Mengou, C. Gambaro, M. Guarnieri. Fast Response of kW-Class Vanadium Redox Flow Batteries, IEEE Trans. Sustain. Energy 12, (2021) 2413–2422.Trovò, M. Rugna, N. Poli, M. Guarnieri. Prospects for industrial vanadium flow batteries, Ceram. Int., 49 (14), (2023) 24487-24498.Maggiolo, A. Trovò, M. Guarnieri, “Reactant Flow in Flow Batteries”, in L. Cabeza (ed.), Encyclopedia of Energy Storage, vol. 4, pp. 231-242. Oxford: Elsevier. March 2022.A. Prieto-Díaz, S. E. Ibáñez, M. Vera. Fluid dynamics of mixing in the tanks of small vanadium redox flow batteries: Insights from order-of-magnitude estimates and transient two-dimensional simulations, Int. J. Heat Mass Transf. 216, (2023) 124567.Trovò, N. Poli, M. Guarnieri. New strategies for the evaluation of Vanadium Flow Batteries: testing prototypes, Cur. Opin. Chem. Eng. 37, (2022) 100853. Figure 1

Analysis on the Improvement of Energy Efficiency by using its Models in Wireless Sensor Networks

Aims and Background: A self-configured and infrastructure-less wireless network is named as a wireless sensor network (WSN), which has the role of monitoring physical or environmental conditions like sound, motion, temperature, vibration, and pollutants for passing their data throughout the network to a center of location where the data could be easily observed as well as analyzed. Methodology: In WSN, the small-sized sensor node is working with a very small battery with limited energy. Replacing the battery or recharging the battery is not feasible, and so, the energyefficient operation of WSN is the key factor. While designing routing protocols (RPs) for WSNs, one among the significant goals is energy conservation owing to this lower power. Totally, three models, namely, state, cluster, and content, were utilized for enhancing energy efficiency (EE). Each protocol has its own way of routing that varies from the other in terms of the parameters selected or the approach. Results: This paper explicates a survey on WSNs, upgrading EE in WSN based on the state model, EE improvement in WSN based on the cluster model, and EE enhancement in WSN using a contentbased model with its performance comparison. Conclusion: This paper evaluates the number of cluster heads (CHs) of CADS in different nodes with different schemes for WSNs and a comparison of the four schemes in WSNs.

Single-phase static immersion-cooled battery thermal management system with finned heat pipes

Effective thermal management is of critical importance to the performance and safety of lithium-ion batteries. However, research on small and medium-sized battery packs remains scarce. This paper proposes a new immersion cooling method. It combines finned heat pipes with a single-phase static immersion fluid, achieving optimal battery pack homogeneity in existing studies while outperforming the performance of conventional immersion cooling. The method is particularly suitable for energy storage batteries and small and medium-sized battery pack cooling applications. This paper presents a verification of the cooling performance of the cooling method through a combination of simulation analysis and experimental validation. The results demonstrate that the cooling method can enable small and medium-sized battery packs to meet the heat dissipation requirements of 3C or even higher discharge rate under natural convection conditions. In the active cooling scenario, when the battery pack is discharged at a 3C multiplier, the cooling scheme of 9 finned heat pipes can reduce the maximum temperature of the flow-submerged cooled battery pack by 6.4 °C and the maximum temperature difference between cells by 5.3 °C. Even when the battery is discharged at an extreme multiplication rate of 6C, the maximum temperature of the battery pack can be maintained at approximately 60 °C, with a maximum temperature difference between cells of around 15 °C. Based on the aforementioned simulation and experimental results, this paper also validates and analyses the main factors affecting the cooling effect of the method, namely the type of submerged liquid and the number of finned heat pipes.

Rationale and methodology for examining the combination of aerobic exercise and cognitive rehabilitation on new learning and memory in persons with multiple sclerosis and mobility disability: Protocol for a randomized controlled trial

BackgroundThis paper describes the protocol for a Phase I/II, parallel-group, single-blind randomized controlled trial (RCT). The RCT investigates the combined effects of 12-weeks of aerobic exercise training (AET) integrated with virtual reality (VR) and cognitive rehabilitation (CR) on new learning and memory in 78 persons with multiple sclerosis (MS) who have mobility disability and objective impairments in learning and memory. MethodsParticipants will undergo baseline assessments consisting of neuropsychological testing, neuroimaging, self-report questionnaires, and cardiorespiratory fitness. Participants will then be randomized into one of two conditions using concealed allocation: aerobic cycling exercise that incorporates VR combined with CR or stretching and toning (i.e., active control; S/T) combined with CR. Participants will be masked regarding the intent of the conditions. After 7-weeks of exercise alone, the 5-week Kessler Foundation modified Story Memory Technique (KF-mSMT®) will be integrated into the training. After the 12-week training period, participants will complete the same measures as at baseline administered by treatment-blinded assessors. Primary study outcomes include new learning and memory (NLM) measured by a small battery of neuropsychological assessments that assess list learning (California Verbal Learning Test-II), prose memory (Memory Assessment Scale), visuospatial memory (Brief Visuospatial Memory Test-Revised), and everyday memory (Ecological Memory Simulations). Secondary study outcomes include neuroimaging outcomes of hippocampal structure, function, and connectivity. ConclusionIf successful, this trial will provide the first Class I evidence supporting the unique combination of aerobic cycling exercise with VR and CR for treating MS-related learning and memory impairments in persons with mobility disability.

Research progress of lithium manganese iron phosphate cathode materials: From preparation to modification

AbstractLiFePO4 is very promising for application in the field of power batteries due to its high specific capacity (170 mAh−1), stable structure, safety, low price, and environmental friendliness. However, it is well known that the slow electron transport and Li+ transport of LiFePO4 results in a rate performance that is far below the requirements for small batteries, resulting in a low LiFePO4 energy density. In order to solve this problem, LiMn1−xFexPO4 (LMFP) cathode material was synthesized by combining Fe and Mn in a certain ratio, and its material properties were improved. Here, we provide a detailed review of LiMn1−xFexPO4 anode material preparation. In addition, this review focuses on the preparation of LiMn1−xFexPO4 and several modification methods to compensate for the inherent deficiencies of certain materials, as well as predicting their future trends.

Elastokinematic design and optimization of the multi-link torsion axle – a novel rear axle concept for BEVs

This work describes the elastokinematic design and optimization for a new, package space saving rear suspension especially intended for small battery electric vehicles. Mainly for reasons of cost and weight efficiency, the twist-beam axle is widely used, especially in the A and B segments. One of the main disadvantages of twist-beam axles is that they limit the rear package space because of the torsion profile. The basic idea of this work was to reverse the installation direction of the twist-beam axle to create an additional package space for the battery. To obtain an optimized elastokinematic design, a new algorithm was introduced that allows modeling of the elastokinematic behavior of the new mechanism. A Pareto optimization of the body connecting bushing was performed, revealing that a specific spatial orientation enables effective compensation for camber compliance deficits. Finally, the Pareto-optimized elastokinematic design was compared to compliance measurement results of a prototype vehicle equipped with the new suspension concept.

System-bath correlations and finite-time operation enhance the efficiency of a dissipative quantum battery

The reduced state of a small system strongly coupled to a thermal bath may be athermal and used as a small battery once disconnected. The unitarily extractable energy (a.k.a. ergotropy) will be negligible if the disconnecting process is too slow. To study the efficiency of this battery, we consider the cycle of disconnecting, extracting, and connecting the battery back to the bath. Efficiency, i.e. the ratio between ergotropy and connecting plus disconnecting work, is a function of disconnecting time. We consider the Caldeira–Leggett model of a quantum battery in two scenarios. In the first scenario, we assume that the discharged battery is uncorrelated to the bath when connecting back and find that the efficiency peaks at an optimal disconnecting time. In the second scenario, the discharged battery is correlated to the bath, and see that the optimal efficiency corresponds to an instantaneous disconnection. On top of these results, we analyze various thermodynamic quantities for these Caldeira–Leggett quantum batteries and express the first and second laws of thermodynamics for the cycles in simple form despite the system-bath initial correlations and strong coupling regime of the working device.

Integration of very small modular reactors and renewable energy resources in the microgrid

Hybrid microgrids, integrating local energy resources, present a promising but challenging solution, especially in areas with limited or no access to the national grid. Reliable operation of off-grid energy systems necessitates sustainable energy sources, given the intermittent nature of renewables. While fossil fuel diesel generators mitigate risks, they increase carbon emissions. This study assesses the viability of integrating a very small modular renewable energy reactor into a microgrid for replacing conventional diesel generators, substantially curbing greenhouse gas emissions. A comprehensive analysis, including design and economic evaluation, was conducted for an off-grid community microgrid with an annual generation and load of 8.5 GWh and 7.8 GWh, respectively. The proposed microgrid configurations incorporate very small modular reactors, alongside solar, wind, and battery storage systems. MATLAB modeling and simulation across eight cases, accounting for seasonal variations, demonstrate the technical and economic feasibility of case 7. This configuration, integrating modular reactors, photovoltaics, wind turbines, and battery storage, satisfactorily meets load demands. Notably, it boasts a high internal rate of return up to ∼31% and a shorter payback period of around 4 years compared to alternative scenarios.

An efficient beaconing of bluetooth low energy by decision making algorithm

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

Gesture recognition with a 2D low-resolution embedded camera to minimise intrusion in robot-led training of children with autism spectrum disorder

Growing evidence shows the potential benefits of robot-assisted therapy for children with Autism Spectrum Disorder (ASD). However, when developing new robotics technologies, it must be considered that this condition often causes increased anxiety in unfamiliar settings. Indeed, children with ASD have difficulties accepting changes like introducing multiple new technological devices in their routines, therefore, embedded solutions should be preferred. Also, in this context, robots should be small as children find the bigger ones scary. This leads to limited computing resources onboard as small batteries power them. This article presents a study on gesture recognition using video recorded only by the camera embedded in a NAO robot, while it was leading a clinical procedure. The video is 2D and low quality because of the limits of the NAO-embedded computing resources. The recognition is made more challenging by robot movements, which alter the vision by moving the camera and sometimes by obstructing it with the robot’s arms for short periods. Despite these challenging real-world conditions, in our experiments, we have tuned and improved state-of-the-art algorithms to yield an accuracy higher than 90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$90\\%$$\\end{document} in the gesture classification, with the best accuracy being 94%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$94\\%$$\\end{document}. This level of accuracy is suitable for evaluating the children’s performance and providing information for the diagnosis and continuous assessment of the therapy. We have also considered the performance improvement of using a low-power GPU-AI accelerator embedded system, which could be included in future robots, to enable gesture analysis during the therapy, which could be adapted to the child’s performance.Graphical abstract

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Monitoring the health of lithium-ion batteries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell’s major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior.

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles. There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users. This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented. With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging. The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network. The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

Barriers to circular economy: Insights from a small electric vehicle battery manufacturer

Small and medium enterprises (SMEs) play a crucial role in the transition to circular economy (CE). The CE transition for lithium-ion batteries (LIBs) with a focus on manufacturing SMEs underpins this research, which explores the potential barriers to implementing a CE business model. Barriers to CE transition are a growing research area, and this study makes two key contributions to the literature. First, we explore the interactions between different barriers to CE transition. Second, we do this through an in-depth case study of a small enterprise at the center of the electrification and mobility ecosystem that specializes in high-voltage batteries for special vehicles. Our results show that barriers related to the regulatory framework, market structure, actors and their attitudes, structure, technology, and tools hinder CE transition. Moreover, the interactions between these barriers keep the system unbalanced by reinforcing loops and making CE transition more challenging.

Designing small batteries and adaptive charging strategies for operation on rough terrain

The global trajectory is swiftly progressing towards the adoption of electric vehicles (EVs). Given that batteries constitute a pivotal component within the domain of EV technology, their performance and life have become of utmost importance. Battery life is affected by multiple internal and external factors. This paper addresses real-time challenges associated with using battery packs in dynamic on-road conditions characterized by variable discharge rates, temperatures, depth of discharge, storage conditions, and rough terrains. The study involves designing 2.8kWh battery packs and using them to power e-rickshaws in on-road conditions in places like India. The study shows how minor issues, which are often overlooked during the initial pack design phase, can impact the long-term operational performance of the batteries.In real-world scenarios, users lack control over the discharge of a battery. However, the charging process can be effectively controlled. In this paper, we propose an adaptive slow-charging strategy to prolong the life of the battery pack. The suggested charging algorithm regulates the charging current based on parameters such as battery temperature, state of health, and terminal voltage. To assess the impact of this algorithm, we conducted tests on 2.8kWh battery packs used in e-rickshaws for over two years under real-world driving conditions. The effectiveness of the proposed algorithm is demonstrated by comparing the battery degradation with the standard constant current constant voltage charging. The study also introduces a novel balancing technique involving temporary suspension of balancing and charging currents during voltage measurements, mitigating errors linked to ohmic drops. Additionally, the research examines the degradation caused by parking vehicles for extended periods.

An optimized deep learning-based fault-tolerant mechanism for energy efficient data transmission in IoT

<p class="Author">Artificial Intelligence (AI) based framework for the Internet of Things (IoTs) have gained worldwide attention in recent years, mainly with the explosion of Micro-Electro-Mechanical Systems (MEMS) technology. Basically, MEMS has facilitated the development of tiny and smart sensors for the IoT-based framework. An AI-based IoT model is an emerging technology that helps in both fault-tolerant as well as energy-efficient data transmission purposes. For efficient data transmission in an IoT-based model, the concept of Wireless Sensor Network (WSN) plays a vital role that comprises various sensor nodes that communicate together to monitor and gather information from the various Region of Interest (RoI). Generally, sensor nodes are tiny in size and having a small battery life, limited sensing, processing, and communication capabilities. So, the fault-tolerant mechanism for energy efficient data transmission in IoT is a good initiative with the combination of Deep Learning as an AI approach. In this research article, the concept of deep learning-based fault-tolerant mechanism in IoT frameworks for energy efficient data transmission is proposed in an optimized manner. Here, the concept of the Grouped-Bee Colony (GBC) algorithm is designed for the fault detection mechanism as an optimization approach along with the Deep Learning as an AI.<em></em></p>

A quad DC source switched three-phase multilevel DC-link inverter topology

The concept of an isolated DC source cascaded multilevel inverter finds good solutions for generating quality output voltage for low-medium power applications. It shapes the output voltage from three levels into a number of steps closer to a sinusoidal shape using small DC sources or batteries. Several advantages have been sighted like lower voltage stress and bearing noise, and lesser THD. However, a common issue in the MLIs is the total components required which increase with the rise in voltage levels. This paper proposes a three-phase MLI design having several isolated quad voltage source modules including an H-Bridge inverter. The design suggested claims reduced switching components for current conduction paths showing improved output quality. The operational features of the suggested MLI have been analyzed using Matlab/Simulink software, furthermore, an experimental module is constructed for demonstrating the effectiveness of the simulated results.