Insights and reviews on battery lifetime prediction from research to practice

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.

Fully relativistic energies,transition properties,and lifetimes of lithium-like germanium

Employing two fully relativistic methods,the multi-reference configuration Dirac-Hartree-Fock(MCDHF)methodand the relativistic many-body perturbation theory(RMBPT)method,we report energies and lifetime values for the lowest35 energy levels of the(1s^(2))nl configurations(where the principal quantum number n=2-6 and the angular quantum numberl=0,...,n-1)of lithium-like germanium(Ge XXX),as well as complete data on the transition wavelengths,radiativerates,absorption oscillator strengths,and line strengths between the levels.Both the allowed(E1)and forbidden(magneticdipole M1,magnetic quadrupole M2,and electric quadrupole E2)ones are reported.The results from the two methodsare consistent with each other and align well with previous accurate experimental and theoretical findings.We assess theoverall accuracies of present RMBPT results to be likely the most precise ones to date.The present fully relativistic resultsshould be helpful for soft x-ray laser research,spectral line identification,plasma modeling and diagnosing.The datasetspresented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00113.00135.

Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

Gross Alpha and Beta Activities and Related Lifetime Risks Assessment Due to Ingestion of Drinking Water from Different Sources in the District of Abidjan, Cote d’Ivoire

Drinking good quality water is essential for better health. It is therefore essential to assess the radiological quality of all water consumed in the District of Abidjan in order to prevent related hazards. Thus, the objective of this study was to assess the risk of cancer due to the ingestion of alpha and beta emitting radionuclides in the different types of water consumed in the region. A total of 63 water samples with 43 tap water samples, 5 bottled mineral water and 15 sachet water samples was collected and taken to GAEC laboratory for analysis. The low background Gas-less Automatic Alpha/Beta counting system (Canberra iMaticTM) was used to determine alpha and beta activity concentrations. Activity concentrations of both gross alpha and gross beta obtained in water sample were respectively lower than the WHO recommended limits of 0.1 Bq/l and 1 Bq/l. Also, the annual effective dose and total equivalent effective dose found in mineral bottled water samples were higher than in other types of water. The assessment of radiological lifetime risk has shown values of cancer risk due to ingestion alpha and beta emitters lower than recommended limit. These results indicate that there is no health hazard associated to consumption of water in the District of Abidjan.

Numerical and Experimental Study of Localized Lifetime Control LIGBT by High Dose He Ion Implantation

A high speed LIGBT with localized lifetime control by using high dose and low en ergy helium implantation(LC-LIGBT) is proposed.Compared with conventional LIGB Ts,particle irradiation results show that trade-off relationship between turn- off time and forward voltage drop is improved.At the same time,the forward volta ge drop and turn-off time of such device are researched,when localized lifetime control region place near the p+-n junction,even in p+ anode.The results s how for the first time,helium ions,which stop in the p+ anode,also contribute to the forward voltage drop increasing and turn-off time reducing.

One Method for Fast Gate Oxide TDDB Lifetime Prediction

A method for fast gate oxide TDDB lifetime prediction for process control monitors （PCM） is proposed. For normal TDDB lifetime prediction at operation voltage and temperature, we must ge（three lifetimes at relative low stress voltages and operation temperature. Then we use these three lifetimes to project the TDDB lifetime at operation voltage and temperature via the E-model. This requires a very long time for measurement. With our new method,it can be calculated quickly by projecting the TDDB lifetime at operation voltage and temperature with measurement data at relatively high stress voltages. Our test case indicates that this method is very effective. And the result with our new method is very close to that with the normal TDDB lifetime prediction method. But the measurement time is less than 50s for one sample,less than 1/100000 of that with the normal prediction method. With this new method,we can monitor gate oxide TDDB lifetime on-line.

Applications,of fluorescence lifetime imaging in clinical medicine

Fluorescence lifetime is not only associated with the molecular structure f fuorophores,but alsostrongly depends on the environment around them,which llows fuorescence lifetime imagingmicroscopy(FLIM)to be used as a tool for precise measurement of the cell or tisue microenvironment,This review introduces the basic principle of fuorescence lifetime imagingtechnology and its application in clinical medicine,including research and diagnosis of diseases inskin,brain,eyes,mouth,bone,blood vessels and cavity organs,and drug evaluation.As anoninvasive,nontoxic and nonionizing radiation technique,FLIM demonstrates excellent per-formance with high sensitivity and specificity,which allows to determine precise position of thelesion and,thus,has good potential for application in biomedical research and clinical diagnosis.

The NBEE and NWEE classes of lifetime distributions and their properties

A class of lifetime distributions, new better than equilibrium in expectation （NBEE）, and its dual, new worse than equilibrium in expectation （NWEE）, are studied based on the comparison of the expectations of lifetime X and its equilibrium Xo. The relationships of the NBEE （NWEE） and other lifetime distribution classes are discussed. It is proved that the NBEE is very large, and increasing failure rate （IFR）, new better than used （NBU） and the L class are its subclasses. The closure properties under two kinds of reliability operations, namely, convolution and mixture, are investigated. Furthermore, a Poisson shock model and a special cumulative model are also studied, in which the necessary and sufficient conditions for the NBEE （NWEE） lifetime distribution of the systems are established. In the homogenous Poisson shock model, the system lifetime belongs to NBEE（NWEE） if and only if the corresponding discrete failure distribution belongs to the discrete NBEE（NWEE）. While in the cumulative model, the system has an NBEE lifetime if and only if the stochastic threshold of accumulated damage is NBEE.

Fluid-structure Interaction Analysis and Lifetime Estimation of a Natural Gas Pipeline Centrifugal Compressor under Near-choke and Near-surge Conditions

Up to present, there have been no studies concerning the application of fluid-structure interaction（FSI） analysis to the lifetime estimation of multi-stage centrifugal compressors under dangerous unsteady aerodynamic excitations. In this paper, computational fluid dynamics（CFD） simulations of a three-stage natural gas pipeline centrifugal compressor are performed under near-choke and near-surge conditions, and the unsteady aerodynamic pressure acting on impeller blades are obtained. Then computational structural dynamics（CSD） analysis is conducted through a one-way coupling FSI model to predict alternating stresses in impeller blades. Finally, the compressor lifetime is estimated using the nominal stress approach. The FSI results show that the impellers of latter stages suffer larger fluctuation stresses but smaller mean stresses than those at preceding stages under near-choke and near-surge conditions. The most dangerous position in the compressor is found to be located near the leading edge of the last-stage impeller blade. Compressor lifetime estimation shows that the investigated compressor can run up to 102.7 h under the near-choke condition and 200.2 h under the near-surge condition. This study is expected to provide a scientific guidance for the operation safety of natural gas pipeline centrifugal compressors.

Probabilistic Lifetime Assessment of Marine Reinforced Concrete with Steel Corrosion and Cover Cracking

In order to study the durability behavior of marine reinforced concrete structure suffering from chloride attack, the structural service life is assumed to be divided into three critical stages, which can be characterized by steel corrosion and cover cracking. For each stage, a calculated model used to predict the lifetime is developed. Based on the definition of durability limit state, a probabilistic lifetime model and its time-dependent reliability analytical method are proposed considering the random natures of influencing factors. Then, the probabilistic lifetime prediction models are applied to a bridge pier located in the Hangzhou Bay with Monte Carlo simulation. It is found that the time to corrosion initiation to follows a lognormal distribution, while that the time from corrosion initiation to cover cracking t~ and the time for crack to develop from hairline crack to a limit crack width t2 can be described by Weibull distributions. With the permitted failure probability of 5.0%, it is also observed that the structural durability lifetime mainly depends on the durability life to and that the percentage of participation of the life to to the total service life grows from 61.5% to 83.6% when the cover thickness increases from 40 mm to 80 mm. Therefore, for any part of the marine RC bridge, the lifetime predictions and maintenance efforts should also be directed toward controlling the stage of corrosion initiation induced by chloride ion.

Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error

For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.

Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data

Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.

Room-temperature continuous-wave operation of GaN-based blue-violet laser diodes with a lifetime longer than 1000h

GaN-based continuous-wave operated blue-violet laser diodes(LDs) with long lifetime are demonstrated, which are grown on a c-plane GaN substrate by metal organic chemical vapor deposition with a 10 × 600 μm^2 ridge waveguide structure.The electrical and optical characteristics of a blue-violet LD are investigated under direct-current injection at room temperature(25 °C). The stimulated emission wavelength and peak optical power of the LD are around 413 nm and over 600 mW, respectively.In addition, the threshold current density and voltage are as small as 1.46 kA/cm^2 and 4.1 V, respectively. Moreover, the lifetime is longer than 1000 hours under room-temperature continuous-wave operation.

The Application of Neural Network in Lifetime Prediction of Concrete

There are many difficulties in concrete endurance prediction, especially in accurate predicting service life of concrete engineering. It is determined by the concentration of S042-/ Mg2+ / Cl- /Ca2+ , reactionareas , the cycles of freezing and dissolving, alternatives of dry and wet state, the kind of cement, etc. . In general , because of complexity itself and cognitive limitation, endurance prediction under sulphate erosion is still illegible and uncertain, so this paper adopts neural network technology to research this problem. Through analyzing , the paper sets up a 3 - levels neural network and a 4 - levels neural network to predict the endurance undersulphate erosion. The 3 - levels neural network includes 13 inputting nodes, 7 outputting nodes and 34 hidden nodes. The 4 - levels neural network also has 13 inputting nodes and 7 outputting nodes with two hidden levels which has 1 nodes and 8 nodes separately. In the end the paper give a example with laboratorial data and discussion the result and deviation. The paper shows that deviation results from some faults of training specimens; such as few training specimens and few distinctions among training specimens. So the more specimens should be collected to reduce data redundancy and improve the reliability of network analysis conclusion.

Lifetime Prolonging Algorithms for Underwater Sensor Networks

Underwater acoustic modem technology has attained a level of maturity to support underwater acoustic sensor networks （UASNs） which are generally formed by acoustically connected sensor nodes and a surface station providing a link to an on-shore control center. While many applications require long-term monitoring of the deployment area, the batterypowered network nodes limit the lifetime of UASNs. Therefore, designing a UASN that minimizes the power consumption while maximizing lifetime becomes a very difficult task. In this paper, a method is proposed to determine the optimum number of clnstens throngh combining an application-specific protocol architecture and underwater acoustic communication model so as to reduce the energy dissipation of UASNs. Deploying more sensor nodes which work alternately is another way to prolong the lifetime of UASNs, An algorithm is presented for selecting sensor nodes and putting them into operation in each round, ensuring the monitoring to the whole given area. The present results show that the algorithm can help prolong system lifetime remarkably when it is applied to other conventional approaches for sensor networks under the condition that the sensor node density is high.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Analysis of Numerical Simulation Results of LIPS-200 Lifetime Experiments

Accelerator grid structural and electron backstreaming failures are the most important factors affecting the ion thruster＇s lifetime.During the thruster＇s operation,Charge Exchange Xenon（CEX） ions are generated from collisions between plasma and neutral atoms.Those CEX ions grid＇s barrel and wall frequently,which cause the failures of the grid system.In order to validate whether the 20 cm Lanzhou Ion Propulsion System（LIPS-200） satisfies China＇s communication satellite platform＇s application requirement for North-South Station Keeping（NSSK）,this study analyzed the measured depth of the pit/groove on the accelerator grid＇s wall and aperture diameter＇s variation and estimated the operating lifetime of the ion thruster.Different from the previous method,in this paper,the experimental results after the 5500 h of accumulated operation of the LIPS-200 ion thruster are presented firstly.Then,based on these results,theoretical analysis and numerical calculations were firstly performed to predict the on-orbit lifetime of LIPS-200.The results obtained were more accurate to calculate the reliability and analyze the failure modes of the ion thruster.The results indicated that the predicted lifetime of LIPS-200＇s was about 13218.1 h which could satisfy the required lifetime requirement of 11000 h very well.

Intra-beam scattering and beam lifetime in a candidate lattice of the soft X-ray diffraction-limited storage ring for the upgraded SSRF

New-generation synchrotron light sources are being designed and operated worldwide to provide brighter radiation by reducing the beam emittance to X-ray diffraction limits.Intra-beam scattering(IBS)and Touschek scattering in such facilities are significant and require attention because of their ultra-low emittance.Therefore,cure strategies need to be carefully studied to obtain highquality photon beams.For the Shanghai Synchrotron Radiation Facility Upgrade(SSRF-U),a candidate lattice of the storage ring,reaching the soft X-ray diffraction limit,was designed and presented for the first time in this study.The emittance growth and beam lifetime in the SSRF-U storage ring were studied using particle simulations for a series of different machine configurations.The gains with RF frequencies of 100 MHz and 500 MHz were compared.Along with a better filling pattern,a more suitable RF frequency was adopted in the SSRF-U.The variations in the equilibrium beam emittance with beam coupling and bunch-lengthening were identified using simulations.Optimal beam coupling and required bunch-lengthening for the SSRF-U storage ring were thus determined.The fitness of the beam energy in the SSRF-U was subsequently assessed using the obtained parameters.Additionally,the Touschek scattering and beam lifetime were calculated,and an acceptable total beam lifetime was obtained.

Effects of Shot Peening Process on Thermal Cycling Lifetime of TBCs Prepared by EB-PVD

Conventional two-layered thermal barrier coatings （TBCs） are prepared by electron beam physical vapor deposition （EB-PVD） with ZrO2-8 wt% Y2O3 （8YSZ） as top coat and CoCrAlY as bond coat on disk-shaped Ni based super-alloy. In this paper, three kinds of shot peening process with different lengths of operating time were adopted for bond coating. As a result, changes took place in its surface roughness and the surface micro-hardness. A thermal cycling test at 1 273 Kx55 rain and another at room temperature for 5 min were performed to study the effects of shot peening process on the thermal cycling lifetime of TBCs. It is found that a moderate shot peening process will be able to prolong the life time. The oxidation dynamic of the as-processed TBCs basically accords with the parabolic rule, and the oxidation test also attests to the spallation between YSZ and thermal growth oxide （TGO） responsible mainly for the failure of TBCs.