Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in th...Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.展开更多
In the present study, we aimed to investigate the effects of Xiao-Xu-Ming decoction extract(XXM) on lipopolysaccaride(LPS)-induced neuroinflammation in vitro and in vivo. In vitro, the microglia BV2 cells were treated...In the present study, we aimed to investigate the effects of Xiao-Xu-Ming decoction extract(XXM) on lipopolysaccaride(LPS)-induced neuroinflammation in vitro and in vivo. In vitro, the microglia BV2 cells were treated with 200 ng/mL LPS for 24 h to induce inflammatory responses. In vivo, mice were treated with 5 mg/kg LPS to induce inflammatory responses. The NO level was determined by Griess Reagents. The levels of IL-1β, IL-6, TNF-α and MCP-1 were determined by ELISA. The expressions of Iba-1, TLR4 and MyD88 at the protein levels were determined by Western blotting analysis. The mRNA levels of TLR4 and MyD88 were determined by real-time PCR. In vitro, XXM significantly reduced the levels of various pro-inflammatory factors, including NO, IL-1β, IL-6 and TNF-α, induced by LPS in the supernatant of BV2 cells and suppressed expressions of inflammatory proteins TLR4 and MyD88 induced by LPS in BV2 cells. In vivo, XXM significantly inhibited microglia activation, attenuated LPS-induced inflammatory factors and chemokine production, such as IL-1β, IL-6, TNF-α and MCP-1, and inhibited the expressions of inflammatory proteins including TLR4 and MyD88, in the cortex of LPS-induced mice. Our findings suggested that XXM could attenuate LPS-induced neuroinflammation via down-regulating TLR4/MyD88 signaling pathway.展开更多
Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and...Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.展开更多
Acute kidney injury(AKI)is a common clinical serious illness.Esculin(ES)is a coumarin compound of traditional Chinese medicine Cortex Fraxini.Our previous study has found that ES protects against inflammation and rena...Acute kidney injury(AKI)is a common clinical serious illness.Esculin(ES)is a coumarin compound of traditional Chinese medicine Cortex Fraxini.Our previous study has found that ES protects against inflammation and renal damage in diabetic rats.In the present study,we aimed to investigate the effects and the possible mechanism of ES against lipopolysaccharides(LPS)-induced AKI in mice.Renal morphology was observed by H&E staining.Renal function was evaluated by blood urea nitrogen(BUN)level and creatinine content in serum.Inflammatory factor levels were measured by ELISA assay.The inflammatory proteins were analyzed by RT-PCR and Western blotting analysis.The results showed that ES alleviated LPS-induced pathological injury and renal dysfunction,and decreased BUN level and creatinine content in serum.In addition,ES significantly reduced the release of pro-inflammatory factors,including IL-1β,IL-6 and TNF-α,chemokine MCP-1 and cell adhesion molecule ICAM-1.Furthermore,the expressions of inflammatory pathway proteins P2 X7,HMGB1,TLR4 and MyD88 both at the mRNA and protein levels were all down-regulated by ES in the kidney tissue of LPS-challenged mice.These results suggested ES protected against LPS-induced AKI through inhibiting P2 X7 expression and HMGB1/TLR4 inflammatory pathway.展开更多
Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetic...Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetics,the sulfur redox kinetics for Li-S batteries is still not ultrafast.Herein,in this work,a catalyst with dual-single-atom Pt-Co embedded in N-doped carbon nanotubes(Pt&Co@NCNT)was proposed by the atomic layer deposition method to suppress the shuttle effect and synergistically improve the interconversion kinetics from polysulfides to Li_(2)S.The X-ray absorption near edge curves indicated the reversible conversion of Li_(2)Sx on the S/Pt&Co@NCNT electrode.Meanwhile,density functional theory demonstrated that the Pt&Co@NCNT promoted the free energy of the phase transition of sulfur species and reduced the oxidative decomposition energy of Li_(2)S.As a result,the batteries assembled with S/Pt&Co@NCNT electrodes exhibited a high capacity retention of 80%at 100 cycles at a current density of 1.3 mA cm^(−2)(S loading:2.5 mg cm^(−2)).More importantly,an excellent rate performance was achieved with a high capacity of 822.1 mAh g^(−1) at a high current density of 12.7 mA cm^(−2).This work opens a new direction to boost the sulfur redox kinetics for ultrafast Li-S batteries.展开更多
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coup...The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
The exceptional photoelectric performance and high compatibility of perovskite materials render perovskite solar cells highly promising for extensive development,thus garnering significant attention.In perovskite sola...The exceptional photoelectric performance and high compatibility of perovskite materials render perovskite solar cells highly promising for extensive development,thus garnering significant attention.In perovskite solar cells,the hole transport layer plays a crucial role.For the commonly employed organic small molecule hole transport material Spiro-OMeTAD,a certain period of oxidation treatment is required to achieve complete transport performance.However,this posttreatment oxidation processes typically rely on ambient oxidation,which poses challenges in terms of precise control and leads to degradation of the perovskite light absorption layer.This approach fails to meet the demands for high efficiency and stability in practical application.Herein,the mechanism of ultrafast laser on Spiro-OMeTAD and the reaction process for laser-induced oxidation of it are investigated.PbI_(2) at Perovskite/Spiro-OMeTAD interface breaks down to produce I_(2) upon ultrafast laser irradiation and I_(2) promote the oxidation process.Through the laser irradiation oxidation processing,a higher stability of perovskite solar cells is achieved.This work establishes a new approach toward oxidation treatment of Spiro-OMeTAD.展开更多
Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constr...Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations.We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles.Our analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world applications.This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves.By leveraging inexpensive unlabeled data in a self-supervised approach,our method demonstrates improvements in average root mean square errors of 74.54%and 60.50%in the best and worst cases,respectively,compared to the supervised benchmark.This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in realworld scenarios.展开更多
Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds...Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds of enterprises,and put forward docking strategies.The analysis shows that the mismatch between skills adaptation and literacy is the main cause of slow employment.To this end,research and design training programs,including curriculum restructuring,school-enterprise cooperation practice platforms,and employment-oriented quality improvement plans are imperative.At the same time,we will develop teaching modules for career planning to enhance competitiveness and ensure that the content is synchronized with industry standards.After the implementation,the employment rate of students increased by 15%within six months,the degree of interconnection increased by 20%,and the degree of enterprise recognition increased by 25%.The research effectively promotes the docking of higher vocational education with the market,and has far-reaching significance for easing slow employment.展开更多
Under the background of a severe job market and fierce talent competition,the career planning education of college students is of great importance.In this study,a combination of quantitative and qualitative research m...Under the background of a severe job market and fierce talent competition,the career planning education of college students is of great importance.In this study,a combination of quantitative and qualitative research methods was used;500 students in Shenzhen Polytechnic of Information Technology were surveyed by questionnaire,and 40 counselors were interviewed in depth to comprehensively analyze the role and practical effect of counselors in career planning education.The research showed that the role positioning of counselors includes the guide of students’career development,emotional support,and job-hunting strategy,and the specific path selection involves the strengthening of professional knowledge,the construction of interpersonal relationship networks,and personalized counseling.Based on the survey and interview data,this paper constructed a model for enhancing the role of counselors in career planning education and put forward a set of targeted work paths.展开更多
Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintainin...Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed.In this work,we introduce machine learning surrogates that learn physical dynamics.Specifically,we benchmark three operator-learning surrogates for the Single Particle Model(SPM):Deep Operator Networks(DeepONets),Fourier Neural Operators(FNOs)and a newly proposed parameter-embedded Fourier Neural Operator(PE-FNO),which conditions each spectral layer on particle radius and solid-phase diffusivity.We extend the comparison to classical machine-learning baselines by including U-Nets.Models are trained on simulated trajectories spanning four current families(constant,triangular,pulse-train,and Gaussian-random-field)and a full range of State-of-Charge(SOC)(0%to 100%).DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads.The basic FNO maintains mesh invariance and keeps concentration errors below 1%,with voltage mean-absolute errors under 1.7mV across all load types.Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities.PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.Consequently,PE-FNO’s capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation,recovering anode and cathode diffusivities with 1.14%and 8.4%mean absolute percentage error,respectively,and 0.5918 percentage points higher error in comparison with classical methods.These results pave the way for neural operators to meet the accuracy,speed and parametric flexibility demands of real-time battery management,design-of-experiments and large-scale inference.PE-FNO outperforms conventional neural surrogates,offering a practical path towards high-speed and high-fidelity electrochemical digital twins.展开更多
The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for...The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.展开更多
Metasurfaces have intrigued long-standing research interests and developed multitudinous compelling applications owing to their unprecedented capability for manipulating electromagnetic waves,and the emerging programm...Metasurfaces have intrigued long-standing research interests and developed multitudinous compelling applications owing to their unprecedented capability for manipulating electromagnetic waves,and the emerging programmable coding metasurfaces(PCMs)provide a real-time reconfigurable platform to dynamically implement customized functions.Nevertheless,most existing PCMs can only act on the single polarization state or perform in the limited polarization channel,which immensely restricts their practical application in multitask intelligent metadevices.Herein,an appealing strategy of the PCM is proposed to realize tunable functions in co-polarized reflection channels of orthogonal circularly polarized waves and in co-polarized and cross-polarized reflection channels of orthogonal linearly polarized waves from 9.0 to 10.5 GHz.In the above six channels,the spindecoupled programmable meta-atom can achieve high-efficiency reflection and 1-bit digital phase modulation by selecting the specific ON/OFF states of two diodes,and the phase coding sequence of the PCM is dynamically regulated by the field-programmable gate array to generate the desired function.A proof-of-concept prototype is constructed to verify the feasibility of our methodology,and numerous simulation and experimental results are in excellent agreement with the theoretical predictions.This inspiring design opens a new avenue for constructing intelligent metasurfaces with higher serviceability and flexibility,and has tremendous application potential in communication,sensing,and other multifunctional smart metadevices.展开更多
Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classif...Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data.We extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical characteristics.The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms.Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing.The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,respectively.Moreover,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,respectively.This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.展开更多
There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such pr...There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such prediction methods require large amounts of data,generally obtained through experiments or during the operation phase,resulting in substantial economic and time efforts.In this context,generating realistic battery pack data that covers all sensor values a battery management system receives,as well as including fault models,is of particular interest and can mitigate the need to perform extensive laboratory testing.This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics.Initially,the electrical,thermal,and aging modeling of a battery pack is performed.After this,four types of faults,namely hard short circuit,soft short circuit,abnormal internal resistance,and abnormal contact resistance,are modeled using equivalent circuit models.To generate realistic data,both cell-to-cell variations and pack-level variations are considered.Variations included are,for example,the manufacturing quality,temperatures,aging processes,road conditions,state of charge,and fault severity.By combining the battery pack models,fault models,and the different variations through Monte Carlo simulations,a large data set representing different packs with varying levels of inconsistencies is generated.展开更多
Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we pr...Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.展开更多
Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper l...Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.展开更多
Smart antennas have received great attention for their potentials to enable communication and perception functions at the same time.However,realizing the function synthesis remains an open challenge,and most existing ...Smart antennas have received great attention for their potentials to enable communication and perception functions at the same time.However,realizing the function synthesis remains an open challenge,and most existing system solutions are limited to narrow operating bands and high complexity and cost.Here,we propose an externally perceivable leakywave antenna(LWA)based on spoof surface plasmon polaritons(SSPPs),which can realize adaptive real-time switching between the“radiating”and“non-radiating”states and beam tracking at different frequencies.With the assistance of computer vision,the smart SSPP-LWA is able to detect the external target user or jammer,and intelligently track the target by self-adjusting the operating frequency.The proposed scheme helps to reduce the power consumption through dynamically controlling the radiating state of the antenna,and improve spectrum utilization and avoid spectrum conflicts through intelligently deciding the radiating frequency.On the other hand,it is also helpful for the physical layer communication security through switching the antenna working state according to the presence of the target and target beam tracking in real time.In addition,the proposed smart antenna can be generalized to other metamaterial systems and could be a candidate for synaesthesia integration in future smart antenna systems.展开更多
基金financially supported by the National Natural Science Foundation of China(NSFC,U20A20310,52107230,52176199,52102470)the support of the research project Model2Life(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF)。
文摘Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.
基金The National Natural Science Foundation of China(Grant No.81473383)the Innovation Fund for Graduate of Beijing Union Medical College(Grant No.2017-1007-02)+1 种基金the Drug Innovation Major Project(Grant No.2018ZX09711001-003-019)the Medical and Health Innovation Project of Chinese Academy of Medical Sciences(Grant No.2016-I2M-3-007,2018-1007-04)
文摘In the present study, we aimed to investigate the effects of Xiao-Xu-Ming decoction extract(XXM) on lipopolysaccaride(LPS)-induced neuroinflammation in vitro and in vivo. In vitro, the microglia BV2 cells were treated with 200 ng/mL LPS for 24 h to induce inflammatory responses. In vivo, mice were treated with 5 mg/kg LPS to induce inflammatory responses. The NO level was determined by Griess Reagents. The levels of IL-1β, IL-6, TNF-α and MCP-1 were determined by ELISA. The expressions of Iba-1, TLR4 and MyD88 at the protein levels were determined by Western blotting analysis. The mRNA levels of TLR4 and MyD88 were determined by real-time PCR. In vitro, XXM significantly reduced the levels of various pro-inflammatory factors, including NO, IL-1β, IL-6 and TNF-α, induced by LPS in the supernatant of BV2 cells and suppressed expressions of inflammatory proteins TLR4 and MyD88 induced by LPS in BV2 cells. In vivo, XXM significantly inhibited microglia activation, attenuated LPS-induced inflammatory factors and chemokine production, such as IL-1β, IL-6, TNF-α and MCP-1, and inhibited the expressions of inflammatory proteins including TLR4 and MyD88, in the cortex of LPS-induced mice. Our findings suggested that XXM could attenuate LPS-induced neuroinflammation via down-regulating TLR4/MyD88 signaling pathway.
基金supported by the Ministry of Science and Technology of China under the contract No.2019YFE0100200the National Natural Science Foundation of China(grant Nos.51706117,52076121)funded by the Tsinghua Scholarship for Overseas Graduate Studies。
文摘Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.
基金The National Key Research&Development Plan(Grant No.2018YFC0311005)the National Natural Science Foundation of China(Grant No.81473383)+2 种基金the Significant New-Drugs Creation of Science and Technology Major Projects(Grant No.2012ZX09103101-078)the Medical and Health Innovation Project of Chinese Academy of Medical Sciences(Grant No.2016-I2M-3-007)Innovation Fund for Doctoral Students of Beijing Union Medical College(Grant No.2018-1007-04).
文摘Acute kidney injury(AKI)is a common clinical serious illness.Esculin(ES)is a coumarin compound of traditional Chinese medicine Cortex Fraxini.Our previous study has found that ES protects against inflammation and renal damage in diabetic rats.In the present study,we aimed to investigate the effects and the possible mechanism of ES against lipopolysaccharides(LPS)-induced AKI in mice.Renal morphology was observed by H&E staining.Renal function was evaluated by blood urea nitrogen(BUN)level and creatinine content in serum.Inflammatory factor levels were measured by ELISA assay.The inflammatory proteins were analyzed by RT-PCR and Western blotting analysis.The results showed that ES alleviated LPS-induced pathological injury and renal dysfunction,and decreased BUN level and creatinine content in serum.In addition,ES significantly reduced the release of pro-inflammatory factors,including IL-1β,IL-6 and TNF-α,chemokine MCP-1 and cell adhesion molecule ICAM-1.Furthermore,the expressions of inflammatory pathway proteins P2 X7,HMGB1,TLR4 and MyD88 both at the mRNA and protein levels were all down-regulated by ES in the kidney tissue of LPS-challenged mice.These results suggested ES protected against LPS-induced AKI through inhibiting P2 X7 expression and HMGB1/TLR4 inflammatory pathway.
基金supported by the National Natural Science Foundation of China(22208039)the Basic Scientific Research Project of the Educational Department of Liaoning Province(LJKMZ20220878)+1 种基金and the Dalian Science and Technology Talent Innovation Support Plan(2022RQ036)supported by the Natural Science and Engineering Research Council of Canada(NSERC),the Canada Research Chair Program(CRC),the Canada Foundation for Innovation(CFI),and Western University。
文摘Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetics,the sulfur redox kinetics for Li-S batteries is still not ultrafast.Herein,in this work,a catalyst with dual-single-atom Pt-Co embedded in N-doped carbon nanotubes(Pt&Co@NCNT)was proposed by the atomic layer deposition method to suppress the shuttle effect and synergistically improve the interconversion kinetics from polysulfides to Li_(2)S.The X-ray absorption near edge curves indicated the reversible conversion of Li_(2)Sx on the S/Pt&Co@NCNT electrode.Meanwhile,density functional theory demonstrated that the Pt&Co@NCNT promoted the free energy of the phase transition of sulfur species and reduced the oxidative decomposition energy of Li_(2)S.As a result,the batteries assembled with S/Pt&Co@NCNT electrodes exhibited a high capacity retention of 80%at 100 cycles at a current density of 1.3 mA cm^(−2)(S loading:2.5 mg cm^(−2)).More importantly,an excellent rate performance was achieved with a high capacity of 822.1 mAh g^(−1) at a high current density of 12.7 mA cm^(−2).This work opens a new direction to boost the sulfur redox kinetics for ultrafast Li-S batteries.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
基金supported by the National Science Fund for Excellent Youth Scholars of China(52222708)the National Natural Science Foundation of China(51977007)。
文摘The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by the National Key Research and Development Program of China(2020YFA0715000)the Guangdong Basic and Applied Basic Research Foundation(2021B1515120041)the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(2021JJLH0058)。
文摘The exceptional photoelectric performance and high compatibility of perovskite materials render perovskite solar cells highly promising for extensive development,thus garnering significant attention.In perovskite solar cells,the hole transport layer plays a crucial role.For the commonly employed organic small molecule hole transport material Spiro-OMeTAD,a certain period of oxidation treatment is required to achieve complete transport performance.However,this posttreatment oxidation processes typically rely on ambient oxidation,which poses challenges in terms of precise control and leads to degradation of the perovskite light absorption layer.This approach fails to meet the demands for high efficiency and stability in practical application.Herein,the mechanism of ultrafast laser on Spiro-OMeTAD and the reaction process for laser-induced oxidation of it are investigated.PbI_(2) at Perovskite/Spiro-OMeTAD interface breaks down to produce I_(2) upon ultrafast laser irradiation and I_(2) promote the oxidation process.Through the laser irradiation oxidation processing,a higher stability of perovskite solar cells is achieved.This work establishes a new approach toward oxidation treatment of Spiro-OMeTAD.
基金supported by the research project‘‘SafeDaBatt”(03EMF0409A)funded by the German Federal Ministry for Digital and Transport(BMDV)+2 种基金the National Key Research and Development Program of China(2022YFE0102700)the Key Research and Development Program of Shaanxi Province(2023-GHYB-05,2023-YBSF-104)the financial support from the China Scholarship Council(CSC)(202206567008)。
文摘Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations.We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles.Our analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world applications.This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves.By leveraging inexpensive unlabeled data in a self-supervised approach,our method demonstrates improvements in average root mean square errors of 74.54%and 60.50%in the best and worst cases,respectively,compared to the supervised benchmark.This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in realworld scenarios.
文摘Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds of enterprises,and put forward docking strategies.The analysis shows that the mismatch between skills adaptation and literacy is the main cause of slow employment.To this end,research and design training programs,including curriculum restructuring,school-enterprise cooperation practice platforms,and employment-oriented quality improvement plans are imperative.At the same time,we will develop teaching modules for career planning to enhance competitiveness and ensure that the content is synchronized with industry standards.After the implementation,the employment rate of students increased by 15%within six months,the degree of interconnection increased by 20%,and the degree of enterprise recognition increased by 25%.The research effectively promotes the docking of higher vocational education with the market,and has far-reaching significance for easing slow employment.
文摘Under the background of a severe job market and fierce talent competition,the career planning education of college students is of great importance.In this study,a combination of quantitative and qualitative research methods was used;500 students in Shenzhen Polytechnic of Information Technology were surveyed by questionnaire,and 40 counselors were interviewed in depth to comprehensively analyze the role and practical effect of counselors in career planning education.The research showed that the role positioning of counselors includes the guide of students’career development,emotional support,and job-hunting strategy,and the specific path selection involves the strengthening of professional knowledge,the construction of interpersonal relationship networks,and personalized counseling.Based on the survey and interview data,this paper constructed a model for enhancing the role of counselors in career planning education and put forward a set of targeted work paths.
基金funding from the project“SPEED”(03XP0585)funded by the German Federal Ministry of ResearchTech-nology and Space(BMFTR)and the project“ADMirABLE”(03ETE053E)funded by the German Federal Ministry for Economic Affairs and Energy(BMWE)support of Shell Research UK Ltd.for the Ph.D.studentship of Amir Ali Panahi and the EPSRC Faraday Institution Multi-Scale Modelling Project(FIRG084).
文摘Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed.In this work,we introduce machine learning surrogates that learn physical dynamics.Specifically,we benchmark three operator-learning surrogates for the Single Particle Model(SPM):Deep Operator Networks(DeepONets),Fourier Neural Operators(FNOs)and a newly proposed parameter-embedded Fourier Neural Operator(PE-FNO),which conditions each spectral layer on particle radius and solid-phase diffusivity.We extend the comparison to classical machine-learning baselines by including U-Nets.Models are trained on simulated trajectories spanning four current families(constant,triangular,pulse-train,and Gaussian-random-field)and a full range of State-of-Charge(SOC)(0%to 100%).DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads.The basic FNO maintains mesh invariance and keeps concentration errors below 1%,with voltage mean-absolute errors under 1.7mV across all load types.Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities.PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.Consequently,PE-FNO’s capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation,recovering anode and cathode diffusivities with 1.14%and 8.4%mean absolute percentage error,respectively,and 0.5918 percentage points higher error in comparison with classical methods.These results pave the way for neural operators to meet the accuracy,speed and parametric flexibility demands of real-time battery management,design-of-experiments and large-scale inference.PE-FNO outperforms conventional neural surrogates,offering a practical path towards high-speed and high-fidelity electrochemical digital twins.
基金supported by the Natural Science Foundation of China(No.52377221,62172448)the Natural Science Foundation of Hunan Province,China(No.2023JJ30698)+1 种基金Part of the work is supported by the research project“COBALT-P”(16BZF314C)funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK).Lisen Yan is supported by China Scholarship Council(Grant No.202206370146).
文摘The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.
基金Air Force Engineering University(KGD080921020)Natural Science Basic Research Program of Shaanxi Province(2021JQ-363)+1 种基金Fundamental Research Funds for the Central Universities(2242022k30004)National Natural Science Foundation of China(61901508,61971435,62101589,62201609)。
文摘Metasurfaces have intrigued long-standing research interests and developed multitudinous compelling applications owing to their unprecedented capability for manipulating electromagnetic waves,and the emerging programmable coding metasurfaces(PCMs)provide a real-time reconfigurable platform to dynamically implement customized functions.Nevertheless,most existing PCMs can only act on the single polarization state or perform in the limited polarization channel,which immensely restricts their practical application in multitask intelligent metadevices.Herein,an appealing strategy of the PCM is proposed to realize tunable functions in co-polarized reflection channels of orthogonal circularly polarized waves and in co-polarized and cross-polarized reflection channels of orthogonal linearly polarized waves from 9.0 to 10.5 GHz.In the above six channels,the spindecoupled programmable meta-atom can achieve high-efficiency reflection and 1-bit digital phase modulation by selecting the specific ON/OFF states of two diodes,and the phase coding sequence of the PCM is dynamically regulated by the field-programmable gate array to generate the desired function.A proof-of-concept prototype is constructed to verify the feasibility of our methodology,and numerous simulation and experimental results are in excellent agreement with the theoretical predictions.This inspiring design opens a new avenue for constructing intelligent metasurfaces with higher serviceability and flexibility,and has tremendous application potential in communication,sensing,and other multifunctional smart metadevices.
基金funding from the research project“SPEED”(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF)Part of the work was done within the research project"Model2life"(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF).
文摘Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data.We extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical characteristics.The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms.Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing.The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,respectively.Moreover,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,respectively.This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.
基金funding from the research project“Safe-DaBatt”(03EMF0409A)funded by the German Federal Ministry of Digital and Transport(BMDV).
文摘There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such prediction methods require large amounts of data,generally obtained through experiments or during the operation phase,resulting in substantial economic and time efforts.In this context,generating realistic battery pack data that covers all sensor values a battery management system receives,as well as including fault models,is of particular interest and can mitigate the need to perform extensive laboratory testing.This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics.Initially,the electrical,thermal,and aging modeling of a battery pack is performed.After this,four types of faults,namely hard short circuit,soft short circuit,abnormal internal resistance,and abnormal contact resistance,are modeled using equivalent circuit models.To generate realistic data,both cell-to-cell variations and pack-level variations are considered.Variations included are,for example,the manufacturing quality,temperatures,aging processes,road conditions,state of charge,and fault severity.By combining the battery pack models,fault models,and the different variations through Monte Carlo simulations,a large data set representing different packs with varying levels of inconsistencies is generated.
基金funding from the research project"SPEED"(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF).
文摘Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.
基金the Major Scientific and Technological Projects of Anhui Province(Grant No.17030901065)for its support to this research.
文摘Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.
基金supports from the National Natural Science Foundation of China(Grant Nos.62288101,and 61971134)National Key Research and Development Program of China(Grant Nos.2021YFB3200502,and 2017YFA0700200)+2 种基金the Major Project of the Natural Science Foundation of Jiangsu Province(Grant No.BK20212002)the Fundamental Research Funds for Central Universities(Grant No.2242021R41078)the 111 Project(Grant No.111-2-05).
文摘Smart antennas have received great attention for their potentials to enable communication and perception functions at the same time.However,realizing the function synthesis remains an open challenge,and most existing system solutions are limited to narrow operating bands and high complexity and cost.Here,we propose an externally perceivable leakywave antenna(LWA)based on spoof surface plasmon polaritons(SSPPs),which can realize adaptive real-time switching between the“radiating”and“non-radiating”states and beam tracking at different frequencies.With the assistance of computer vision,the smart SSPP-LWA is able to detect the external target user or jammer,and intelligently track the target by self-adjusting the operating frequency.The proposed scheme helps to reduce the power consumption through dynamically controlling the radiating state of the antenna,and improve spectrum utilization and avoid spectrum conflicts through intelligently deciding the radiating frequency.On the other hand,it is also helpful for the physical layer communication security through switching the antenna working state according to the presence of the target and target beam tracking in real time.In addition,the proposed smart antenna can be generalized to other metamaterial systems and could be a candidate for synaesthesia integration in future smart antenna systems.
