Green Energy and Intelligent Transportation最新文献

{"title":"Effects of calendering state on coupled electrochemical-mechanical performance of silicon based composite electrodes","authors":"","doi":"10.1016/j.geits.2024.100154","DOIUrl":"10.1016/j.geits.2024.100154","url":null,"abstract":"<div><p>The super volume changes and severe mechanical degradation have been a hindrance in the wide application of silicon based composite electrodes in commercial lithium-ion batteries (LIBs). Calendering, one procedure in producing LIBs' electrodes, is indispensable to ensure low porosity and energy density. However, the repercussions of the calendering process on the physical characteristics related to the behavior of silicon (Si) based electrodes during the electrochemical reaction have not been well understood. Thus, on account of the deformation characteristic of cantilever electrodes, an in-situ technique is employed to analyze the repercussions of calendering status on the coupled electro-chemo-mechanical performances. During the electrochemical cycling, Young's modulus and diffusion-induced stress in composite electrodes are quantified. The results show that the swelling strain, the stress and the modulus of the Si-based electrode and the calendering degree are positively correlated. Meanwhile, the stress induced by diffusion in the active layer tends to increase in the stage of lithiation and reverses during the delithiation process. Accompany with the SEM analysis, we conclude that the calendering process can induce larger stress, driving the formation of cracks in electrodes. These findings can help understand how the calendering process could affect the capacity dissipating and lifetime of Si based electrodes.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000069/pdfft?md5=7a95bd794b1b2747dd08d97a8acf15a3&pid=1-s2.0-S2773153724000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical evaluation of transit policies in Lima, Peru; resilience of rail rapid transit (Metro) in a developing country","authors":"","doi":"10.1016/j.geits.2024.100172","DOIUrl":"10.1016/j.geits.2024.100172","url":null,"abstract":"<div><p>This paper evaluates rail transit within the context of the transit policies implemented in Lima, Peru. First it reviews the implementation of rapid transit, and bus reform. Secondly, it evaluates the outcomes of such policies by using Total Factor Productivity for policy effectiveness, Data Envelopment Analysis for rapid transit performance, and Generalized Cost of Travel for improvements. This paper finds that implementation failed in enforcing key requirements for rail transit regarding penetration of CBD and short transfers to bus transit; and that the basic assumptions of bus reform did not hold regarding bus oversupply, bus congestion or bus pollution. This paper also finds that outcomes of policies failed dramatically in achieving the planning goals; however, rail transit (Metro) shows high level of resilience in serving large ridership at high speed. On the other hand, bus reform was associated with a disproportionate increase of motorization, well over the effect of income growth or car attractiveness, and more related to the excessive reduction of bus transit capacity ill-advised from unproved bus reform assumptions. This paper recommends expanding rail rapid transit due to its intensive use of green renewable energy and its potential of demand growth if combined with modern Intelligent Transportation services, but this opportunity can be wasted without the proposed policy constraint to achieve lower Generalized Cost of Travel at any governmental intervention for bus reform, instead of just reducing bus transit capacity as implemented. Finally, this paper recommends government to government contracts to build rail transit and to enforce proper planning.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000240/pdfft?md5=03b70c9cd281200c3af5c8159ae47506&pid=1-s2.0-S2773153724000240-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve","authors":"","doi":"10.1016/j.geits.2024.100169","DOIUrl":"10.1016/j.geits.2024.100169","url":null,"abstract":"<div><p>The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000215/pdfft?md5=340544f53cc025b057c2325e7c2cc83e&pid=1-s2.0-S2773153724000215-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote condition monitoring of rail tracks using distributed acoustic sensing (DAS): A deep CNN-LSTM-SW based model","authors":"","doi":"10.1016/j.geits.2024.100178","DOIUrl":"10.1016/j.geits.2024.100178","url":null,"abstract":"<div><p>Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may have far-reaching impacts on communities and the surrounding areas. As a solution to this issue, the use of distributed acoustic sensing (DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures. Nevertheless, analyzing DAS data to assess railroad health or detect potential damage is a challenging task. Due to the large amount of data generated by DAS, as well as the unstructured patterns and substantial noise present, traditional analysis methods are ineffective in interpreting this data. This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs, augmented by sliding window techniques (CNN-LSTM-SW), to advance the state-of-the-art in the railroad condition monitoring system. As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks. Extracting insights from the data of High tonnage load (HTL)- a 4.16 ​km fiber optic and DAS setup, we were able to distinguish train position, normal condition, and abnormal conditions along the railroad. Notably, our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup. Moreover, in terms of pinpointing the train's position, the CNN-LSTM architecture showcased an impressive 97% detection rate. Applying a sliding window, the CNN-LSTM labeled data, the remaining 3% of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition. Altogether, these proposed models exhibit promising potential for accurately identifying various railroad conditions, including anomalies and discrepancies that warrant thorough exploration.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000306/pdfft?md5=a79391c83814fdcc707b2d1095025afd&pid=1-s2.0-S2773153724000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capacity degradation analysis and knee point prediction for lithium-ion batteries","authors":"","doi":"10.1016/j.geits.2024.100171","DOIUrl":"10.1016/j.geits.2024.100171","url":null,"abstract":"<div><p>Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000239/pdfft?md5=899d33fc9697174f1f74aed781dbfb61&pid=1-s2.0-S2773153724000239-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State of charge estimation for electric vehicles using random forest","authors":"","doi":"10.1016/j.geits.2024.100177","DOIUrl":"10.1016/j.geits.2024.100177","url":null,"abstract":"<div><p>This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.902,8% compared to 6.312,7% for ELM, and a lower Mean Absolute Error (MAE) of 4.432,1% versus 5.111,2% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277315372400029X/pdfft?md5=14bd47fe21759638310a9a74eebfe9f8&pid=1-s2.0-S277315372400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares","authors":"Dong Zhen ,&nbsp;Jiahao Liu ,&nbsp;Shuqin Ma ,&nbsp;Jingyu Zhu ,&nbsp;Jinzhen Kong ,&nbsp;Yizhao Gao ,&nbsp;Guojin Feng ,&nbsp;Fengshou Gu","doi":"10.1016/j.geits.2024.100207","DOIUrl":"10.1016/j.geits.2024.100207","url":null,"abstract":"<div><p>Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery, thereby influencing safety of entire electric vehicles. Precise estimation of battery model parameters using key measured signals is essential. However, measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors, potentially diminishing model estimation accuracy. Addressing the challenge of accuracy reduction caused by noise, this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares (BCFFRLS) method. Initially, a variational error model is crafted to estimate the average weighted variance of random noise. Subsequently, an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors, compensating for bias in the parameter estimates. To assess the proposed method's effectiveness in improving parameter identification accuracy, lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule (UDDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC). The proposed method, alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares (FFRLS)—was employed for battery model parameter identification. Comparative analysis reveals substantial improvements, with the mean absolute error reduced by 25%, 28%, and 15%, and the root mean square error reduced by 25.1%, 42.7%, and 15.9% in UDDS, HPPC, and DST operating conditions, respectively, when compared to the FFRLS method.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 4","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000598/pdfft?md5=999324213bcb12e2532de767f001b72e&pid=1-s2.0-S2773153724000598-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model","authors":"","doi":"10.1016/j.geits.2024.100152","DOIUrl":"10.1016/j.geits.2024.100152","url":null,"abstract":"<div><p>The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 ​°C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 ​°C.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 4","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000045/pdfft?md5=9681b8ae8614f1d24c6c280a906197f0&pid=1-s2.0-S2773153724000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries","authors":"","doi":"10.1016/j.geits.2024.100163","DOIUrl":"10.1016/j.geits.2024.100163","url":null,"abstract":"<div><p>Accurate state of charge (SOC) estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles, and it is also a key technology component in battery management systems. In recent years, lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity. However, these methods commonly face the issue of poor model generalization and limited robustness. To address such issues, this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression (SA-SVR) combined with minimum error entropy based extended Kalman filter (MEE-EKF) algorithm. Firstly, a probability-based SA algorithm is employed to optimize the internal parameters of the SVR, thereby enhancing the precision of original SOC estimation. Secondly, utilizing the framework of the Kalman filter, the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF, while the ampere-hour integral physical model serves as the state equation, effectively attenuating the measurement noise, enhancing the estimation accuracy, and improving generalization ability. The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training. The results demonstrate that the proposed method achieves a mean absolute error below 0.60% and a root mean square error below 0.73% across all operating conditions, showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms. The high precision and generalization capability of the proposed method are evident, ensuring accurate SOC estimation for electric vehicles.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 4","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277315372400015X/pdfft?md5=b835d40a859f17ab622d7eebb38969bc&pid=1-s2.0-S277315372400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A joint model of infrastructure planning and smart charging strategies for shared electric vehicles","authors":"","doi":"10.1016/j.geits.2024.100168","DOIUrl":"10.1016/j.geits.2024.100168","url":null,"abstract":"<div><p>This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 4","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000203/pdfft?md5=e126d36b4e1e633af38616e6f348520e&pid=1-s2.0-S2773153724000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}