Aiming at the special needs of heavy-duty hybrid electric vehicles(HEVs)., an adaptive energy management strategy based on frequency domain power distribution is proposed. This article uses MATLAB/Simulink to establish a dynamic model of a heavy-duty HEV. Firstly, the nonlinear autoregressive with external input(NARX) neural network is used to predict the speed of the vehicle. Secondly, according to the predicted vehicle speed, principal component analysis and K-means clustering method are used to classify the working conditions, the corresponding control parameters are adjusted adaptively according to the working conditions category, and the power is distributed in the frequency domain. A piece of real vehicle driving cycle data of the vehicle is used as the simulation condition to verify and analyze the strategy. The simulation results show that this strategy can quickly restore the deviated battery state-of-charge (SoC) to the target value and maintain it stably. The battery's charge and discharge current amplitude are effectively reduced, and meanwhile, the transient working conditions of the engine are reduced too, and therefore the engine can work on the optimal efficiency curve. It is verified that this strategy is an effective real-time energy management strategy for heavy-duty HEVs.
{"title":"Adaptive Energy Management Strategy Based on Frequency Domain Power Distribution","authors":"Cheng-shi Luo, Ying Huang, Xu Wang, Yongliang Li, Fen Guo","doi":"10.1109/CVCI51460.2020.9338521","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338521","url":null,"abstract":"Aiming at the special needs of heavy-duty hybrid electric vehicles(HEVs)., an adaptive energy management strategy based on frequency domain power distribution is proposed. This article uses MATLAB/Simulink to establish a dynamic model of a heavy-duty HEV. Firstly, the nonlinear autoregressive with external input(NARX) neural network is used to predict the speed of the vehicle. Secondly, according to the predicted vehicle speed, principal component analysis and K-means clustering method are used to classify the working conditions, the corresponding control parameters are adjusted adaptively according to the working conditions category, and the power is distributed in the frequency domain. A piece of real vehicle driving cycle data of the vehicle is used as the simulation condition to verify and analyze the strategy. The simulation results show that this strategy can quickly restore the deviated battery state-of-charge (SoC) to the target value and maintain it stably. The battery's charge and discharge current amplitude are effectively reduced, and meanwhile, the transient working conditions of the engine are reduced too, and therefore the engine can work on the optimal efficiency curve. It is verified that this strategy is an effective real-time energy management strategy for heavy-duty HEVs.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123688211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/cvci51460.2020.9338589
{"title":"[Copyright notice]","authors":"","doi":"10.1109/cvci51460.2020.9338589","DOIUrl":"https://doi.org/10.1109/cvci51460.2020.9338589","url":null,"abstract":"","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125063723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338503
Hao Li, M. Yue, Ru-Feng Zhang, C. Fang
This paper mainly studies a harzard escaping strategy for high speed vehicle during tire blowout from the viewpoint of interference. Firstly, a harzard escaping strategy is proposed, mainly concerning with three stages, such as lane keeping, lane changing and emergency braking. Secondly, a vehicle stability controller is designed based on the model predictive control (MPC), which can deal with multiple constraint problem. Thirdly, external interference is employed to simulate the tire blowout of the vehicle at first time. Finally, the effectiveness of the escaping strategy and controller proposed is verified by the Simulink/CarSim co-simulation platform.
{"title":"A Harzard Escaping Strategy for High Speed Vehicle During Tire Blowout From the Viewpoint of Interference*","authors":"Hao Li, M. Yue, Ru-Feng Zhang, C. Fang","doi":"10.1109/CVCI51460.2020.9338503","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338503","url":null,"abstract":"This paper mainly studies a harzard escaping strategy for high speed vehicle during tire blowout from the viewpoint of interference. Firstly, a harzard escaping strategy is proposed, mainly concerning with three stages, such as lane keeping, lane changing and emergency braking. Secondly, a vehicle stability controller is designed based on the model predictive control (MPC), which can deal with multiple constraint problem. Thirdly, external interference is employed to simulate the tire blowout of the vehicle at first time. Finally, the effectiveness of the escaping strategy and controller proposed is verified by the Simulink/CarSim co-simulation platform.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129071324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An adaptive energy management control strategy is proposed for a commuter plug-in hybrid electrical vehicle (PHEV) during car-following process in this paper. The proposed energy management strategy (EMS) is an instantaneous optimization control strategy integrating car-following behavior performance index into adaptive equivalent consumption minimization strategy (A-ECMS). In order to achieve better fuel economy and safety performance under different car-following scenarios, the equivalent factor (EF) of ECMS and the weight factor of car-following performance in the instantaneous optimization cost function are designed as adaptive forms of Map tables about battery state of charge (SOC) and travel distance. The Mapping tables are established offline by utilizing historical traffic data of the commute road and particle swarm optimization (PSO) method. The effectiveness and practicality of the designed EMS are verified through the co-simulation of MATLAB/Simulink and GT-Suite simulator.
{"title":"An Adaptive Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles During Car-Following Process","authors":"Jiaqi Xue, Xiongxiong You, Xiaohong Jiao, Yahui Zhang","doi":"10.1109/CVCI51460.2020.9338659","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338659","url":null,"abstract":"An adaptive energy management control strategy is proposed for a commuter plug-in hybrid electrical vehicle (PHEV) during car-following process in this paper. The proposed energy management strategy (EMS) is an instantaneous optimization control strategy integrating car-following behavior performance index into adaptive equivalent consumption minimization strategy (A-ECMS). In order to achieve better fuel economy and safety performance under different car-following scenarios, the equivalent factor (EF) of ECMS and the weight factor of car-following performance in the instantaneous optimization cost function are designed as adaptive forms of Map tables about battery state of charge (SOC) and travel distance. The Mapping tables are established offline by utilizing historical traffic data of the commute road and particle swarm optimization (PSO) method. The effectiveness and practicality of the designed EMS are verified through the co-simulation of MATLAB/Simulink and GT-Suite simulator.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124653763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338537
Weitao Zou, Jianwei Li, Hongwen He, Qingqing Yang, Cheng Wang
Clean and efficient fuel cell(FC) power systems have shown great potential as an alternative to distributed energy resources. Fuel cell interconnection can relieve the pressure on the grid and meet emergency power needs. A strategy of fuel cell energy management based on evolutionary game is proposed. In the game, the fuel cell energy scheduling problem is treated as a multi-population scenario. Each part of the population has its own mixing strategy. On the other hand, there is a corresponding relationship between pure strategy and mixed strategy. Thus, the strategy here can flexibly meet different demands of power grid. In order to verify the feasibility of this method, the performance of the proposed approach is tested on real data measured on a distribution transformer from the SOREA utility grid company in the region of Savoie, France. The simulation results are compared with the dynamic programming results to further verify the effectiveness of the control strategy,
{"title":"An Energy Management Strategy for Fuel Cell to Grid based on Evolutionary Game","authors":"Weitao Zou, Jianwei Li, Hongwen He, Qingqing Yang, Cheng Wang","doi":"10.1109/CVCI51460.2020.9338537","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338537","url":null,"abstract":"Clean and efficient fuel cell(FC) power systems have shown great potential as an alternative to distributed energy resources. Fuel cell interconnection can relieve the pressure on the grid and meet emergency power needs. A strategy of fuel cell energy management based on evolutionary game is proposed. In the game, the fuel cell energy scheduling problem is treated as a multi-population scenario. Each part of the population has its own mixing strategy. On the other hand, there is a corresponding relationship between pure strategy and mixed strategy. Thus, the strategy here can flexibly meet different demands of power grid. In order to verify the feasibility of this method, the performance of the proposed approach is tested on real data measured on a distribution transformer from the SOREA utility grid company in the region of Savoie, France. The simulation results are compared with the dynamic programming results to further verify the effectiveness of the control strategy,","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121664206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338471
Longqing Li, K. Song, H. Xie
The self-driving forklift, as a promising technology to reduce the labor intensity of workers, can also improve the efficiency of logistics freight transportation. In this paper, a path-following controller that combines cascaded active disturbance rejection controller and geometry-based feedforward controller, is proposed. The cascaded controller, designed based on a kinematic model, minimizes the lateral error via the outer-loop by mitigating the desired heading direction, and then achieved by the inner loop through adjusting the steering angle. The deviation between the simplified kinematic model and the actual forklift motion is lumped as a total disturbance, to be observed by the extended state observer (ESO). In order to enhance the transient response, a geometry-based feedforward controller is developed, computing the desired steering angle through preview. The proposed method effectively improves the response speed and reduces the overshoot. The effectiveness of the algorithm is quantitatively evaluated in experiments.
{"title":"Active Disturbance Rejection Path-following Control for Self- driving Forklift Trucks with Geometry based Feedforward","authors":"Longqing Li, K. Song, H. Xie","doi":"10.1109/CVCI51460.2020.9338471","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338471","url":null,"abstract":"The self-driving forklift, as a promising technology to reduce the labor intensity of workers, can also improve the efficiency of logistics freight transportation. In this paper, a path-following controller that combines cascaded active disturbance rejection controller and geometry-based feedforward controller, is proposed. The cascaded controller, designed based on a kinematic model, minimizes the lateral error via the outer-loop by mitigating the desired heading direction, and then achieved by the inner loop through adjusting the steering angle. The deviation between the simplified kinematic model and the actual forklift motion is lumped as a total disturbance, to be observed by the extended state observer (ESO). In order to enhance the transient response, a geometry-based feedforward controller is developed, computing the desired steering angle through preview. The proposed method effectively improves the response speed and reduces the overshoot. The effectiveness of the algorithm is quantitatively evaluated in experiments.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122921242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338614
P. Hang, Chen Lv, Chao Huang, Yang Xing, Zhongxu Hu, Jiacheng Cai
With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.
{"title":"Human-Like Lane-Change Decision Making for Automated Driving with a Game Theoretic Approach","authors":"P. Hang, Chen Lv, Chao Huang, Yang Xing, Zhongxu Hu, Jiacheng Cai","doi":"10.1109/CVCI51460.2020.9338614","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338614","url":null,"abstract":"With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338540
Akito Saito, T. Shen
This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.
{"title":"Value-Function Learning-based Solutions to Optimal Energy Management Problem of HEVs","authors":"Akito Saito, T. Shen","doi":"10.1109/CVCI51460.2020.9338540","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338540","url":null,"abstract":"This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122326412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338643
P.-Y. Sun, Baiyu Xin, Xing Wang, Huifeng Zhang, Li Long, Qiang Wang
High-pressure injectors are the key actuators of a GDI engine. However, manufacturing deviation leads to the inconsistency of flow characteristics in the ballistic zone, which affects the accuracy of fuel control. Based on the feedback signal of driving voltage of high-pressure injector, the algorithm of recognizing the opening and closing action characteristics of needle valve is studied, and the self-learning method of flow characteristics and the compensation method of injection driving pulse width are proposed. The test results show that the method can effectively improve the consistency of flow characteristics in the ballistic zone for different fuel injectors, reduce the deviation from 25% to 10%, and effectively improve the fuel injection accuracy, so that fuel rail pressure can be increased, injection splitting can be adopted or injection splitting times can be increased under more engine conditions, so as to improve emissions.
{"title":"Research on Correction of Flow Characteristics in Ballistic Zone of GDI Engine Injector","authors":"P.-Y. Sun, Baiyu Xin, Xing Wang, Huifeng Zhang, Li Long, Qiang Wang","doi":"10.1109/CVCI51460.2020.9338643","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338643","url":null,"abstract":"High-pressure injectors are the key actuators of a GDI engine. However, manufacturing deviation leads to the inconsistency of flow characteristics in the ballistic zone, which affects the accuracy of fuel control. Based on the feedback signal of driving voltage of high-pressure injector, the algorithm of recognizing the opening and closing action characteristics of needle valve is studied, and the self-learning method of flow characteristics and the compensation method of injection driving pulse width are proposed. The test results show that the method can effectively improve the consistency of flow characteristics in the ballistic zone for different fuel injectors, reduce the deviation from 25% to 10%, and effectively improve the fuel injection accuracy, so that fuel rail pressure can be increased, injection splitting can be adopted or injection splitting times can be increased under more engine conditions, so as to improve emissions.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126978407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338485
Hang Zhang, Siwen Lv, Yu Zhang, S. Zhang
In order to assess the vehicle emissions and energy consumption in actual driving, the accurate vehicle driving cycles are extremely necessary. On the basis of the previous driving cycle's construction methods, the innovation of this paper is proposing a method for constructing urban driving cycle based on simulated annealing algorithm. The major task is the data processing and optimizing. For data processing, the characteristic parameter of the micro-trips is selected according to the theory of micro-trips analysis, then this paper performs principal component analysis to reduce the dimensions of motion characteristic parameters and the K-means clustering method is used to classify kinematics segments. In the selection of fragments, this paper adopts the simulated annealing algorithm to optimize. The final analysis results show that the error is largely reduced and the accuracy of the operating conditions is further improved.
{"title":"Construction of urban standard driving cycle based on simulated annealing algorithm optimization","authors":"Hang Zhang, Siwen Lv, Yu Zhang, S. Zhang","doi":"10.1109/CVCI51460.2020.9338485","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338485","url":null,"abstract":"In order to assess the vehicle emissions and energy consumption in actual driving, the accurate vehicle driving cycles are extremely necessary. On the basis of the previous driving cycle's construction methods, the innovation of this paper is proposing a method for constructing urban driving cycle based on simulated annealing algorithm. The major task is the data processing and optimizing. For data processing, the characteristic parameter of the micro-trips is selected according to the theory of micro-trips analysis, then this paper performs principal component analysis to reduce the dimensions of motion characteristic parameters and the K-means clustering method is used to classify kinematics segments. In the selection of fragments, this paper adopts the simulated annealing algorithm to optimize. The final analysis results show that the error is largely reduced and the accuracy of the operating conditions is further improved.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127303553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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