Green Energy and Intelligent Transportation最新文献_第10页

Decision-making models on perceptual uncertainty with distributional reinforcement learning 基于分布式强化学习的感知不确定性决策模型

Green Energy and Intelligent Transportation Pub Date : 2023-04-01 DOI: 10.1016/j.geits.2022.100062

Shuyuan Xu , Qiao Liu , Yuhui Hu , Mengtian Xu , Jiachen Hao

引用次数: 3

Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving 智能辅助驾驶实时综合驾驶能力评价算法

Green Energy and Intelligent Transportation Pub Date : 2023-04-01 DOI: 10.1016/j.geits.2023.100065

Fang Liu , Feng Xue , Wanru Wang , Weixing Su , Yang Liu

{"title":"Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving","authors":"Fang Liu , Feng Xue , Wanru Wang , Weixing Su , Yang Liu","doi":"10.1016/j.geits.2023.100065","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100065","url":null,"abstract":"<div><p>To meet the needs of the human-machine co-driving decision problem in the intelligent assisted driving system for real-time comprehensive driving ability evaluation of drivers, this paper proposes a real-time comprehensive driving ability evaluation method that integrates driving skill, driving state, and driving style. Firstly, by analyzing the driving experiment data obtained based on the intelligent driving simulation platform (the experiment can effectively distinguish the driver's driving skills and avoid the interference of driving style), the feature values that significantly represent driving skills and driving state are selected, and the time correlation between driving state and driving skills is pointed out. Furthermore, the concept of relativity in comprehensive driving ability evaluation is further proposed. Under this concept, the natural driving trajectory dataset-HighD is used to establish the distribution map of feature values of the human driver group as the evaluation benchmark to realize the relative evaluation of driving skill and driving state. Similarly, HighD is used to establish a distribution map of human driver style feature values as an evaluation benchmark to achieve relative driving style evaluation. Finally, a comprehensive driving ability evaluation model with a “punishment” and “affirmation” mechanism is proposed. The experimental comparative analysis shows that the evaluation algorithm proposed in this paper can take into account the driver's driving skill, driving state, and driving style in the real-time comprehensive driving ability evaluation, and draw differential evaluation conclusions based on the “punishment” and “affirmation” mechanism model to achieve a comprehensive and objective evaluation of the driver's driving ability. It can meet the needs of human-machine shared driving decisions for driver's driving ability evaluation.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 2","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49722858","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}

引用次数: 0

An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system 基于改进神经网络模型的能源运输系统电池智能电量估计

Green Energy and Intelligent Transportation Pub Date : 2023-04-01 DOI: 10.1016/j.geits.2023.100067

Bingzhe Fu , Wei Wang , Yihuan Li , Qiao Peng

{"title":"An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system","authors":"Bingzhe Fu , Wei Wang , Yihuan Li , Qiao Peng","doi":"10.1016/j.geits.2023.100067","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100067","url":null,"abstract":"<div><p>The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation. To achieve safe management and optimal control of batteries, the state of charge (SOC) is one of the important parameters. The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years. However, a common problem with these models is that their estimation performances are not always stable, which makes them difficult to use in practical applications. To address this problem, an optimized radial basis function neural network (RBF-NN) that combines the concepts of Golden Section Method (GSM) and Sparrow Search Algorithm (SSA) is proposed in this paper. Specifically, GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model, and its parameters such as radial base center, connection weights and so on are optimized by SSA, which greatly improve the performance of RBF-NN in SOC estimation. In the experiments, data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model, and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 2","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49722555","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}

引用次数: 8

Multiple learning neural network algorithm for parameter estimation of proton exchange membrane fuel cell models 质子交换膜燃料电池模型参数估计的多重学习神经网络算法

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100040

Yiying Zhang , Chao Huang , Hailong Huang , Jingda Wu

引用次数: 7

Interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication 基于多模型ETUKF的网联车辆V2V通信状态估计

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100044

Yan Wang, Zhongxu Hu, Shanhe Lou, Chen Lv

{"title":"Interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication","authors":"Yan Wang, Zhongxu Hu, Shanhe Lou, Chen Lv","doi":"10.1016/j.geits.2022.100044","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100044","url":null,"abstract":"<div><p>Accurate prediction of the motion state of the connected vehicles, especially the preceding vehicle (PV), would effectively improve the decision-making and path planning of intelligent vehicles. The evolution of vehicle-to-vehicle (V2V) communication technology makes it possible to exchange data between vehicles. However, since V2V communication has a transmission interval, which will result in the host vehicle not receiving information from the PV within the time interval. Furthermore, V2V communication is a time-triggered system that may occupy more communication bandwidth than required. On the other hand, traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving conditions. To address these issues, an event-triggered unscented Kalman filter (ETUKF) is first employed to estimate the PV state to strike a balance between estimation accuracy and communication cost. Then, an interactive multi-model (IMM) approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and applicability. Finally, simulation experiments under different driving conditions are implemented to verify the effectiveness of IMMETUKF. The test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84% and the proposed algorithm is highly adaptable to different driving conditions.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49721002","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}

引用次数: 2

The effect of electric vehicle energy storage on the transition to renewable energy 电动汽车储能对向可再生能源过渡的影响

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100042

Efstathios E. Michaelides , Viet N.D. Nguyen , Dimitrios N. Michaelides

{"title":"The effect of electric vehicle energy storage on the transition to renewable energy","authors":"Efstathios E. Michaelides , Viet N.D. Nguyen , Dimitrios N. Michaelides","doi":"10.1016/j.geits.2022.100042","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100042","url":null,"abstract":"<div><p>The most viable path to alleviate the Global Climate Change is the substitution of fossil fuel power plants for electricity generation with renewable energy units. This substitution requires the development of very large energy storage capacity, with the inherent thermodynamic irreversibility of the storage-recovery process. Currently, the world experiences a significant growth in the numbers of electric vehicles with large batteries. A fleet of electric vehicles is equivalent to an efficient storage capacity system to supplement the energy storage system of the electricity grid. Calculations based on the hourly demand-supply data of ERCOT, a very large electricity grid, show that a fleet of electric vehicles cannot provide all the needed capacity and the remaining capacity must be met by hydrogen. Even though the storage capacity of the batteries is close to 1–2% of the needed storage capacity of the grid, the superior round-trip storage efficiency of batteries reduces the energy dissipation associated with the storage and recovery processes by up to 38% and the total hydrogen storage capacity by up to 50%. The study also shows that anticipated improvements in the round-trip efficiencies of batteries are almost three times more effective than improvements in hydrogen storage systems.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49762075","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}

引用次数: 4

Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks 混合动力推进系统的能量管理:三维交通网络下飞行汽车的最新进展

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100061

Chao Yang , Zhexi Lu , Weida Wang , Ying Li , Yincong Chen , Bin Xu

{"title":"Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks","authors":"Chao Yang , Zhexi Lu , Weida Wang , Ying Li , Yincong Chen , Bin Xu","doi":"10.1016/j.geits.2022.100061","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100061","url":null,"abstract":"<div><p>The hybrid electric propulsion system (HEPS) holds clear potential to support the goal of sustainability in the automobile and aviation industry. As an important part of the three-dimensional transportation network, vehicles and aircraft using HEPSs have the advantages of high fuel economy, low emission, and low noise. To fulfill these advantages, the design of their energy management strategies (EMSs) is essential. This paper presents an in-depth review of EMSs for hybrid electric vehicles (HEVs) and hybrid electric aircraft. First, in view of the main challenges of current EMSs of HEVs, the referenced research is reviewed according to the solutions facing real-time implementation problems, variable driving conditions adaptability problems, and multi-objective optimization problems, respectively. Second, the existing research on the EMSs for hybrid electric aircraft is summarized according to the hybrid electric propulsion architectures. In addition, with the advance in propulsion technology and mechanical manufacturing in recent years, flying cars have gradually become a reality, further enriching the composition of the three-dimensional transportation network. And EMSs also play an essential role in the efficient operation of flying cars driven by HEPSs. Therefore, in the last part of this paper, the development status of flying cars and their future prospects are elaborated. By comprehensively summarizing the EMSs of HEPS for vehicles and aircraft, this review aims to provide guidance for the research on the EMSs for flying cars driven by HEPS and serve as the basis for knowledge transfer of relevant researchers.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49721004","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}

引用次数: 6

Forecasting the spatial and temporal charging demand of fully electrified urban private car transportation based on large-scale traffic simulation 基于大规模交通模拟的全电动城市私家车交通充电需求时空预测

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100039

Florian Straub , Otto Maier , Dietmar Göhlich , Yuan Zou

{"title":"Forecasting the spatial and temporal charging demand of fully electrified urban private car transportation based on large-scale traffic simulation","authors":"Florian Straub , Otto Maier , Dietmar Göhlich , Yuan Zou","doi":"10.1016/j.geits.2022.100039","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100039","url":null,"abstract":"<div><p>To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars, accurate models are needed to determine the charging energy and power demand of battery electric vehicles (BEVs) with high spatial and temporal resolution. Typically, e-mobility traffic simulations are used for this purpose. In particular, activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area. In addition to inaccuracies in determining the spatial distribution of BEV charging demand, one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area. However, these data are not available for most places in the world. Therefore, this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter. Instead, a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations. The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination, as well as the car-access attractiveness and the availability of parking spots at the destinations.</p><p>The applicability of this model is demonstrated for the urban area of Berlin, Germany, and its 448 sub-districts. For each district in Berlin, both the required daily BEV charging energy demand and the power demand are determined. In addition, the load shifting potential is investigated for an exemplary district. The results show that peak power demand can be reduced by up to 31.7% in comparison to uncontrolled charging.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49721003","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}

引用次数: 3

Composites for electric vehicles and automotive sector: A review 电动汽车和汽车领域的复合材料:综述

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100043

Adil Wazeer , Apurba Das , Chamil Abeykoon , Arijit Sinha , Amit Karmakar

{"title":"Composites for electric vehicles and automotive sector: A review","authors":"Adil Wazeer , Apurba Das , Chamil Abeykoon , Arijit Sinha , Amit Karmakar","doi":"10.1016/j.geits.2022.100043","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100043","url":null,"abstract":"<div><p>The automotive sector is undergoing a significant transformation to address critical challenges affecting consumers and the climate. One of the most difficult tasks is reducing the weight of vehicles in order to minimize energy consumption. A ten percent decrease in curb weight is predicted to result in a six to eight percent reduction in energy consumption. Composite materials having better strength to weight ratio are one of the finest options for planning, designing and manufacturing of the lightweight components. In automobile sector, employment of composite materials would reduce the weight of electric vehicles as well as influence their aerodynamic properties. Therefore, it would decrease the consumption of fuel as well by cutting down harmful emissions and particulate matter. Numerous developments in such technologies are studied over the last decade by automobile establishments and academic researchers. Fiber-reinforced polymers, particularly those established on glass and carbon fibers, have attracted attention of the automobile sector due to their high performance and lesser weight. This paper reviews the applications of various types of composite materials and the fabrication techniques of such composites in electric vehicles and automobiles. Furthermore, a comprehensive data breakdown of the lightweight materials statistics and figures on market analysis of high performance composite is presented. Finally, a discussion is made on the different applications of these composites. Hence, the details presented in this study should be useful for automobile companies to align with NET ZERO global mission while sustaining their businesses.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49721008","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}

引用次数: 10

Energy efficient route prediction for solar powered vehicles 太阳能汽车节能路线预测

Green Energy and Intelligent Transportation Pub Date : 2023-02-01 DOI: 10.1016/j.geits.2022.100063

Julie Gallagher, Siobhán Clarke

{"title":"Energy efficient route prediction for solar powered vehicles","authors":"Julie Gallagher, Siobhán Clarke","doi":"10.1016/j.geits.2022.100063","DOIUrl":"https://doi.org/10.1016/j.geits.2022.100063","url":null,"abstract":"<div><p>Solar powered vehicles are currently being developed towards entirely self-sustaining vehicles that harness their energy directly from the sun. For such vehicles, it is important to optimise their solar exposure while driving, thereby reducing their energy consumption through fossil fuels. Research has emerged to estimate optimised routes for solar vehicles, and this paper builds on this work to expand on the parameters used to calculate the route, thereby improving the energy-harnessing quality of the route together with its overall utility for the driver. The ArcGIS tool and the open weather API are used to predict the solar potential of a vehicle by taking into account shade based on surrounding topography, vehicle type, weather, distance and time of day. The model was implemented as a user mobile application ‘Drive Solar’ that calculates the optimal route for the user based on their preferences for time and energy efficiency. The effectiveness of the prediction model was tested using a solar irradiance sensor in Dublin city. The results show that the model predicts the route with the most energy absorbed with a 51.65% accuracy and chooses the route with the most energy consumed with a 86.65% accuracy. We conclude that Drive Solar can aid in the transition to widespread use of self-sustaining solar vehicles.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 1","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49721034","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}

引用次数: 1