Pub Date : 2024-11-05DOI: 10.1109/TITS.2024.3480817
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Pub Date : 2024-11-05DOI: 10.1109/TITS.2024.3480168
Simona Sacone
Summary form only: Abstracts of articles presented in this issue of the publication.
仅为摘要形式：在本期刊物上发表的文章摘要。
{"title":"Scanning the Issue","authors":"Simona Sacone","doi":"10.1109/TITS.2024.3480168","DOIUrl":"https://doi.org/10.1109/TITS.2024.3480168","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15136-15190"},"PeriodicalIF":7.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TITS.2024.3473534
Dianfeng Zhang;Yanfeng Li;Yanlai Li
In-vehicle infotainment system (IVIS) plays an increasingly important role in vehicle intelligence. New energy vehicles have sufficient power, and their IVIS is developing in the direction of large size and multiple functions. By using online comments, thirteen attributes of IVIS were extracted through data clustering. Then, the attribute types and their corresponding contributions to the improvement of satisfaction and the reduction of dissatisfaction were determined by structured data transformation, correlation analysis, dual satisfaction analysis, and fine-grained satisfaction analysis using Kano model. Then, satisfaction and dissatisfaction influence indexes (IFIs) were calculated by combining attention, correlation coefficient and sentimental intensity. An improved cumulative prospect theory was then adopted to calculate the overall IFIs of different attributes. Sensitivity analyses of risk aversion and attention tendency were then conducted, and specific satisfaction optimization suggestions were then discussed. The results of the study have a guiding role in the optimization design of IVIS and the effectiveness of marketing promotion. The research methods contribute theoretically to improving the identification accuracy of online comments, coordinating the influence of sentimental tendency, integrating the analysis of attention’s influence and testing the effect of risk attitudes.
{"title":"Fine-Grained Satisfaction Analysis of In-Vehicle Infotainment Systems Using Improved Kano Model and Cumulative Prospect Theory","authors":"Dianfeng Zhang;Yanfeng Li;Yanlai Li","doi":"10.1109/TITS.2024.3473534","DOIUrl":"https://doi.org/10.1109/TITS.2024.3473534","url":null,"abstract":"In-vehicle infotainment system (IVIS) plays an increasingly important role in vehicle intelligence. New energy vehicles have sufficient power, and their IVIS is developing in the direction of large size and multiple functions. By using online comments, thirteen attributes of IVIS were extracted through data clustering. Then, the attribute types and their corresponding contributions to the improvement of satisfaction and the reduction of dissatisfaction were determined by structured data transformation, correlation analysis, dual satisfaction analysis, and fine-grained satisfaction analysis using Kano model. Then, satisfaction and dissatisfaction influence indexes (IFIs) were calculated by combining attention, correlation coefficient and sentimental intensity. An improved cumulative prospect theory was then adopted to calculate the overall IFIs of different attributes. Sensitivity analyses of risk aversion and attention tendency were then conducted, and specific satisfaction optimization suggestions were then discussed. The results of the study have a guiding role in the optimization design of IVIS and the effectiveness of marketing promotion. The research methods contribute theoretically to improving the identification accuracy of online comments, coordinating the influence of sentimental tendency, integrating the analysis of attention’s influence and testing the effect of risk attitudes.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15547-15561"},"PeriodicalIF":7.9,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1109/TITS.2024.3407760
Zihan Guo;Yan Wu;Lifang Wang;Junzhi Zhang
One typical application of connected and automated vehicles (CAVs) is to coordinate multiple CAVs at a non-signalized intersection in mixed traffic, and it may take advantage of multi-agent deep reinforcement learning (MDRL) approaches to improve the overall coordination efficiency. This study proposes a heuristic-based MDRL algorithm (H-QMIX) developed based on a value-based MDRL algorithm, QMIX. This algorithm incorporates a heuristic-based action mask module to guide CAVs efficiently and safely through intersections, composed of a stimulative passing sequence and safety restrictions on CAVs’ action space in the junction area. Compared with other MDRL algorithms (e.g., IPPO, QMIX), the H-QMIX algorithm demonstrates improved training performance in terms of safety and efficiency in two case studies, where the first requires all CAVs to affix their routes, and another allows CAVs to choose random routes. Concerning the model’s generalization ability, the trained models with the maximal episodic return are then transferred to a more practical scenario with a certain vehicle-to-vehicle (V2V) communication delay in a zero-shot manner. The simulation results illustrate that H-QMIX is robust to a certain communication delay. The code for this paper is available at: https://github.com/flammingRaven/heuristic_based_qmix .
{"title":"Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection","authors":"Zihan Guo;Yan Wu;Lifang Wang;Junzhi Zhang","doi":"10.1109/TITS.2024.3407760","DOIUrl":"https://doi.org/10.1109/TITS.2024.3407760","url":null,"abstract":"One typical application of connected and automated vehicles (CAVs) is to coordinate multiple CAVs at a non-signalized intersection in mixed traffic, and it may take advantage of multi-agent deep reinforcement learning (MDRL) approaches to improve the overall coordination efficiency. This study proposes a heuristic-based MDRL algorithm (H-QMIX) developed based on a value-based MDRL algorithm, QMIX. This algorithm incorporates a heuristic-based action mask module to guide CAVs efficiently and safely through intersections, composed of a stimulative passing sequence and safety restrictions on CAVs’ action space in the junction area. Compared with other MDRL algorithms (e.g., IPPO, QMIX), the H-QMIX algorithm demonstrates improved training performance in terms of safety and efficiency in two case studies, where the first requires all CAVs to affix their routes, and another allows CAVs to choose random routes. Concerning the model’s generalization ability, the trained models with the maximal episodic return are then transferred to a more practical scenario with a certain vehicle-to-vehicle (V2V) communication delay in a zero-shot manner. The simulation results illustrate that H-QMIX is robust to a certain communication delay. The code for this paper is available at: \u0000<uri>https://github.com/flammingRaven/heuristic_based_qmix</uri>\u0000.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16235-16248"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1109/TITS.2024.3419095
Sunghoon Hong;Daejin Park
Convolutional neural networks (CNNs) for powerful visual image analysis are gaining popularity in artificial intelligence. The main difference in CNNs compared to other artificial neural networks is that many convolutional layers are added, which improve the performance of visual image analysis by extracting the feature maps required for image classification. However, algorithm optimization is required to run applications that require low-latency in edge compute modules with limited processing resources. In this paper, we propose a novel algorithm optimization method for fast CNNs by using continuous differential images. The main idea is to reduce computation variably by using the differential value of the input in each convolutional layer. Also, the proposed method is compatible with all types of CNNs, and the performance is better when the pixel value difference of continuous images is low. We use the DarkNet framework to evaluate our algorithm using fast convolution and half convolution approaches on a clustered system. As a result, when the input frame rate is 10 fps, FLOPs are reduced by about 4.92 times compared to the original YOLOv7-tiny. By reducing the FLOPs of the convolutional layer, the inference speed increases to about 4.86 FPS, performing 1.57 times faster than the original YOLOv7-tiny. In the case of parallel processing that used two edge compute modules for using half convolution approach, FLOPs reduced more, and the response speed improved. In addition, faster Object detection implementation is possible by additionally expanding up to 7 compute modules in a scalable clustered embedded system as much as the user wants.
{"title":"Differential Image-Based Scalable YOLOv7-Tiny Implementation for Clustered Embedded Systems","authors":"Sunghoon Hong;Daejin Park","doi":"10.1109/TITS.2024.3419095","DOIUrl":"https://doi.org/10.1109/TITS.2024.3419095","url":null,"abstract":"Convolutional neural networks (CNNs) for powerful visual image analysis are gaining popularity in artificial intelligence. The main difference in CNNs compared to other artificial neural networks is that many convolutional layers are added, which improve the performance of visual image analysis by extracting the feature maps required for image classification. However, algorithm optimization is required to run applications that require low-latency in edge compute modules with limited processing resources. In this paper, we propose a novel algorithm optimization method for fast CNNs by using continuous differential images. The main idea is to reduce computation variably by using the differential value of the input in each convolutional layer. Also, the proposed method is compatible with all types of CNNs, and the performance is better when the pixel value difference of continuous images is low. We use the DarkNet framework to evaluate our algorithm using fast convolution and half convolution approaches on a clustered system. As a result, when the input frame rate is 10 fps, FLOPs are reduced by about 4.92 times compared to the original YOLOv7-tiny. By reducing the FLOPs of the convolutional layer, the inference speed increases to about 4.86 FPS, performing 1.57 times faster than the original YOLOv7-tiny. In the case of parallel processing that used two edge compute modules for using half convolution approach, FLOPs reduced more, and the response speed improved. In addition, faster Object detection implementation is possible by additionally expanding up to 7 compute modules in a scalable clustered embedded system as much as the user wants.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16036-16047"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.
{"title":"Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic","authors":"Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang","doi":"10.1109/TITS.2024.3443887","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443887","url":null,"abstract":"To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18899-18912"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.
三维物体检测因其在自动驾驶汽车（AV）中的巨大潜力而受到广泛关注。基于深度学习的物体检测器的成功依赖于大规模标注数据集的可用性，而编译这些数据集既耗时又昂贵，尤其是三维边界框标注。在这项工作中，我们研究了基于多样性的主动学习（AL），将其作为减轻注释负担的潜在解决方案。在注释预算有限的情况下，我们只自动选择信息量最大的帧和对象进行注释。从技术上讲，我们利用了视听数据集中提供的多模态信息，并提出了一种新颖的获取函数，可在所选样本中强制实现空间和时间多样性。在现实注释成本测量条件下，我们对所提出的方法与其他 AL 策略进行了基准测试，其中既考虑到了注释帧的现实成本，也考虑到了注释三维边界框的现实成本。我们在 nuScenes 数据集上证明了所提方法的有效性，并表明它明显优于现有的 AL 策略。
{"title":"Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving","authors":"Jinpeng Lin;Zhihao Liang;Shengheng Deng;Lile Cai;Tao Jiang;Tianrui Li;Kui Jia;Xun Xu","doi":"10.1109/TITS.2024.3463801","DOIUrl":"https://doi.org/10.1109/TITS.2024.3463801","url":null,"abstract":"3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15454-15466"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. The delayed effect in latest complete OD flow collection and complex spatiotemporal correlations of OD flows in high dimension make it challengeable to predict short-term OD flow. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the real-time mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%.
短期 OD 流量（即往返于车站之间的乘客数量）预测对于地铁系统的交通管理至关重要。最新完整 OD 流量收集的延迟效应和高维度 OD 流量的复杂时空相关性使得预测短期 OD 流量成为难题。由于没有充分利用实时乘客流动数据，也没有充分模拟车站间流动模式的隐性关联，现有方法亟待改进。本文提出了一种基于 Completion 的自适应异构图卷积时空预测器。其新颖性主要体现在两个方面。一是通过建立观测到的 OD 流量与高维预测目标 OD 流量之间的隐含相关性来模拟实时流动性演化，这种隐含相关性基于一个关键的数据驱动洞察：从一个车站出发的乘客的目的地分布与其他具有相似属性（如地理位置、区域功能）的车站相关。其次，通过考虑实时流动性演变和车站间的时间成本，估算未完成行程的目的地分布，从而完成最新的未完成 OD 流量，这是时间序列预测的基础，可以提高模型的动态适应性。在两个真实地铁数据集上的广泛实验证明了我们的模型优于其他竞争对手，模型性能的最大提升接近 4%。此外，我们提出的数据完整框架可以集成到其他模型中，使其性能提高达 2.1%。
{"title":"A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System","authors":"Jiexia Ye;Juanjuan Zhao;Furong Zheng;Chengzhong Xu","doi":"10.1109/TITS.2024.3467094","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467094","url":null,"abstract":"Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. The delayed effect in latest complete OD flow collection and complex spatiotemporal correlations of OD flows in high dimension make it challengeable to predict short-term OD flow. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the real-time mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15614-15627"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with $3.3~kW$ to $19~kW$ need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port $(N (+),O(-))$ is formed using the neutral of the EV motor winding $(N)$ and negative rail of the drivetrain inverter $(O)$ . Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.
{"title":"A Combinatory AC and DC Charging Approach for Electric Vehicles","authors":"Baktharahalli Shantaveerappa Umesh;Vinod Khadkikar;Hatem Zeineldin;Shakti Singh;Hadi Otrok;Rabeb Mizouni;Akshay Rathore","doi":"10.1109/TITS.2024.3464591","DOIUrl":"https://doi.org/10.1109/TITS.2024.3464591","url":null,"abstract":"Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with \u0000<inline-formula> <tex-math>$3.3~kW$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$19~kW$ </tex-math></inline-formula>\u0000 need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port \u0000<inline-formula> <tex-math>$(N (+),O(-))$ </tex-math></inline-formula>\u0000 is formed using the neutral of the EV motor winding \u0000<inline-formula> <tex-math>$(N)$ </tex-math></inline-formula>\u0000 and negative rail of the drivetrain inverter \u0000<inline-formula> <tex-math>$(O)$ </tex-math></inline-formula>\u0000. Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15467-15476"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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