A Survey on Electric Buses—Energy Storage, Power Management, and Charging Scheduling R. Deng, Y. Liu, W. Chen, and H. Liang In recent years, aiming to reduce the metropolitan air pollution caused by fossil fuel-powered vehicles, the electrification of transportation, such as electric vehicles (EVs) and electric buses (EBs), has attracted great attention from the automobile industry, academia, and public

As a significant part of the next-generation smart grid, electric vehicles (EVs) are essential for most countries to achieve energy independence, secure energy supply, and alleviate the pressure on environmental protection and energy security. Although EVs have grown rapidly, the slow recharge time is still the biggest obstacle to a wider application. While gasoline vehicles can pump enough gasoline

For significant characteristics such as high efficiency and environmental protection, electric vehicles (EVs) have become a new technology trend in the intelligent transportation system (ITS). Data like real time traffic situation and charge point occupation in ITS shows significant real-time characteristics. Distributed storage system, an effective technology to ensure reliable sharing of dynamic

With the development of extreme fast charging technology, charging stations need to use energy storage stations to reduce the rising peak to average power ratio (PAPR). Lithium-ion capacitor (LIC) is a chemical power source that uses both Faraday process and non-Faraday process to store energy. Because of its attractive performance in terms of rate characteristics and chemical stability, it is suitable

Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2 nd order RC equivalent

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In 2015, the Board of Governors of IEEE Intelligent Transportation Systems Society had approved the proposal to name the Best Paper Award in IEEE Transactions on Intelligent Transportation Systems as the George N. Saridis Best Transactions Paper Award. After nearly five years of preparation and planning, and almost one year of hard and concentrated effort by the Award Committee, we are pleased to announce

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

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Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A Survey

Electric vehicles can place a significant load on the power grid due to their unscheduled charging events. One way of improving power grid stability is to schedule electric vehicle charging in advance. Before a charging visit, the electric vehicle provides necessary information to request for charging at a charging station, which prepares and reserves the energy before the visit. However, the reported

In [1] , the traffic volume of the left-turn movement was set to be extremely high (with significant queue spill back from the left-turn bay) in the microscopic traffic simulator. Although some explanation has been provided in the paper, we consider the simulation results to be non-representative. Therefore, we reran the simulation with more balanced values across different movements, and updated the

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Electric vehicles (EVs) are becoming increasingly popular, but the frequent charging and large charging latency remain major obstacles to the EV industry. This article focuses on the charging scheduling of on-the-move EVs in a transportation network to minimize EVs’ charging latency, including driving time to charging stations (CSs), wait time and charging time. We formulate this charging scheduling

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A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries

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The mechanism revelation of performance decrease and fast-charging limitation of lithium-ion batteries at low temperatures is indispensable to optimize battery design and develop fast-charging methods. In this article, an electrochemical model-based quantitative analysis method is proposed to uncover the dominant reason for performance decrease and fast-charging limitation of batteries at low temperatures

Adopting extreme fast charging for electric vehicles will significantly reduce the charging time for electric vehicle owners, which will improve the public acceptance of electric vehicle. However, under the conditions of wide spread fast charging stations, large charging power of fast charging stations will bring nonnegligible impacts to the power system. For an aggregator that owns multiple fast charging

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Linear Seam Elimination of Tunnel Crack Images Based on Statistical Specific Pixels Ratio and Adaptive Fragmented Segmentation

From November 4th to 7th, 2018, the IEEE Intelligent Transportation Systems Society (ITSS) sponsored the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), one of the most prestigious academic meetings on Intelligent Transportation Systems. The 2018 annual flagship conference of the IEEE ITS Society was held in Maui, Hawaii, reaching unprecedented numbers in terms

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Safe and reliable intelligent charging stations are imperative in an intelligent transportation infrastructure. Over the past few years, a big number of smart charging stations have been deployed, and most of them are online and connected, resulting in potential risks of threats. Although there exists related work on securing intelligent vehicles, very little work focused on the security of charging

The extreme fast charging (XFC) technology helps to reduce refueling time, alleviate mile anxiety, extend driving range and finally promote the popularity of electric vehicles (EVs). However, it would also pose great challenges on the power grid infrastructure especially distribution networks, due to the large-scale and intermittent power demand. This paper proposes a coordinated planning method for

Acting as a key to future environmentally friendly transportation systems, electric vehicles (EVs) have attached importance to develop fast charging technologies to accomplish the requirement of vehicle users. However, fast charging behaviors would cause degradations in EVs’ batteries, as well as negative effects like new demand peak and feeder overloads to the connected distribution network, especially

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Is It Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

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With the development of battery technology, large-scale battery applications are increasing. In order to obtain a higher current and voltage level and improve the overall energy efficiency, batteries are connected in series and parallel. Bulk model is the most used model to simulate battery packs, and the simulation results of single cell are enlarged several times to represent a battery pack. But

This paper presents a variable-current charging strategy of Li-ion batteries. Since the battery characteristics vary with state of charge ( SOC ), it is more reasonable to divide the charging process based on SOC than on cut off voltage. We find an optimal charging pattern of the proposed strategy by Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The Second-order Thevenin model of battery

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Traditional industry is experiencing a worldwide evolution with Industry 4.0. Wireless sensor networks (WSNs) have a main role in this evolution as an essential part of data acquisition and the way in which WSNs are powered is one of the main challenges to face, if the industry wants to achieve digital transformation. Energy harvesting technologies are one of the possible solutions to this challenge

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Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process. In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging. Therefore, building an effective charging planning system has

Plug-in Electric Vehicles (PEV) are characterized as a type of unconventional electric load, and their Fast Charging Stations (FCS) are capital-intensive transportation service infrastructure. Therefore, FCS location planning must consider the requirements of electricity and transportation infrastructures simultaneously. This paper, for the first time, proposes a graph-computing based integrated location

This paper addresses the problem of operator-based vehicle repositioning in free-floating electric car-sharing systems (FFCSs). New algorithmic techniques are developed which derive cost-effective relocation tours by primarily minimizing relocation drivers’ walking time. Car repositioning takes place overnight and aims at complete rebalancing of the system i.e., at achieving an optimal, based on user

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Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

Cloud Computing, a catchy metaphor for utility computing, implemented through the provisioning of various types of hosted services over the Internet, has seen phenomenal growth and quasi-universal adoption in the past two decades. The underlying business model of cloud computing is the familiar “pay-as-you-go” model of metered services, where a user pays for whatever he/she uses and no more, and where

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Electric Vehicle (EV) public charging is important to meet the exploding charging demand and to address the range anxiety issue. In this paper, we focus on the EV public charging market with heterogeneous charging stations (CSs) under the time-based billing model. We jointly consider the charging time optimization for EVs, the EV-CS pairing, and the pricing mechanism for CSs. A hierarchical game, which

This paper investigates the dynamic system optimum (DSO) problem with simultaneous route and departure time assignments for a general traffic network partitioned into multiple regions. Regional traffic congestion is modeled with a well-defined macroscopic fundamental diagram (MFD) mapping the trip completion rate to the vehicular accumulation. To overcome the limitation of inconsistent flow propagation

Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this paper, we demonstrate that the function of a city influences the utility of social media data in travel demand models by generating models for eight US cities with different functions. Data from Twitter and Foursquare

Computation offloading for vehicular edge computing (VEC) architecture has gained increasing attention, with the emergence of mobile and vehicular applications with high-computing and low-latency demands, such as Intelligent Transportation Systems and IoT-based applications. However, existing challenges need to be addressed for VEC’s resources to be used in an efficient manner. The fundamental challenges

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Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey

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As a wheel traction motor used for electric vehicles (EVs), due to the requirement of high power/torque density and the poor working condition, it is more prone to cause severe inter-turn short circuit (ITSC) faults. In this paper, the ITSC faults characteristics of a permanent magnet synchronous machine (PMSM) used for wheel traction motors in EVs are deeply and comprehensively researched. The influence

Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done

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