A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification

Wardt, Thomas J. T. Van der; Farid, Amro M.

doi:10.3390/en10050653

Cited by 22 publications

(5 citation statements)

References 105 publications

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“…Therefore, advanced control strategies for charging EVs such as coordinated charging [80,81], vehicle-to-grid stabilization [79,[82][83][84][85][86], and charging queue management [87,88] have been proposed to stabilize electric vehicles' charging schedules. These works have determined that a holistic approach to studying electric vehicles is necessary given the coupling with the electricity sector [31,[89][90][91]. Electrified transportation is discussed further in Sect.…”

Section: The Emergence Of Electrified Transportationmentioning

confidence: 99%

“…These loads may be further exacerbated temporally by similar charging patterns driven by similar work and travel lifestyles or geographically by the relative sparsity of charging infrastructure in high-demand areas [380]. This transportationelectricity nexus (TEN) [31,[89][90][91]382] requires new assessment models whose scope includes the functionality of both systems. Recent works have also proposed axiomatic design as a means to model large systems such as the transportation and manufacturing systems [383][384][385][386][387].…”

Section: Connected Automated and Electrified Multi-modal Transportationmentioning

confidence: 99%

“…A third study focused on the differences between conventional plug-in and online (wireless) EVs [31]. More recently, a performance assessment methodology for multi-modal electrified transportation has been developed that integrates the methodologies of previous studies [91]. An older review compares a variety of open source transportation modeling tools [392].…”

Section: Connected Automated and Electrified Multi-modal Transportationmentioning

confidence: 99%

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Muhanji¹,

Flint²,

Farid³

2019

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adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

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Section: The Emergence Of Electrified Transportationmentioning

confidence: 99%

Section: Connected Automated and Electrified Multi-modal Transportationmentioning

confidence: 99%

Section: Connected Automated and Electrified Multi-modal Transportationmentioning

confidence: 99%

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eIoT

Muhanji¹,

Flint²,

Farid³

2019

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“…The effects of alternative vehicle technologies have been continuously evaluated in scientific literature. The research focus has been on passenger vehicles [11,12] or transportation in general [13,14]. The investment in hybrid vehicle development was investigated by real option reasoning in [11].…”

Section: Technology Assessment Of Alternative Powertrainsmentioning

confidence: 99%

Overview of Powertrain Electrification and Future Scenarios for Non-Road Mobile Machinery

et al. 2018

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Powertrain electrification continues to be a growing trend in vehicular applications. Electric powertrains have numerous advantages over traditional mechanical and hydraulic powertrains but there are still important challenges to overcome for long-term commercial success. This research presents a technological assessment of present and future developments of powertrain electrification in non-road mobile machinery (NRMM). The challenges and opportunities of NRMM electrification are described in detail. The trends and drivers related to technological development such as regulations, policies and market development are analyzed, and technology enablers are highlighted. Future scenarios are formulated based on the prevailing trends and drivers, development of key components, scientific literature and status of the non-road mobile machinery industry. Some recommendations are also given in relation to the development of hybrid and electric powertrains for NRMM. The key findings of this research indicate that the electrification of NRMM is slowly started and the progress is demonstrated by hybridization of some specific, successful mobile machines. In short-term, high component and technology development costs remain the main barrier for higher adoption of electric and hybrid powertrains. In the long-term scenario, many NRMM can operate autonomously and powertrain electrification has become mainstream technology.

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“…Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior of EVs, established a dynamic EV charging demand prediction model and evaluated the congestion degree on the distribution network and traffic network caused by large-scale aggregation charging. In addition, researchers [18,19] adopted a microscopic traffic model to depict the dynamic characteristics and electrical state of EVs in the joint simulation system of transportation electrification, and they predicted the evolution trend of vehicle traffic demand and charging demand. Further, traffic nodes and electricity nodes in heterogeneous physical space were mapped to networks in studies [20,21].…”

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confidence: 99%

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

et al. 2020

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Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the 'Electric Vehicles-Power Grid-Traffic Network' fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners' charging behavior.Energies 2020, 13, 1412 2 of 32 331,000 public charging piles and 477,000 private charging piles in 2018, an increase of 74.2% over the same period in 2017 [6].However, the driving and charging behaviors of numerous EVs in urban internal networks are bound to interact with the energy and information generated by the traffic network and power grid [7,8]. The residents' trip rule, urban road network structure and charging facility distribution affect the vehicle driving distribution and charging selection, whereas the vehicle battery parameters, driving path plan and human decision-making behavior also affect the degree of traffic network obstruction and the power grid operation state [9][10][11][12]. Therefore, accurate prediction of EV charging demand and reasonable fast-charging station (FCS) recommendations are the premise of realizing compatibility between EVs and the power grid along with the transportation network.At present, various studies have developed EV charging demand models from the aspect of cooperation between EVs, the transportation system and power system all together. Hence, in [13,14], an origin destination (OD) matrix analysis method was utilized to track the all-weather driving trajectory of EVs and to predict the charging load distribution of the regional electricity grid as well as the flow status of road networks through vehicle traffic trip demands. Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior o...

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A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification

Cited by 22 publications

References 105 publications

eIoT

eIoT

Overview of Powertrain Electrification and Future Scenarios for Non-Road Mobile Machinery

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

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