A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abstract:With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

show abstract

“…(5) Upper and lower bound constraints that SOC should meet in the charging process of an electric bus [26]:…”

Section: Constraint Conditionmentioning

confidence: 99%

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As the AG deployment is conducted on a long time-line, the traffic pattern analysis results are macroscopic traffic data (e.g., traffic flow, density, speed, etc.). Although numerous travel pattern research works have been published in recent decades, there only exists a small portion of EV-related travel pattern works due to the lack of EV travelling data [18]. In this section, the EV-related travel pattern works are reviewed in terms of their modelling methodologies, as shown in Table III. 1) Data-Based Model: For the data-based model, the sources of data are essential as they are the basis of a realistic and precise travelling model.…”

Section: B Ev Travel Pattern Analysismentioning

confidence: 99%

“…[16] studies the charging impact on the system component level, while [17] investigates the impact broadly on the economy, environment, and system stability. Based on the EV charging impact study, [18] reviews the charging infrastructure deployment from the SG perspective. Both charging and discharging scheduling has been comprehensively reviewed from the viewpoints of methodology [12], [19]- [21] and algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

show abstract

“…Researches show that EV charging relies mainly on EV charging infrastructures (EVCIs) located at home or at work [8]. But such charging infrastructures are large in scale and geographically dispersed, which poses great challenges for the planning of charging infrastructures [9].…”

Section: Introductionmentioning

confidence: 99%

Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply

Guo

Yang

2018

Energies

View full text Add to dashboard Cite

Motivated by the development of electric vehicles (EVs), this paper addresses the planning of EV charging infrastructures (EVCIs). Considering that the optimal locations for EVCIs in most cities with a tight land supply are difficult to obtain, it is significant to study the planning of EVCIs in such cities. This paper proposes a planning method and model of EVCIs under the condition that alternative locations of centralized charging infrastructures (CCIs) are known. Firstly, based on the principle of energy equivalence, the sales volume of the fuel of gas stations in the planned area is converted into the equivalent electric quantity (EEQ) and the EEQ is divided according to the demands of different EVs for charging. Then, an equal product of the load and distance (EPLD) criterion is used to distribute the EEQ to CCIs and distributed charging infrastructures (DCIs) located in areas for load forecasting. The final plan is given out after checking the constraints and planning rationality. In addition, the net present value (NPV), the average charging distance, and the total harmonic distortion (THD) rate are used to evaluate the planning scheme. Finally, the feasibility and practicability of the proposed method are verified by a case study in Beijing.

show abstract

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Cited by 30 publications

References 82 publications

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Energy and Information Management of Electric Vehicular Network: A Survey

Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply

Contact Info

Product

Resources

About