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Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction

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A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14057

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Dr. Hui Yang

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Guest Editor

School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: new energy vehicles; wind power generation; robot servo and other applications; novel permanent magnet machines and drives; permanent magnet motors; dynamic modeling; design optimization theory; coordinated control of drive and magnetic modulation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qingshan Xu

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Guest Editor

Department of Electrical Engineering, Southeast University, Nanjing, China
Interests: renewable energy; power system control

Dr. Yifei Wang

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Guest Editor

School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: power system scheduling and operation; power system resilience and security analysis; electricity market and demand side management

Dr. Tao Chen

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Guest Editor

School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: electricity market; demand side management; data analytics; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Dr. Xiangjun Quan

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Guest Editor

1. School of Electrical Engineering, Southeast University, Nanjing 210018, China
2. Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210018, China
Interests: advanced power electronics control; grid synchronization; renewable energy integration and smart grids; grid-forming and lower-inertia system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Given the continued decarbonization of the global electricity supply, large-scale adoption of Electric Vehicles (EVs) is increasingly important, especially as they have been shown to reduce greenhouse gas emissions through more channels than previously expected. Next-generation EV technologies, including advanced powertrains, energy management, and grid interaction, represent the best smart solutions for transportation electrification and societal modernization in the 21^st century.

Improved EV-related technologies can maintain a clean, green environment and offer a reliable solution for air pollution and carbon emissions; furthermore, with the increase in EV-charging infrastructure, their popularity has experienced significant growth. In this new wave of EV technology development, numerous new methods and tools have emerged, increasing EV adoption in our society and thus the implementation of intelligent driving, routing, energy management, grid-connected operation, etc. In addition, emerging interdisciplinary techniques, e.g., big data and AI techniques, are widely used to address EV powertrain control and battery energy management under variable road conditions and during unmanned vehicle driving. It is believed that these novel technologies will further enhance driving performance and EV–grid interaction, promoting low-carbon smart cities and 100% transportation electrification.

This Special Issue will present the latest developments in advanced EV powertrains, EV energy management, and EV–grid interaction. Authors are invited to submit original contributions considering, but not limited to, the following topics of interest:

Keywords:

Hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), electric vehicles (EVs);
Range-extended EVs and unmanned EVs with low-carbon features;
Highly efficient EV powertrains and power electronics;
Advanced electric machines, motor drives, and propulsion architectures for EVs;
Green charging stations, on-board and off-board chargers;
EV fast chargers, opportunity chargers, and wireless charging;
Vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), and other V2X technology;
Low-carbon EV–grid interaction and grid interface technologies;
Power and transport nexus and utility-scale EV operation;
V2G communication, vehicle connectivity, EV fleet, and EV routing;
Low-carbon EV battery manufacture and pollution reduction;
Energy storage systems and battery management systems (BMS);
Heavy-duty electric vehicles and special-type electric vehicles.

Dr. Hui Yang
Prof. Dr. Qingshan Xu
Dr. Yifei Wang
Dr. Tao Chen
Dr. Xiangjun Quan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

23 pages, 6016 KiB

Open AccessArticle

Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network

by Haiyun An, Qian Zhou, Yongyong Jia, Zhe Chen, Bingcheng Cen, Tong Zhu, Huiyun Li and Yifei Wang

World Electr. Veh. J. 2024, 15(1), 24; https://doi.org/10.3390/wevj15010024 - 10 Jan 2024

Viewed by 1086

Abstract

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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25 pages, 10683 KiB

Open AccessEditor’s ChoiceArticle

VSG Control for Cascaded Three-Phase Bridge Based Battery Inverter

by Xiaojing Qi and Jianyong Zheng

World Electr. Veh. J. 2023, 14(8), 203; https://doi.org/10.3390/wevj14080203 - 30 Jul 2023

Viewed by 1208

Abstract

With the increasing number of new energy sources connected to the grid, the unbalanced output of three-phase grid-connected inverters and the lack of no inertia and damping characteristics in the traditional microgrid control system will seriously affect the stability of voltage, frequency, and power angle for microgrids. This paper proposes a novel cascaded three-phase bridge inverter topology for the battery system used for the electric vehicle. Compared with traditional cascaded H-bridge inverters, the proposed multilevel inverter can achieve self-adaptive balance for three phases. The mathematical model of a cascaded three-phase bridge inverter is established in this paper. Based on the voltage and current equations of a multilevel inverter, a new modulation strategy named carrier phase-shifted-distributed pulse width modulation (CPSD-PWM) was developed, which is more suitable for cascaded three-phase bridge inverters. The harmonic analytic equations of carrier phase-shifted pulse width modulation (CPS-PWM) and CPSD-PWM are constructed by the double Fourier analysis method. Compared with the traditional PWM modulation strategy, the CPSD-PWM can reduce the output harmonics and improve the balance of the three-phase output, which can realize the three-phase adaptive balance in the cascaded three-phase bridge inverter. This paper develops a cascaded three-phase bridge multilevel power converter system based on the virtual synchronous generator (VSG) control strategy. The voltage and frequency of inverter output can be accurately controlled in both island mode and grid-connected mode through active power-frequency regulation and reactive power–voltage regulation, and the stability of primary frequency regulation for the multilevel microgrid inverter can be improved by collaborative optimization of virtual inertia and virtual damping. The CPSD-PWM modulation strategy and VSG control strategy are verified by the simulation results and experimental data for the cascaded three-phase bridge inverter. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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17 pages, 2847 KiB

Open AccessArticle

Pricing Strategy for a Virtual Power Plant Operator with Electric Vehicle Users Based on the Stackelberg Game

by Qiang Liu, Jiale Tian, Ke Zhang and Qingxin Yan

World Electr. Veh. J. 2023, 14(3), 72; https://doi.org/10.3390/wevj14030072 - 14 Mar 2023

Cited by 1 | Viewed by 1704

Abstract

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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15 pages, 5082 KiB

Open AccessArticle

A Game-Theoretic Approach to Solve Competition between Multi-Type Electric Vehicle Charging and Parking Facilities

by Meihui Jiang, Tao Chen, Ciwei Gao, Rui Ma, Wencong Su and Abdollah Kavousi-Fard

World Electr. Veh. J. 2023, 14(3), 59; https://doi.org/10.3390/wevj14030059 - 27 Feb 2023

Cited by 1 | Viewed by 2077

Abstract

This paper investigates the competition problem between electric vehicle charging and parking desks for different owners using a non-cooperative Bertrand game. There is growing attention on electric vehicles from both policy makers and the public charging service provider, as well as the electric vehicle owners. The interaction between different entities forms a competition (game), especially between multi-type electric vehicle charging and parking facilities. Most of the existing studies on charging platforms are about the optimization of the charging platform scheduling strategy or the game relationship between charging platforms and EV users, but there is a lack of exploration on the revenue game between charging platforms. In this paper, the competitive interactions between different charging decks are studied and analyzed using a general game-theoretic framework, specifically the Nikaido–Isoda solution. In the pricing competition model, the pricing strategies of all players and physical constraints, such as distribution line capacity, are taken into consideration. Through the case studies, it is clearly indicated that the game played between different electric vehicle charging/parking decks will always converge to a Nash equilibrium point. Both charging service providers and customers could benefit from such an open and fully competitive energy service ecosystem, which enhances the overall social welfare. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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13 pages, 3502 KiB

Open AccessArticle

A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural Network

by Yanhe Yin, Baojun Xu, Yi Zhong, Tao Bao and Pengyu Wang

World Electr. Veh. J. 2023, 14(2), 51; https://doi.org/10.3390/wevj14020051 - 10 Feb 2023

Cited by 1 | Viewed by 1660

Abstract

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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23 pages, 4013 KiB

Open AccessArticle

Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles

by Taoyong Li, Jinjin An, Dongmei Zhang, Xiaohong Diao, Changliang Liu and Weiliang Liu

World Electr. Veh. J. 2022, 13(12), 235; https://doi.org/10.3390/wevj13120235 - 06 Dec 2022

Cited by 1 | Viewed by 1564

Abstract

The operation process of a virtual power plant is affected by many uncertainties, and how to ensure its comprehensive operation quality is a pressing challenge. For the virtual power plant incorporating electric vehicles, the interval number is used to describe the stochastic fluctuation of system uncertainties, and the optimization objectives are to (1) improve the operating economy, environmental protection, and grid load smoothing, (2) build a multi-objective interval optimal dispatching model considering the constraints of power balance and equipment operating characteristics, (3) solve the Pareto solution set by adopting the improved NSGA-II algorithm incorporating extreme scenario analysis, and (4) determine the optimal dispatching solution by the hierarchical analysis method. The median values of the determined optimal target intervals are 6456.11 yuan, 9860.01 kg, and 2402.56 kW. The algorithm shows that the proposed optimal dispatching strategy can effectively improve the economy of the virtual power plant and ensure that environmental protection and grid load smoothing requirements are met. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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21 pages, 4218 KiB

Open AccessArticle

State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm

by Lihong Xiang, Li Cai, Nina Dai, Le Gao, Guoping Lei, Junting Li and Ming Deng

World Electr. Veh. J. 2022, 13(11), 220; https://doi.org/10.3390/wevj13110220 - 21 Nov 2022

Cited by 3 | Viewed by 1736

Abstract

An improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC curve is obtained using a 7-segment linear fitting method before the algorithms estimate the SOC. In addition, by combining this improved method with a third-order RC equivalent circuit model in the dynamic stress test (DST) case the convergence time is reduced by 0.15 s compared to the second-order RC equivalent circuit model. Following that, four different types of comparison experiments are carried out by comparing the improved algorithm to EKF and other SHEKF algorithms.The estimation accuracy under DST conditions of 0 °C, 25 °C and 45 °C is approximately 0.5%, 2.2% and 1.3% improvement compared to the EKF algorithm. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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19 pages, 4401 KiB

Open AccessArticle

Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on K-Means Clustering Optimization Deep Learning

by Shixiang Lu, Xiaofeng Feng, Guoying Lin, Jiarui Wang and Qingshan Xu

World Electr. Veh. J. 2022, 13(11), 198; https://doi.org/10.3390/wevj13110198 - 25 Oct 2022

Cited by 2 | Viewed by 1586

Abstract

Electric vehicles have the advantages of zero emissions and high energy efficiency. They have a broad potential in today’s social life, especially in China where they have been widely used. In the current situation, whereby the storage capacity of electric vehicles is continually increasing and the requirements for grid stability are getting higher and higher, V2G technology emerges to keep up with the times. Since the electric vehicle charging station is a large-scale electric vehicle cluster charging terminal, it is necessary to pay attention to the status and controllability of each charging pile. In view of the lack of attention to the actual operation of the electric vehicle charging station in the existing vehicle–network interaction mode, the charging state of the current electric vehicle charging station is fixed. In this paper, deep learning is used to establish a load perception model for electric vehicle charging stations, and K-means clustering is used to optimize the load perception model to realize random load perception and non-intrusive load monitoring stations for electric vehicle charging. The calculation example results show that the proposed method has good performance in the load perception and controllability evaluation of electric vehicle charging stations, and it provides a feasible solution for the practical realization of electric vehicle auxiliary response. Full article

(This article belongs to the Special Issue Novel Electric Vehicle Technology towards Low Carbon Future: Advanced Powertrain, Energy Management and Grid Interaction)

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