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Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (11 September 2024) | Viewed by 12127

Special Issue Editors

Dr. Mingfei Ban

E-Mail Website
Guest Editor
Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Interests: optimal operation of integrated energy system; electric vehicles; renewable energy; smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongkai Yi

E-Mail Website
Guest Editor
School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: convex optimization; machine learning; virtual power plant
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Wang

E-Mail Website
Guest Editor
Department of Electrical Engineering, SEIEE 1-237, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China
Interests: optimal operation of power systems; power markets; renewable energy; flexible distribution networks; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With one-quarter of global energy-related greenhouse gas emissions originating from the transportation sector, both the capacity of renewable energy sources and electric vehicle sales are projected to experience significant growth as key strategies for curbing CO2 emissions, leading to rapid transformation of energy systems. While it is generally agreed that electrification, based on green energy, is crucial for the transportation sector’s green energy system transition, views vary on how to achieve this, including technological pathways and application details. Collecting a broader portfolio of recent academic and industrial solutions that enhance transportation electrification and maximize green energy benefits in interactions of electric transport with the power system is of great importance, and it will help bridge the gap between the transport, power, and energy sectors for decarbonizing mobility.

This Special Issue will cover promising, recent, and novel research trends in the optimization and control strategies of electric vehicles and green energy systems to help address potential difficulties and challenges in green-energy-based transportation electrification. Authors are encouraged to submit original research and review articles with theoretical, methodological, or practical focuses.

Topics of interest for publication include, but are not limited to:

  • Advanced optimal planning and operation methods for promoting green energy in transportation electrification
  • Impact of electric transport on the green-energy-based power systems
  • Analysis and discussions for transportation decarbonization pathways
  • Power-to-hydrogen-based electrification solutions for the transportation sector
  • Emission and environment impact of transportation electrification
  • Energy storage systems promoting green mobility
  • Vehicle-to-X and X-to-vehicle systems
  • Machine learning in power systems

Dr. Mingfei Ban
Dr. Zhongkai Yi
Dr. Xu Wang
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • transportation electrification
  • electric vehicles 
  • green energy system 
  • intelligent transportation 
  • advanced optimization strategy

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Published Papers (7 papers)

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Research

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16 pages, 1350 KiB  
Article
Economic Value Assessment of Vehicle-to-Home (V2H) Operation under Various Environmental Conditions
by Kwanghun Chung and Jong-Hyun Ryu
Energies 2024, 17(15), 3828; https://doi.org/10.3390/en17153828 - 2 Aug 2024
Cited by 2 | Viewed by 1664
Abstract
The rise of electric vehicles (EVs) has initiated a significant transformation in both the transportation and energy sectors. With the increasing adoption of EVs, their interaction with the power grid is becoming more critical. A notable and innovative concept emerging in this context [...] Read more.
The rise of electric vehicles (EVs) has initiated a significant transformation in both the transportation and energy sectors. With the increasing adoption of EVs, their interaction with the power grid is becoming more critical. A notable and innovative concept emerging in this context is Vehicle-to-Home (V2H) operations, which utilize the battery storage capabilities of EVs to meet residential energy demands. Our research provides a way of economically evaluating V2H operations under various environmental conditions including pricing, seasonal differences, and EV operations. The proposed model aids in understanding the optimal operation of V2H and identifying the factors that encourage its adoption. Furthermore, optimizing V2H use can promote renewable energy utilization, providing an additional solution to address its intermittent nature. The findings highlight the potential of V2H operations to contribute to more economically efficient energy systems, provided that supportive policies and adaptive technologies are in place. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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18 pages, 9259 KiB  
Article
Integrated Vehicle-Following Control for Four-Wheel Independent Drive Based on Regenerative Braking System Control Mechanism for Battery Electric Vehicle Conversion Driven by PMSM 30 kW
by Pataphiphat Techalimsakul and Wiwat Keyoonwong
Energies 2024, 17(11), 2576; https://doi.org/10.3390/en17112576 - 26 May 2024
Cited by 1 | Viewed by 1519
Abstract
This study proposed the hybrid energy storage paradigm (HESP) equipped with front-wheel permanent magnet synchronous motors (PMSMs) for battery electric vehicles (BEVs). In this case, all four wheels are driven by a single motor using mechanical coupling to distribute the motor’s power to [...] Read more.
This study proposed the hybrid energy storage paradigm (HESP) equipped with front-wheel permanent magnet synchronous motors (PMSMs) for battery electric vehicles (BEVs). In this case, all four wheels are driven by a single motor using mechanical coupling to distribute the motor’s power to each wheel evenly. The HESP is a combination of several supercapacitors (SCs) and an NMC-lithium battery equipped with an advanced artificial neural network (ANN) that will enhance the regenerative braking system (RBS) efficiency of energy storage during braking. The three-phase inverter switching algorithm ensures efficient regenerative braking and fine adjustment of the brake force distribution. Under the RBS, the HESP with the ANN first transfers braking energy to the SC and, when the safety standard is reached, the SC transfers it to the battery. The RBS control maintains an even distribution of braking force at all distances to ensure stability during braking. The results show that a traditional BEV can drive 245.46 km (35 cycles), while an EV with an RBS-only battery can drive 282.56 km (40 cycles). An EV with HESP-RBS can drive 338.78 km (48 cycles), which is an increase of 93.32 km (13 cycles). The HESP-RBS increased the regenerative efficiency by 38.01% when compared to a traditional BEV. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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20 pages, 2133 KiB  
Article
Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach
by Weiqi Pan, Xiaorong Yu, Zishan Guo, Tao Qian and Yang Li
Energies 2024, 17(11), 2491; https://doi.org/10.3390/en17112491 - 22 May 2024
Cited by 6 | Viewed by 1664
Abstract
The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to [...] Read more.
The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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14 pages, 2189 KiB  
Article
Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G
by Weiqi Pan, Bokang Zou, Fengtao Li, Yifu Luo, Qirui Chen, Yuanshi Zhang and Yang Li
Energies 2024, 17(11), 2448; https://doi.org/10.3390/en17112448 - 21 May 2024
Cited by 2 | Viewed by 1069
Abstract
With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared [...] Read more.
With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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22 pages, 7093 KiB  
Article
Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle
by Zhiwen Zhang, Jie Tang, Jiyuan Zhang and Tianci Zhang
Energies 2024, 17(6), 1359; https://doi.org/10.3390/en17061359 - 12 Mar 2024
Cited by 1 | Viewed by 1139
Abstract
In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the [...] Read more.
In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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19 pages, 5100 KiB  
Article
An Accurate Torque Control Strategy for Permanent Magnet Synchronous Motors Based on a Multi-Closed-Loop Regulation Design
by Feifan Ji, Qingyu Song, Yanjun Li and Ran Cao
Energies 2024, 17(1), 156; https://doi.org/10.3390/en17010156 - 27 Dec 2023
Cited by 1 | Viewed by 2249
Abstract
Torque control accuracy is a significant index of permanent magnet synchronous motors (PMSMs) and affects the safety of many applications greatly. Due to the strong nonlinearity of the motor as well as the disturbance of non-ideal factors such as temperature fluctuation and the [...] Read more.
Torque control accuracy is a significant index of permanent magnet synchronous motors (PMSMs) and affects the safety of many applications greatly. Due to the strong nonlinearity of the motor as well as the disturbance of non-ideal factors such as temperature fluctuation and the parameter error in field-oriented control (FOC), it is undoubtedly difficult to accurately control the actual output torque. Meanwhile, the parameter differences between motors and sensors during mass production and the assembly process affect the consistency of output torque and even increase the factory failure rate of the motor. No torque sensor is implemented due to the cost and limited space. Accurate estimation of the motor torque becomes essential to realize the closed-loop feedback for torque and improve the accuracy at a lower cost. In this paper, a look-up table (LUT) model that can reflect the nonlinear mapping relationship between power and torque is established based on numerous offline experiments, which avoids the calculation of complex losses. A multi-closed-loop control strategy is proposed to dynamically adjust the amplitude and angle of the preset current command, respectively, to improve the torque accuracy. The effectiveness of the strategy has been validated by experimental results. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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Review

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20 pages, 1662 KiB  
Review
Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review
by Jingzhe Hu, Xu Wang and Shengmin Tan
Energies 2024, 17(19), 4775; https://doi.org/10.3390/en17194775 - 24 Sep 2024
Cited by 2 | Viewed by 1714
Abstract
Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance [...] Read more.
Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem. Full article
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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