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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: 11 September 2024 | Viewed by 1662

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
Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
Interests: optimization and machine learning in power systems
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

Published Papers (2 papers)

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Research

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
Viewed by 387
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
Viewed by 688
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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