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Electronics
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Advanced Energy Supply and Storage Systems for Electric Vehicles
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Advanced Energy Supply and Storage Systems for Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 20660

Special Issue Editors

Dr. Dmitry Baimel

E-Mail Website
Guest Editor
Electrical engineering department, Shamoon College of Engineering, Jabotinski St 84, Ashdod, Israel
Interests: power systems; renewable energy; power electronics; energy conversion
Special Issues, Collections and Topics in MDPI journals
Dr. Inna Katz

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Shamoon College of Engineering, Basel St., Be'er Sheva 8410802, Israel
Interests: electrodynamics processes in induction motor; analysis of a operation of frequency converter

Special Issue Information

Dear Colleagues,

In recent years, electric vehicles have become incredibly popular and widespread. Electric vehicles are based on evolving technology that is constantly being improved. Their key components are power sources and energy storage systems, and the features of these components directly influence the performance and driving distance of the vehicles.

Therefore, in recent years, academia and industry have invested a lot of money and resources in the research and development of more efficient, safe, reliable, fault-tolerant, and cheaper power sources and energy storage systems for electric vehicles. Another important aspect of research is the reduction in weight and dimensions of these systems.

The purpose of this Special Issue is to publish original theoretical and practical research ideas in the field of power supply and energy storage systems for electric vehicles. The topics include but are not limited to:

  • New energy storage systems for electric vehicles;
  • Battery and fuel cell storage systems for electric vehicles;
  • Energy management systems for electric vehicles;
  • Hybrid battery/ultra‐capacitor energy storage systems;
  • New topologies and control methods of inverters for electric vehicles;
  • Thermal management of battery systems;
  • Advanced charging systems for electric vehicles.

Dr. Dmitry Baimel
Dr. Inna Katz
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. Electronics 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 2400 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

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11 pages, 1292 KiB  
Article
Charging Dispatching Strategy for Islanded Microgrid Battery-Swapping Stations
by Zezhou Li, Wu Zhu and Guangdong Wang
Electronics 2024, 13(1), 49; https://doi.org/10.3390/electronics13010049 - 21 Dec 2023
Cited by 2 | Viewed by 1165
Abstract
To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China’s isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, [...] Read more.
To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China’s isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, to analyze battery-swapping station operation and isolated microgrid operation schedules. Considering the needs of potential future communities that might depend on electric vehicle battery-swapping stations, two scenarios were analyzed, and the most effective integer planning method was adopted. The international algorithm software program YALMIP + CPLEX was used to address the problem, and simulation results proved that the proposed model and its solution method could effectively affect the safe operation and scheduling of islanded microgrid battery-swapping stations and reduce the cost of islanded microgrid operation, with significant advantages. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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19 pages, 12196 KiB  
Article
Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures
by Chao Chen, Zhenhua Li and Jie Wei
Electronics 2023, 12(21), 4433; https://doi.org/10.3390/electronics12214433 - 27 Oct 2023
Cited by 2 | Viewed by 2012
Abstract
In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The [...] Read more.
In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘C to 25 ∘C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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16 pages, 3471 KiB  
Article
An Improved LSTNet Approach for State-of-Health Estimation of Automotive Lithium-Ion Battery
by Fan Ping, Xiaodong Miao, Hu Yu and Zhiwen Xun
Electronics 2023, 12(12), 2647; https://doi.org/10.3390/electronics12122647 - 13 Jun 2023
Cited by 5 | Viewed by 2050
Abstract
Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of [...] Read more.
Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model’s robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model’s feasibility and accuracy. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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19 pages, 6858 KiB  
Article
Study on the Systematic Design of a Passive Balancing Algorithm Applying Variable Voltage Deviation
by Heewook Song and Seongjun Lee
Electronics 2023, 12(12), 2587; https://doi.org/10.3390/electronics12122587 - 8 Jun 2023
Cited by 10 | Viewed by 4898
Abstract
A balancing circuit in a multi-series battery pack prevents a specific cell from being overcharged by reducing the voltage difference between the cells. Passive cell balancing is widely used for easy implementation and volume and size reduction. For optimal passive cell balancing, the [...] Read more.
A balancing circuit in a multi-series battery pack prevents a specific cell from being overcharged by reducing the voltage difference between the cells. Passive cell balancing is widely used for easy implementation and volume and size reduction. For optimal passive cell balancing, the charging/discharging current conditions and the state of charge (voltage condition) of the battery must be determined. In addition, the balancing algorithm must determine an allowable voltage deviation threshold between the cells connected in series to determine whether a specific cell performs a balancing operation. However, previous studies have not dealt with the design of balancing operating conditions in detail. In addition, the balancing time and efficiency improvement effect under specific conditions for arbitrary battery cells used in each previous study were mainly presented. Therefore, this study proposes a variable voltage deviation method in which the threshold for determining the voltage to be balanced is changed by reflecting the battery capacity, rated current specification, open-circuit voltage, and resistance of the balancing circuit. In addition, the voltage management performance and efficiency analysis results of the existing balancing algorithm and the proposed balancing method for the case where there is parameter deviation in the cells of the battery pack are also presented. The proposed method was verified through the simulation and experimental results of a reduced battery module in which three types of battery cells, INR 18650-30Q, INR 18650-29E, and INR 21700-50E, were arranged in 4-series. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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20 pages, 2213 KiB  
Article
Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack
by Hongbo Zhu, Hui Yin, Xue Feng, Xinxin Zhang and Zongyao Wang
Electronics 2023, 12(6), 1321; https://doi.org/10.3390/electronics12061321 - 10 Mar 2023
Cited by 4 | Viewed by 1841
Abstract
The initiative of users to participate in power grid operation is a key factor in realizing the optimal allocation of power. Demand response (DR) management mechanisms based on real-time pricing (RTP) can effectively promote the enthusiasm of users, stimulate the efficiency of microgrids [...] Read more.
The initiative of users to participate in power grid operation is a key factor in realizing the optimal allocation of power. Demand response (DR) management mechanisms based on real-time pricing (RTP) can effectively promote the enthusiasm of users, stimulate the efficiency of microgrids for power dispatch, and achieve the goasl of power peak shifting and valley filling. In this paper, we consider a microgrid composed of several energy providers (EPs) and multiple users, and each user is equipped with several electric vehicles (EVs). It should be noted that EVs may be attacked by networks in the process of data exchange when EVs connect to the MG. In this environment, we establish a multi-time slots social welfare maximization model that reflects the common interests of EPs and users. To simplify the problem, we decompose this multi-time slots model into a set of single-time slot optimization problems by the relaxation method. Furthermore, the mechanisms of identification and processing (MIP) for EVs under cyber-attack are proposed. The problem is decoupled to EPs and users by duality decomposition. Then, through integration with MIP, a distributed RTP algorithm based on the dual subgradient algorithm is designed to obtain the optimal electricity price. Finally, the simulation results verify the feasibility of the model and the effectiveness of the proposed algorithm. Through comparative analysis, the necessity of identifying EVs under cyber-attack is fully embodied. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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Review

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26 pages, 4028 KiB  
Review
Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles
by Seyed Mahdi Miraftabzadeh, Michela Longo, Andrea Di Martino, Alessandro Saldarini and Roberto Sebastiano Faranda
Electronics 2024, 13(10), 1973; https://doi.org/10.3390/electronics13101973 - 17 May 2024
Cited by 10 | Viewed by 4280
Abstract
The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical [...] Read more.
The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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27 pages, 2080 KiB  
Review
Status and Development of Research on Orderly Charging and Discharging of Electric Vehicles
by Zhaoyun Zhang and Linjun Lv
Electronics 2023, 12(9), 2041; https://doi.org/10.3390/electronics12092041 - 28 Apr 2023
Cited by 11 | Viewed by 3529
Abstract
As the scale of electric vehicles continues to expand, the charging load of electric vehicles into the network has become an issue that cannot be ignored. This paper introduces the concept and development of ordered charging based on the current background of ordered [...] Read more.
As the scale of electric vehicles continues to expand, the charging load of electric vehicles into the network has become an issue that cannot be ignored. This paper introduces the concept and development of ordered charging based on the current background of ordered charging research. The application architecture of ordered charging is summarized, and the advantages and disadvantages of centralized, distributed, and hierarchical control architectures are introduced. The current status of research on orderly charging is analyzed at four levels: steps and methods of load modeling for orderly charging, optimization objectives of orderly charging, optimization methods of orderly charging, and practical projects of orderly charging. The methods of load modeling for orderly charging are summarized, different optimization objectives of grid operation for orderly charging are introduced, and the advantages and disadvantages of different optimization algorithms are compared and analyzed. Practical projects on orderly charging illustrate the great potential of orderly charging. This paper points out four problems of communication, data security, market mechanism, and the number of charging stations that orderly charging is currently facing and proposes feasible solutions. The development prospect of orderly charging being more environmentally friendly, energy-efficient, intelligent, and safe is proposed. Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
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