Advanced Modeling, Control and Emerging Application of Energy Storage Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (10 January 2023) | Viewed by 5773

Special Issue Editors

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: advanced energy storage systems; big data mining and analysis; power system integration and intelligent control technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: design and safety technology research of power battery system/fuel cell system of new energy vehicles
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: automotive dynamics; hybrid electric vehicle NVH configuration design energy management; intelligent vehicle environment-aware path planning and decision-making; the transmission control
Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: battery characteristic modeling; fault diagnosis; states estimation; thermal management; energy equilibrium
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy storage systems (ESSs) are essential for achieving transportation electrification and intelligence and are considered fundamental assets integrated into modern power systems. Unleashing and leveraging the full potential of these energy storage systems requires advanced modeling and control techniques, along with the meticulously sophisticated integration of multiple disciplines, such as material, engineering, control theory, and environmental sciences. Emerging applications of energy storage systems with various entities can accelerate the paradigm shift towards sustainable transport and power systems. High electrification and intelligent networking can be rich information sources for safety, reliability, operation and maintenance efficiency, and an essential link between the energy storage industry and other industries.

This Special Issue aims to provide timely solutions for emerging scientific and technical challenges in energy storage (e.g., batteries, supercapacitors, hydrogen, and hybrid systems) for intelligent transportation systems and smart grids. Original, high-quality technical papers, as well as state-of-the-art survey papers and tutorials, are invited for submission.

Topics of interest include but are not limited to:

  • Advanced models for batteries, supercapacitors, and fuel cells, etc.;
  • Energy storage systems integration and performance optimization;
  • Multi-physics field modeling, simulation, and experiments of ESSs;
  • Online parameter identification and joint state estimation of ESSs;
  • Lifetime prediction/analysis and fault diagnosis of HESSs;
  • Innovative data analysis and energy management strategies of ESSs;
  • Advanced battery safety and thermal runaway warning technology;
  • The application of artificial intelligence and edge computing technology;
  • Innovative energy-saving and emission reduction technology;
  • AI-enabled decision making and operation and maintenance.

Technical Program Committee Member:

Doctor Song Hu  University of Science and Technology Beijing

Dr. Jichao Hong
Prof. Dr. Xiaoming Xu
Dr. Xiaolin Tang
Dr. Jiale Xie
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • energy storage systems
  • system modelling
  • performance optimization
  • fault diagnosis
  • energy management strategies
  • artificial intelligence
  • operation & maintenance

Published Papers (3 papers)

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Research

15 pages, 5850 KiB  
Article
Operation Method of PV–Battery Hybrid Systems for Peak Shaving and Estimation of PV Generation
by Kun-Yik Jo and Seok-Il Go
Electronics 2023, 12(7), 1608; https://doi.org/10.3390/electronics12071608 - 29 Mar 2023
Cited by 3 | Viewed by 1168
Abstract
Photovoltaic (PV)–battery hybrid systems, which are composed of PV arrays, batteries, and bidirectional inverters, can level the loads of traditional utility grids. Their objective is to supply predetermined active and reactive power to the power grid. This paper presents an operation method for [...] Read more.
Photovoltaic (PV)–battery hybrid systems, which are composed of PV arrays, batteries, and bidirectional inverters, can level the loads of traditional utility grids. Their objective is to supply predetermined active and reactive power to the power grid. This paper presents an operation method for PV–battery hybrid systems by estimating PV generation. Using the PV installation information, the maximum PV generation on a clear day was predicted and compared with historical data. The PV generation was estimated using historical data from 2007 to 2010. The method aims to reduce the peak load of the power system using the estimated load and PV generation of the next day. With the given weather information and load pattern for the next day, the charge and discharge set points of the battery can be determined by considering the initial SoC (State of Charge) and capacity of the battery. To compensate for the estimation error of the load and PV output, an operational margin was considered. This method can maximize system operation efficiency by fully utilizing the battery. The effectiveness of the operation method was validated through simulation studies. It was confirmed that the peak load could be reduced by 30% using the proposed algorithm. Full article
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17 pages, 6824 KiB  
Article
The Structure Principle and Dynamic Characteristics of Mechanical-Electric-Hydraulic Dynamic Coupling Drive System and Its Application in Electric Vehicle
by Yue Sun, Hongxin Zhang and Jian Yang
Electronics 2022, 11(10), 1601; https://doi.org/10.3390/electronics11101601 - 18 May 2022
Cited by 2 | Viewed by 1930
Abstract
To solve the problem of the low recovery rate of braking energy and the short driving range of electric vehicles, a novel mechanical-electric-hydraulic dynamic coupling drive system (MEH-DCDS) is proposed in this article. MEH-DCDS is a new power integration device that allows electric, [...] Read more.
To solve the problem of the low recovery rate of braking energy and the short driving range of electric vehicles, a novel mechanical-electric-hydraulic dynamic coupling drive system (MEH-DCDS) is proposed in this article. MEH-DCDS is a new power integration device that allows electric, mechanical, and hydraulic energy to be converted mutually. It comprises a swash plate plunger pump/motor and a permanent magnet synchronous motor. This article explains the structure and working principles of MEH-DCDS. We describe the dynamic characteristics of MEH-DCDS and analyze the pump and hydraulic motor in the MEH-DCDS hydraulic module. The simulation results show that the flow variation of the MEH-DCDS hydraulic module accords with the design concept of MEH-DCDS, and the pressure variation of high and low pressure accumulators also accords with the theoretical situation. The energy flow of Mechanical-Electric-Hydraulic Power Coupling Electric Vehicle (MEHPC-EV) under different working modes is expounded, and the mathematical model of its key components is established. Based on AMESim and Simulink, the article establishes a vehicle simulation dynamic model. The dynamic performance of MEHPC-EV in UDDS is analyzed by co-simulation. The simulation results show that the application of MEH-DCDS in electric vehicles is feasible. MEHPC-EV reduced battery energy consumption by 26.18% compared to EV. The research in this paper verifies the accuracy and superiority of the system, which has a significant reference value for the development and study of electric vehicles in the future. Full article
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16 pages, 8253 KiB  
Article
Isolation and Grading of Faults in Battery Packs Based on Machine Learning Methods
by Sen Yang, Boran Xu and Hanlin Peng
Electronics 2022, 11(9), 1494; https://doi.org/10.3390/electronics11091494 - 06 May 2022
Cited by 4 | Viewed by 1750
Abstract
As the installed energy storage stations increase year by year, the safety of energy storage batteries has attracted the attention of industry and academia. In this work, an intelligent fault diagnosis scheme for series-connected battery packs based on wavelet characteristics of battery voltage [...] Read more.
As the installed energy storage stations increase year by year, the safety of energy storage batteries has attracted the attention of industry and academia. In this work, an intelligent fault diagnosis scheme for series-connected battery packs based on wavelet characteristics of battery voltage correlations is designed. First, the cross-cell voltages of multiple cells are preprocessed using an improved recursive Pearson correlation coefficient to capture the abnormal electrical signals. Secondly, the wavelet packet decomposition is applied to the coefficient series to obtain fault-related features from wavelet sub-bands, and the most representative characteristic principal components are extracted. Finally, the artificial neural network (ANN) and multi-classification relevance vector machine (mRVM) are employed to classify and evaluate fault mode and fault degree, respectively. Physical injection of external and internal short circuits, thermal damage, and loose connection failure is carried out to collect real fault data for model training and method validation. Experimental results show that the proposed method can effectively detect and locate different faults using the extracted fault features; mRVM is better than ANN in thermal fault diagnosis, while the overall diagnosis performance of ANN is better than mRVM. The success rates of fault isolation are 82% and 81%, and the success rates of fault grading are 98% and 90%, by ANN and mRVM, respectively. Full article
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