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Batteries
Special Issues
Advanced Control and Optimization of Battery Energy Storage Systems
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Advanced Control and Optimization of Battery Energy Storage Systems

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 10710

Special Issue Editor

Dr. Weiji Han

E-Mail Website
Guest Editor
China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: battery management systems; electric vechiles; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To meet the ever-increasing demand for energy storage and power supply, battery energy storage systems (BESSs), typically consisting of batteries, power electronics, and control systems, are being applied to grid-level energy storage and electric vehicles. Among these BESS applications, numerous benefits have been demonstrated so far, e.g., facilitating the integration of renewable energy with the power grid, improving grid stability and reliability, and promoting transportation electrification. However, there are various research gaps in the planning, operation, maintenance, and control of BESSs, regarding safety, reliability, scalability, cost effectiveness, battery lifespan, etc. Therefore, this Special Issue calls for original and innovative research and review papers to contribute to the advanced control and optimization of BESSs from the perspective of algorithm design or hardware implementation.

Dr. Weiji Han
Guest Editor

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. Batteries 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 2700 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

  • battery energy storage systems
  • battery management systems
  • battery system modeling and simulation
  • state estimation
  • charge balancing
  • thermal management
  • battery system control
  • battery performance optimization
  • battery system reconfiguration
  • battery degradation
  • electric vehicles
  • grid-level energy storage
  • renewable energy integration

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Related Special Issue

Published Papers (5 papers)

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Research

24 pages, 696 KiB  
Article
Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market
by Julio Gonzalez-Saenz and Victor Becerra
Batteries 2024, 10(7), 228; https://doi.org/10.3390/batteries10070228 - 25 Jun 2024
Cited by 1 | Viewed by 1869
Abstract
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in [...] Read more.
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
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18 pages, 3028 KiB  
Article
Dynamic Battery Modeling for Electric Vehicle Applications
by Renos Rotas, Petros Iliadis, Nikos Nikolopoulos, Dimitrios Rakopoulos and Ananias Tomboulides
Batteries 2024, 10(6), 188; https://doi.org/10.3390/batteries10060188 - 31 May 2024
Cited by 2 | Viewed by 1924
Abstract
The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit [...] Read more.
The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
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32 pages, 20012 KiB  
Article
A Novel Differentiated Control Strategy for an Energy Storage System That Minimizes Battery Aging Cost Based on Multiple Health Features
by Wei Xiao, Jun Jia, Weidong Zhong, Wenxue Liu, Zhuoyan Wu, Cheng Jiang and Binke Li
Batteries 2024, 10(4), 143; https://doi.org/10.3390/batteries10040143 - 22 Apr 2024
Viewed by 1799
Abstract
In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency [...] Read more.
In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, and the operating boundaries for both depth of discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
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15 pages, 409 KiB  
Article
Stochastic Control of Battery Energy Storage System with Hybrid Dynamics
by Richard Žilka, Ondrej Lipták and Martin Klaučo
Batteries 2024, 10(3), 75; https://doi.org/10.3390/batteries10030075 - 23 Feb 2024
Viewed by 1771
Abstract
This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, [...] Read more.
This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
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25 pages, 15617 KiB  
Article
Primary-Frequency-Regulation Coordination Control of Wind Power Inertia and Energy Storage Based on Compound Fuzzy Logic
by Suliang Ma, Dixi Xin, Yuan Jiang, Jianlin Li, Yiwen Wu and Guanglin Sha
Batteries 2023, 9(12), 564; https://doi.org/10.3390/batteries9120564 - 23 Nov 2023
Cited by 2 | Viewed by 1925
Abstract
The increasing proportion of wind power systems in the power system poses a challenge to frequency stability. This paper presents a novel fuzzy frequency controller. First, this paper models and analyzes the components of the wind storage system and the power grid and [...] Read more.
The increasing proportion of wind power systems in the power system poses a challenge to frequency stability. This paper presents a novel fuzzy frequency controller. First, this paper models and analyzes the components of the wind storage system and the power grid and clarifies the role of each component in the frequency regulation process. Secondly, a combined fuzzy controller is designed in this paper, which realizes the cooperative control of frequency regulation considering wind power running state, battery energy management, and power grid stability. Finally, this paper establishes typical operation scenarios of various time scales to verify the effectiveness and feasibility of the proposed control strategy. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
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