Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System

Topic Information

Dear Colleagues,

Actively developing and utilizing renewable energy and increasing the proportion of electrified transportation have become the critical methods of sustainable development and are the important driving force to achieve green and sustainable development. Lithium-ion batteries, as an important energy storage system in electric vehicles, boats, and aircrafts, urgently need to be monitored in real time for ensuring safe operation. The batteries that cannot provide the expected performance will be replaced. Therefore, the optimal design and control of battery systems, including battery modeling, states estimation, fault diagnosis, and echelon utilization, have attracted wide attention. For this reason, this Topic is intends provide a platform to share the latest findings on this subject (either research or review articles). Potential topics of interest include but are not limited to the following:

• Battery modeling;
• Battery state estimation (SOC/SOH/SOT/RUL);
• Battery thermal management;
• Battery fault diagnosis;
• Battery smart charging technology;
• Sorting, regrouping, and echelon utilization of retired batteries;
• Artificial-intelligence-based battery management system.

Dr. Zhiqiang Lyu
Dr. Longxing Wu
Prof. Dr. Renjing Gao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.6	3.2	2012	16.7 Days	CHF 2400
Batteries batteries	4.0	5.4	2015	17.7 Days	CHF 2700
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400
Technologies technologies	3.6	5.5	2013	19.7 Days	CHF 1600

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Published Papers (5 papers)

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31 pages, 4717 KiB  

Open AccessArticle

Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market

by Samuel O. Ezennaya and Julia Kowal

Sustainability 2024, 16(9), 3645; https://doi.org/10.3390/su16093645 - 26 Apr 2024

Viewed by 90

Abstract

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark [...] Read more.

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies. Full article

(This article belongs to the Topic Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System)

20 pages, 6670 KiB  

Open AccessArticle

Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries

by Chunsong Lin, Xianguo Tuo, Longxing Wu, Guiyu Zhang and Xiangling Zeng

Batteries 2024, 10(3), 71; https://doi.org/10.3390/batteries10030071 - 21 Feb 2024

Viewed by 1285

Abstract

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health [...] Read more.

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments. Full article

(This article belongs to the Topic Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System)

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17 pages, 6535 KiB  

Open AccessArticle

Impact of Multiple Module Collectors on the Cell Current Distribution within the Battery Pack

by Zhihao Yu, Zhezhe Sun, Long Chang, Chen Ma, Changlong Li, Hongyu Li, Chunxiao Luan and Mohammad Y. M. Al-saidi

Batteries 2023, 9(10), 501; https://doi.org/10.3390/batteries9100501 - 02 Oct 2023

Cited by 1 | Viewed by 1654

Abstract

Lithium-ion batteries are usually connected in series and parallel to form a pack for meeting the voltage and capacity requirements of energy storage systems. However, different pack configurations and battery module collector positions result in different equivalent connected resistances, leading to pack current [...] Read more.

Lithium-ion batteries are usually connected in series and parallel to form a pack for meeting the voltage and capacity requirements of energy storage systems. However, different pack configurations and battery module collector positions result in different equivalent connected resistances, leading to pack current inhomogeneity, which seriously reduces the lifetime and safety of the pack. Therefore, in order to quantitatively analyze the influence of the connected resistance on the current distribution, this study researched the initial cell current distribution of the parallel module by developing mathematical models of different configurations. Then, this study explored the influence of multiple module collector positions on the current inhomogeneity of the pack under the dynamic current condition. The results show that the inhomogeneity of cell current and discharge capacity in the pack with parallel modules connected in series can be improved by keeping each cell in a parallel module with the same distance to its module collector. Furthermore, the current homogeneity of the edge parallel modules in the pack is seriously affected by the position of the single module collector. Therefore, this study innovatively proposes the symmetrical multiple module collectors of the pack, which can greatly improve the current homogeneity of the edge parallel modules, thereby improving the lifetime and safety of the pack. Full article

(This article belongs to the Topic Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System)

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15 pages, 3505 KiB  

Open AccessArticle

Classification of Lithium-Ion Batteries Based on Impedance Spectrum Features and an Improved K-Means Algorithm

by Qingping Zhang, Jiaqiang Tian, Zhenhua Yan, Xiuguang Li and Tianhong Pan

Batteries 2023, 9(10), 491; https://doi.org/10.3390/batteries9100491 - 26 Sep 2023

Viewed by 1466

Abstract

This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is based on the flow direction algorithm (FDA). In order [...] Read more.

This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is based on the flow direction algorithm (FDA). In order to reduce the dimensionality of battery features, the Pearson correlation coefficient is employed to analyze the correlation between impedance spectrum features. The battery classification is carried out using the improved K-means algorithm, which incorporates the optimization of the initial clustering center using the grey wolf optimization (GWO) algorithm. The experimental results demonstrate the effectiveness of this method in accurately classifying batteries and its high level of accuracy and robustness. Consequently, this method can be relied upon to provide robust support for battery performance evaluation and fault diagnosis. Full article

(This article belongs to the Topic Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System)

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15 pages, 5525 KiB  

Open AccessArticle

A Control Algorithm for Tapering Charging of Li-Ion Battery in Geostationary Satellites

by Jeong-Eon Park

Energies 2023, 16(15), 5636; https://doi.org/10.3390/en16155636 - 26 Jul 2023

Viewed by 1562

Abstract

Recently, as the satellite data service market has grown significantly, satellite demand has been rapidly increasing. Demand for geostationary satellites with weather observation, communication broadcasting, and GPS missions is also increasing. Completing the charging process of the Li-ion battery during the sun period [...] Read more.

Recently, as the satellite data service market has grown significantly, satellite demand has been rapidly increasing. Demand for geostationary satellites with weather observation, communication broadcasting, and GPS missions is also increasing. Completing the charging process of the Li-ion battery during the sun period is one of the main tasks of the electrical power system in geostationary satellites. In the case of the electrical power system of low Earth orbit satellites, the Li-ion battery is connected to the DC/DC converter output, and the charging process is completed through CV control. However, in the case of the regulated bus of the DET type, which is mainly used in the electrical power system of geostationary satellites, a Li-ion battery is connected to the input of the DC/DC converter. Therefore, a method other than the CV control of the DC/DC converter is required. This paper proposes a control algorithm for tapering charging of the Li-ion battery in the regulated bus of the DET type for Li-ion battery charge completion operation required by space-level design standards. In addition, the proposed control algorithm is verified through an experiment on a geostationary satellite’s ground electrical test platform. The experiment verified that it has a power conversion efficiency of 99.5% from the solar array to the battery. It has 21 tapering steps at the equinox and 17 tapering steps at the solstice. Full article

(This article belongs to the Topic Advanced Technology in Optimal Design and Control of Lithium-Ion Battery System)
(This article belongs to the Section D: Energy Storage and Application)

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