Topic Editors

Dr. Zhiqiang Lyu
School of Internet, Anhui University, Hefei, China
College of Mechanical Engineering, Anhui Science and Technology University, Chuzhou, China
Prof. Dr. Renjing Gao
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, China

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

Abstract submission deadline
closed (31 October 2023)
Manuscript submission deadline
closed (31 March 2024)
Viewed by
7229

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

  • electric vehicles
  • lithium-ion battery
  • battery modeling and state estimation
  • fault diagnosis
  • artificial intelligence

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 (4 papers)

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20 pages, 6670 KiB  
Article
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 1187
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
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17 pages, 6535 KiB  
Article
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 1621
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
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15 pages, 3505 KiB  
Article
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 1422
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
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15 pages, 5525 KiB  
Article
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 1520
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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