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Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics,...
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Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors

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 (11 March 2024) | Viewed by 11739

Special Issue Editors

Prof. Dr. Chao Lyu

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Interests: novel theories and technichs in battery management
Dr. Jianing Xu

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Interests: energy storage; electric vehicle; battery management systems; battery safety warning
Special Issues, Collections and Topics in MDPI journals
Dr. Yuchen Song

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: lithium-ion battery pack state estimation; lithium-ion battery sensing and management

Special Issue Information

Dear Colleagues,

Nowadays, batteries are becoming more and more popular in electric vehicles, household energy storage, and large-scale grid energy storage. In order to make the battery energy storage technology more competitive than other energy storage methods, high reliability and long life have always been the goal of battery energy storage technology. The deepening of battery physics research, the development of new machine learning algorithms and new sensor technologies have provided unprecedented opportunities for a higher level of battery management system (BMS). Traditional state estimation and lifetime prediction methods can become more accurate and reliable, and can adapt to battery aging and a wider range of temperature and current rate, helping us grasp the real state of the battery. Fault diagnosis methods developed by means of Big Data analysis and multi-source sensor fusion can identify and locate faults more quickly and accurately. The online identification of battery electrical and thermal abuse boundaries can provide a more scientific basis for battery safety and long life scheduling.

To that end, the potential topics of this Special Issue include, but are not limited to:

  • Applications of multi-physics modelling in BMS;
  • Applications of machine learning methods in BMS;
  • Applications of novel force, thermal, electrical, and gas sensors in BMS;
  • Optimal design and control of thermal management system;
  • Optimal scheduling of energy storage battery system with the goal of extending service life.

Prof. Dr. Chao Lyu
Dr. Jianing Xu
Dr. Yuchen Song
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. 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
  • battery management system
  • thermal management
  • multi-physics modelling
  • machine learning
  • novel sensor techniques
  • optimal scheduling of energy storage battery

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Published Papers (4 papers)

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Research

22 pages, 5556 KiB  
Article
SOC Estimation of a Lithium-Ion Battery at Low Temperatures Based on a CNN-Transformer and SRUKF
by Xun Gong, Tianzhu Jiang, Bosong Zou, Huijie Wang, Kaiyi Yang, Xinhua Liu, Bin Ma and Jiamei Lin
Batteries 2024, 10(12), 426; https://doi.org/10.3390/batteries10120426 - 1 Dec 2024
Cited by 1 | Viewed by 1150
Abstract
As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under [...] Read more.
As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under multi-temperature scenarios plays an important role in guaranteeing the safety, economy, and reliability of electric vehicles. In this paper, a SOC estimation method based on the fusion of convolutional neural network-transformer (CNN-Transformer) and square root unscented Kalman filter (SRUKF) for lithium-ion batteries in low-temperature scenarios is proposed. First, the CNN-Transformer base model is established. Then, the SRUKF algorithm is used to update the state of the Coulomb counting method results based on the base model results. Finally, ensemble learning theory is applied to estimate SOC in multi-temperature scenarios. Data is obtained from laboratory conditions at −20 °C, −7 °C, and 0 °C. The experimental results show that the SOC estimation method proposed in this study is stable in terms of the root mean square error (RMSE) being between 2.69% and 4.22%. The proposed base model is also compared with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network to demonstrate its relative advantages. Full article
(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)
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14 pages, 14174 KiB  
Article
Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network
by Miao Bai, Chao Lyu, Dazhi Yang and Gareth Hinds
Batteries 2023, 9(7), 350; https://doi.org/10.3390/batteries9070350 - 1 Jul 2023
Cited by 4 | Viewed by 3262
Abstract
Accurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration [...] Read more.
Accurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as being fast and non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked with establishing a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method. Full article
(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)
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17 pages, 10002 KiB  
Article
A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing
by Hekun Zhang, Xiangdong Kong, Yuebo Yuan, Jianfeng Hua, Xuebing Han, Languang Lu, Yihui Li, Xiaoyi Zhou and Minggao Ouyang
Batteries 2023, 9(7), 346; https://doi.org/10.3390/batteries9070346 - 27 Jun 2023
Cited by 2 | Viewed by 2894
Abstract
During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line [...] Read more.
During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety. Full article
(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)
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18 pages, 5857 KiB  
Article
State of Charge and Temperature Joint Estimation Based on Ultrasonic Reflection Waves for Lithium-Ion Battery Applications
by Runnan Zhang, Xiaoyu Li, Chuanyu Sun, Songyuan Yang, Yong Tian and Jindong Tian
Batteries 2023, 9(6), 335; https://doi.org/10.3390/batteries9060335 - 20 Jun 2023
Cited by 55 | Viewed by 3385
Abstract
Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation [...] Read more.
Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system. Full article
(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)
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