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Batteries
Special Issues
State-of-the-Art in Battery Management Systems
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State-of-the-Art in Battery Management 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 (30 September 2023) | Viewed by 3640

Special Issue Editors

Prof. Dr. Chen Lu

E-Mail Website
Guest Editor
Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Interests: fault diagnosis; prognostics and health management; intelligent maintenance systems
Prof. Dr. Jian Ma

E-Mail Website
Guest Editor
1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2. International Innovation Institute, Beihang University, Hangzhou 311115, China
Interests: batteries; fault diagnosis; prognostics and health management; deep learning; transfer learning; remaining useful life prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Ding

E-Mail Website
Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: fault diagnosis; prognostics and health management; deep reinforcement learning; deep learning; remaining useful life prediction

Special Issue Information

Dear Colleagues,

With the wide application of new energy electric vehicles, the capacity, safety, health status, and endurance of batteries have increasingly become the focus of attention. Battery management systems (BMSs) play crucial roles in monitoring and controlling the battery. They feed collected battery information to a user in real time and adjust the parameters according to the collected information toward maximizing battery performance. Therefore, the performance of BMSs crucially affects the battery lifespan, driving range, and safety of electric vehicles. However, further studies of advanced BMSs are still needed to ensure they continue to meet the ever-increasing practical requirements.

By focusing on these considerations, this Special Issue intends to cover state-of-the-art findings, innovative methodologies, and potential breakthroughs in the related field. Of interest is research that can assist in improving battery performance, safety, and resiliency through software algorithms and hardware systems, such as through artificial intelligence models or mechanism models for analysis, modeling, control, and management of the battery state (including risk, energy, health, thermal, operating mode, etc.). We look forward to the contributions of original research articles and review articles from academia and the industry for publication in this Special Issue.

Topics of interest include, but are not limited to:

  • Reliability analysis and design of battery systems
  • Risk analysis and safety assessment of battery systems
  • Battery aging modeling
  • State of X estimation (SOC, SOH, SOL, etc.)
  • Energy management strategy and circuits
  • Thermal management strategy and circuits
  • Battery system fault detection and prognosis
  • Health management strategy
  • Fault-tolerant control strategy and circuits
  • Reconfigurable battery systems
  • Battery cell and module balancing strategy and circuits

Prof. Dr. Chen Lu
Dr. Jian Ma
Dr. Yu Ding
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
  • battery management system (BMS)
  • hardware and software algorithms
  • reliability and safety analysis
  • fault prognostics and health management

Published Papers (2 papers)

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Research

17 pages, 10873 KiB  
Article
A Two-State-Based Hybrid Model for Degradation and Capacity Prediction of Lithium-Ion Batteries with Capacity Recovery
by Yu Chen, Laifa Tao, Shangyu Li, Haifei Liu and Lizhi Wang
Batteries 2023, 9(12), 596; https://doi.org/10.3390/batteries9120596 - 15 Dec 2023
Viewed by 1792
Abstract
The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the [...] Read more.
The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA’s lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method. Full article
(This article belongs to the Special Issue State-of-the-Art in Battery Management Systems)
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19 pages, 5637 KiB  
Article
Combined State of Charge and State of Energy Estimation for Echelon-Use Lithium-Ion Battery Based on Adaptive Extended Kalman Filter
by Enguang Hou, Zhen Wang, Xiaopeng Zhang, Zhixue Wang, Xin Qiao and Yun Zhang
Batteries 2023, 9(7), 362; https://doi.org/10.3390/batteries9070362 - 6 Jul 2023
Cited by 3 | Viewed by 1277
Abstract
To ensure the safety and reliability of an echelon-use lithium-ion battery (EULIB), the performance of a EULIB is accurately reflected. This paper presents a method of estimating the combined state of energy (SOE) and state of charge (SOC). First, aiming to improve the [...] Read more.
To ensure the safety and reliability of an echelon-use lithium-ion battery (EULIB), the performance of a EULIB is accurately reflected. This paper presents a method of estimating the combined state of energy (SOE) and state of charge (SOC). First, aiming to improve the accuracy of the SOE and SOC estimation, a third-order resistor-capacitance equivalent model (TRCEM) of a EULIB is established. Second, long short-term memory (LSTM) is introduced to optimize the Ohmic internal resistance (OIR), actual energy (AE), and actual capacity (AC) parameters in real time to improve the accuracy of the model. Third, in the process of the SOE and SOC estimation, the observation noise equation and process noise equation are updated iteratively to make adaptive corrections and enhance the adaptive ability. Finally, an SOE and SOC estimation method based on LSTM optimization and an adaptive extended Kalman filter (AEKF) is established. In simulation experiments, when the capacity decays to 90%, 60% and 30% of the rated capacity, regardless of whether the initial value is consistent with the actual value, the values of the SOE and SOC estimation can track the actual value with strong adaptive ability, and the estimated error is less than 1.19%, indicating that the algorithm has a high level of accuracy. The method presented in this paper provides a new perspective for estimating the SOE and SOC of a EULIB. Full article
(This article belongs to the Special Issue State-of-the-Art in Battery Management Systems)
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