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Batteries
Special Issues
Advances in Battery Management Systems
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Advances 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 (15 December 2023) | Viewed by 7970

Special Issue Editors

Dr. Luc Raijmakers

E-Mail Website
Guest Editor
Forschungszentrum Jülich (IEK-9), D-52425 Jülich, Germany
Interests: batteries; Li-ion batteries; solid-state batteries; battery modeling; battery temperature; sensorless battery temperature measurements; electrochemical impedance spectroscopy; battery ageing; battery management systems; reference electrodes and sensors for batteries; vehicle-to-grid
Dr. Kudakwashe Chayambuka

E-Mail Website
Guest Editor
Forschungszentrum Jülich (IEK-9), D-52425 Jülich, Germany
Interests: batteries; lithium-ion; sodium-ion; solid-state batteries; battery modeling; battery temperature; battery ageing; battery management systems

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed phenomenal growth in electrochemical battery technologies driven by the proliferation of Li-ion batteries in the e-mobility and renewable energy storage sectors. At present, we are witnessing beyond-Li-ion breakthroughs in the form of new cell chemistries and configurations, as exemplified by the Na-ion chemistry and anode-free configurations. For all chemistries and configurations, it holds that the successful implementation in battery-powered systems is dependent on the design of the battery management system (BMS), since this finally determines the performance of the system in its broadest sense. The BMS is, therefore, the key enabling component to assure the dependability of emerging battery technologies.

For this Special Issue, we seek contributions focusing on challenges, solutions and improvements to the BMS hardware and software. We aim to highlight recent innovative discoveries addressing battery safety, state-of-charge (SoC), state-of-health (SoH), state-of-power (SoP) estimation, thermal behavior, thermal runaway prediction and Li plating. Moreover, we are happy to receive contributions on the implementation of advanced measurements techniques/diagnostics, sensors and reference electrodes for BMS.

Topics of interest include, but are not limited to:

  • State-of-charge (SoC) state-of-health (SoH) and state-of-power (SoP) estimation;
  • Models for BMS;
  • Battery diagnosis;
  • Battery aging;
  • Balancing techniques;
  • Reference electrodes and sensors for BMS;
  • Measurement techniques for BMS;
  • Advanced BMS safety features;
  • Thermal management systems;
  • Artificial Intelligence in BMS;
  • Cloud-connected BMS;
  • Beyond-Lithium BMS.

Dr. Luc Raijmakers
Dr. Kudakwashe Chayambuka
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

  • BMS
  • battery sensors
  • safe battery pack design
  • battery safety
  • plating detection
  • battery pack balancing circuits
  • thermal runaway detection
  • battery models
  • reference electrodes
  • thermal management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 3267 KiB  
Article
Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure
by Jan Neunzling, Hanno Winter, David Henriques, Matthias Fleckenstein and Torsten Markus
Batteries 2023, 9(10), 494; https://doi.org/10.3390/batteries9100494 - 27 Sep 2023
Cited by 4 | Viewed by 2777
Abstract
State-of-health (SoH) estimation is one of the key tasks of a battery management system, (BMS) as battery aging results in capacity- and power fade that must be accounted for by the BMS to ensure safe operation over the battery’s lifetime. In this study, [...] Read more.
State-of-health (SoH) estimation is one of the key tasks of a battery management system, (BMS) as battery aging results in capacity- and power fade that must be accounted for by the BMS to ensure safe operation over the battery’s lifetime. In this study, an online SoH estimator approach for NMC Li-ion batteries is presented which is suitable for implementation in a BMS. It is based on an observer structure in which the difference between a calculated and expected open-circuit voltage (OCV) is used for online SoH estimation. The estimator is parameterized and evaluated using real measurement data. The data were recorded for more than two years on an electrified bus fleet of 10 buses operated in a mild European climate and used regularly in the urban transport sector. Each bus is equipped with four NMC Li-ion batteries. Every battery has an energy of 30.6 kWh. Additionally, two full-capacity checkup measurements were performed for one of the operated batteries: one directly after production and one after two years of operation. Initial validation results demonstrated a SoH estimation accuracy of ±0.5% compared to the last checkup measurement. Full article
(This article belongs to the Special Issue Advances in Battery Management Systems)
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23 pages, 5370 KiB  
Article
State-of-Health Estimation and Anomaly Detection in Li-Ion Batteries Based on a Novel Architecture with Machine Learning
by Junghwan Lee, Huanli Sun, Yuxia Liu, Xue Li, Yixin Liu and Myungjun Kim
Batteries 2023, 9(5), 264; https://doi.org/10.3390/batteries9050264 - 8 May 2023
Cited by 8 | Viewed by 4387
Abstract
Variations across cells, modules, packs, and vehicles can cause significant errors in the state estimation of LIBs using machine learning algorithms, especially when trained with small datasets. Training with large datasets that account for all variations is often impractical due to resource and [...] Read more.
Variations across cells, modules, packs, and vehicles can cause significant errors in the state estimation of LIBs using machine learning algorithms, especially when trained with small datasets. Training with large datasets that account for all variations is often impractical due to resource and time constraints at initial product release. To address this issue, we proposed a novel architecture that leverages electronic control units, edge computers, and the cloud to detect unrevealed variations and abnormal degradations in LIBs. The architecture comprised a generalized deep neural network (DNN) for generalizability, a personalized DNN for accuracy within a vehicle, and a detector. We emphasized that a generalized DNN trained with small datasets must show reasonable estimation accuracy during cross validation, which is critical for real applications before online training. We demonstrated the feasibility of the architecture by conducting experiments on 65 DNN models, where we found distinct hyperparameter configurations. The results showed that the personalized DNN achieves a root mean square error (RMSE) of 0.33%, while the generalized DNN achieves an RMSE of 4.6%. Finally, the Mahalanobis distance was used to consider the SOH differences between the generalized DNN and personalized DNN to detect abnormal degradations. Full article
(This article belongs to the Special Issue Advances in Battery Management Systems)
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