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Batteries
Special Issues
Towards a Smarter Battery Management System
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Towards a Smarter Battery Management System

A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 8502

Special Issue Editors

Prof. Dr. Chris Mi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: wireless power transfer; battery management systems; power electronics; hybrid electric vehicles; electric machines
Dr. Wei Gao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: battery management system; lithium-ion battery; electric vehicle battery

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are widely used in electric vehicles (EV) and the energy storage industry due to their high-energy density and long cycle life. As their price decreases, lithium-ion batteries will continue to be used in the future. Battery management systems (BMS) are the key component to ensure the stable and reliable operation of battery systems. It monitors the battery operation data; estimates the battery state of charge (SOC) and state of health (SOH); conducts battery balance; manages thermal systems; and performs fault diagnosis, etc. BMS-related hardware and algorithm have developed rapidly in recent years. Therefore, this Special Issue aims to demonstrate the latest BMS-related technologies, such as SOC and SOH estimation algorithms, balance systems, wireless BMS, and second life battery applications, etc.

Potential topics include, but are not limited to:

  • Battery management system hardware and algorithms;
  • Battery modeling;
  • Battery parameter identification;
  • Battery state of charge (SOC) estimation;
  • Battery state of health (SOH) estimation;
  • Battery fault diagnostics;
  • Battery balance or equalization topology and method;
  • Battery thermal management;
  • Battery second life application;
  • Wireless BMSs.

Prof. Dr. Chris Mi
Dr. Wei Gao
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

  • lithium-ion battery
  • battery management system
  • battery modeling
  • battery SOC estimation
  • battery SOH estimation
  • battery parameter identification
  • battery balance
  • battery equalization
  • battery thermal management
  • battery thermal runaway
  • second life battery
  • battery recycling
  • wireless BMS

Published Papers (7 papers)

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Research

13 pages, 4213 KiB  
Article
On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health
by Søren B. Vilsen and Daniel-Ioan Stroe
Batteries 2024, 10(6), 193; https://doi.org/10.3390/batteries10060193 - 31 May 2024
Abstract
As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles to more dynamic profiles constructed to mimic [...] Read more.
As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles to more dynamic profiles constructed to mimic real operation when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end of life. Using data from batteries aged in this fashion to create a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to train models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only one randomly sampled partial charge is needed to achieve errors smaller than 1%. Lastly, as the number of randomly sampled partial charges used to train the model increases, the dependence on particular partial charges tends to decrease. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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23 pages, 4122 KiB  
Article
Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries
by Jiaqi Yao, Steven Neupert and Julia Kowal
Batteries 2024, 10(6), 171; https://doi.org/10.3390/batteries10060171 - 22 May 2024
Viewed by 372
Abstract
As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, [...] Read more.
As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
13 pages, 3087 KiB  
Article
The Effect of Battery Configuration on Dendritic Growth: A Magnetic Resonance Microscopy Study on Symmetric Lithium Cells
by Rok Peklar, Urša Mikac and Igor Serša
Batteries 2024, 10(5), 165; https://doi.org/10.3390/batteries10050165 - 17 May 2024
Viewed by 363
Abstract
The potential of metallic lithium to become the anode material for next-generation batteries is hampered by significant challenges, chief among which is dendrite growth during battery charging. These dendritic structures not only impair battery performance but also pose safety risks. Among the non-destructive [...] Read more.
The potential of metallic lithium to become the anode material for next-generation batteries is hampered by significant challenges, chief among which is dendrite growth during battery charging. These dendritic structures not only impair battery performance but also pose safety risks. Among the non-destructive analytical techniques in battery research, Magnetic Resonance Imaging (MRI) stands out as a promising tool. However, the direct imaging of lithium by 7Li MRI is limited by its low sensitivity and spatial resolution, making it a less effective way of imaging dendrite growth. Instead, a recently introduced indirect imaging approach which is based on 1H MRI of the electrolyte was used in this study. This method was used to sequentially 3D image and thus monitor the charging process of lithium metal symmetric cells in three different electrical circuits, namely those composed of a single cell, four cells in parallel, and four cells in series. The measured sequential images allowed for the measurement of dendrite growth in each cell using volumetric analysis. The growth results confirmed the theoretical prediction that the growth across cells is uneven in a parallel circuit, and even in a series circuit. The methods presented in this study can also be applied to analyze many other dendrite-related issues in batteries. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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30 pages, 6948 KiB  
Article
Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling
by Zaid A. Al Muala, Mohammad A. Bany Issa and Pastora M. Bello Bugallo
Batteries 2024, 10(4), 138; https://doi.org/10.3390/batteries10040138 - 19 Apr 2024
Viewed by 947
Abstract
Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy [...] Read more.
Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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18 pages, 5824 KiB  
Article
Low-Computational Model to Predict Individual Temperatures of Cells within Battery Modules
by Ali Abbas, Nassim Rizoug, Rochdi Trigui, Eduardo Redondo-Iglesias and Serge Pelissier
Batteries 2024, 10(3), 98; https://doi.org/10.3390/batteries10030098 - 12 Mar 2024
Cited by 1 | Viewed by 1540
Abstract
Predicting the operating temperature of lithium-ion battery during different cycles is important when it comes to the safety and efficiency of electric vehicles. In this regard, it is vital to adopt a suitable modeling approach to analyze the thermal performance of a battery. [...] Read more.
Predicting the operating temperature of lithium-ion battery during different cycles is important when it comes to the safety and efficiency of electric vehicles. In this regard, it is vital to adopt a suitable modeling approach to analyze the thermal performance of a battery. In this paper, the temperature of lithium-ion NMC pouch battery has been investigated. A new formulation of lumped model based on the thermal resistance network is proposed. Unlike previous models that treated the battery as a single entity, the proposed model introduces a more detailed analysis by incorporating thermal interactions between individual cells and tabs within a single cell scenario, while also considering interactions between cells and insulators or gaps, located between the cells, within the module case. This enhancement allows for the precise prediction of temperature variations across different cells implemented within the battery module. In order to evaluate the accuracy of the prediction, a three-dimensional finite element model was adopted as a reference. The study was performed first on a single cell, then on modules composed of several cells connected in series, during different operating conditions. A comprehensive comparison between both models was conducted. The analysis focused on two main aspects, the accuracy of temperature predictions and the computational time required. Notably, the developed lumped model showed a significant capability to estimate cell temperatures within the modules. The thermal results revealed close agreement with the values predicted by the finite element model, while needing significantly lower computational time. For instance, while the finite element model took almost 21 h to predict the battery temperature during consecutive charge/discharge cycles of a 10-cell module, the developed lumped model predicted the temperature within seconds, with a maximum difference of 0.42 °C. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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16 pages, 2827 KiB  
Article
A Novel Long Short-Term Memory Approach for Online State-of-Health Identification in Lithium-Ion Battery Cells
by Mike Kopp, Alexander Fill, Marco Ströbel and Kai Peter Birke
Batteries 2024, 10(3), 77; https://doi.org/10.3390/batteries10030077 - 26 Feb 2024
Viewed by 1908
Abstract
Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise [...] Read more.
Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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13 pages, 13121 KiB  
Article
Pretreatment of Lithium Ion Batteries for Safe Recycling with High-Temperature Discharging Approach
by Arpita Mondal, Yuhong Fu, Wei Gao and Chunting Chris Mi
Batteries 2024, 10(1), 37; https://doi.org/10.3390/batteries10010037 - 21 Jan 2024
Cited by 2 | Viewed by 2484
Abstract
The ongoing transition toward electric vehicles is a major factor in the exponential rise in demand for lithium-ion batteries (LIBs). There is a significant effort to recycle battery materials to support the mining industry in ensuring enough raw materials and avoiding supply disruptions, [...] Read more.
The ongoing transition toward electric vehicles is a major factor in the exponential rise in demand for lithium-ion batteries (LIBs). There is a significant effort to recycle battery materials to support the mining industry in ensuring enough raw materials and avoiding supply disruptions, so that there will be enough raw materials to produce LIBs. Nevertheless, LIBs that have reached the end of their useful lives and are sent for recycling may still have some energy left in them, which could be dangerous during handling and processing. Therefore, it is important to conduct discharge pretreatment of LIBs before dismantling and crushing them, especially in cases where pyrometallurgical recycling is not used. Electrochemical discharge in conducting solutions has been commonly studied and implemented for this purpose, but its effectiveness has yet to be fully validated. Non-electrochemical discharge has also been researched as a potentially cleaner and more efficient discharge technology at the same time. This article presents a non-electrochemical discharge process by completely draining the energy from used batteries before recycling. A comprehensive investigation of the behavior of LIBs during discharge and the amount of energy remaining after fully discharging the battery at different temperatures is analyzed in this work. According to the experimental findings, completely discharging the battery at higher temperatures results in a reduced amount of residual energy in the battery. This outcome holds great importance in terms of safe and environmentally friendly recycling of used LIBs, emphasizing that safety and environmentally friendly recycling must go hand in hand with a cost-effective and sustainable solution. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
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