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Towards a Smarter Battery Management System: 2nd Edition
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Towards a Smarter Battery Management System: 2nd Edition

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".

Deadline for manuscript submissions: closed (25 March 2025) | Viewed by 15657

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
Special Issues, Collections and Topics in MDPI journals
Dr. Zhi Cao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: battery management systems; energy management systems; electric machines; magnetic bearings
Dr. Naser Vosoughi Kurdkandi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: DC–DC and DC–AC power electronics converters; battery-based energy storage systems; on-board and off-board battery chargers for EVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are widely used in electric vehicles (EVs) 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 (BMSs) are the key component to ensure the stable and reliable operation of battery systems. They monitor battery operation data; estimate the battery state of charge (SOC) and state of health (SOH); conduct battery balance; manage thermal systems; and perform fault diagnosis. BMS-related hardware and algorithms 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 BMSs, and second-life battery applications.

Potential topics include, but are not limited to, the following:

  • 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. Zhi Cao
Dr. Naser Vosoughi Kurdkandi
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 BMSs

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Related Special Issue

Published Papers (9 papers)

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Research

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21 pages, 1231 KiB  
Article
Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximising-Recurrent Conditional Generative Adversarial Networks
by Steven Neupert, Jiaqi Yao and Julia Kowal
Batteries 2025, 11(4), 149; https://doi.org/10.3390/batteries11040149 - 9 Apr 2025
Viewed by 280
Abstract
The paper presents two approaches to generating load cycles for electrical energy storage systems. A load cycle is described as the operation of an energy storage system. The cycles can include different metrics depending on the storage application. Load cycle analysis using the [...] Read more.
The paper presents two approaches to generating load cycles for electrical energy storage systems. A load cycle is described as the operation of an energy storage system. The cycles can include different metrics depending on the storage application. Load cycle analysis using the rainflow counting method is employed to understand and validate the metrics of the load cycles generated. Current load cycle generation can involve clustering methods, random microtrip methods, and machine learning techniques. The study includes a random microtrip method that utilises the Random Pulse Method (RPM) and enhances it to develop an improved version called the Gradient Random Pulse Method (gradRPM), which includes the control of stress factors such as the gradient of the state of charge (SOC). This method is relatively simple but, in many cases, it fulfills its purpose. Another more sophisticated method to control stress factors has been proposed, namely the Information Maximising-Recurrent Conditional Generative Adversarial Network (Info-RCGAN). It uses a deep learning algorithm to follow a machine learning-based, data-driven load cycle generation approach. Both approaches use the measurement dataset of a BMW i3 over multiple years to generate new synthetic load cycles. After generating the load cycles using both approaches, they are applied in a laboratory environment to evaluate the stress factors and validate how similar the synthetic data are to a real measurement. The results provide insights into generating simulation or testing data for electrical energy storage applications. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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18 pages, 9541 KiB  
Article
Evaluating the Role of Entropy Change in Lithium-Ion Battery Electro-Thermal Modelling
by Félix-Antoine LeBel, Pascal Messier, Mathieu Blanchard and João Pedro F. Trovão
Batteries 2025, 11(3), 84; https://doi.org/10.3390/batteries11030084 - 20 Feb 2025
Viewed by 752
Abstract
The accurate estimation of lithium-ion cell internal temperature is crucial for the safe operation of battery packs, especially during high discharge rates, as operating outside the safe temperature range can lead to accelerated degradation or catastrophic failures. Heat generation in lithium-ion cells arises [...] Read more.
The accurate estimation of lithium-ion cell internal temperature is crucial for the safe operation of battery packs, especially during high discharge rates, as operating outside the safe temperature range can lead to accelerated degradation or catastrophic failures. Heat generation in lithium-ion cells arises primarily from ohmic losses and entropy change (ΔS), yet the latter remains frequently overlooked in battery modelling. However, the impact of considering or discarding ΔS from electro-thermal modelling remains subject to debate. This research highlights the critical role of ΔS in improving the accuracy of electro-thermal models for lithium-ion batteries, particularly in high-fidelity thermal simulations. It presents a systematic integration, ΔS, into electro-thermal models, leveraging the energetic macroscopic representation (EMR) approach to enhance predictive accuracy, a methodology not previously structured in this manner. This paper addresses this issue by performing a comparative analysis of an electro-thermal model (ETM) with and without ΔS. The findings provide clear insights into the role of entropy in electro-thermal modelling, demonstrating that while entropy change has a minimal impact on electrical behaviour prediction, it plays a crucial role in accurately capturing temperature dynamics, helping define the conditions under which it must be considered in simulations. While entropy can be neglected for coarse heat generation estimation, its inclusion enhances temperature prediction accuracy by up to 4 °C, making it essential for applications requiring precise thermal management. This study offers a detailed analysis of the conditions under which ΔS becomes critical to model accuracy, providing actionable guidance for battery engineers and researchers. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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15 pages, 4756 KiB  
Article
Inductor-Based Active Balancing Topology with Wide Voltage Range Capability
by Hourong Song, Branislav Hredzak and John Fletcher
Batteries 2025, 11(2), 77; https://doi.org/10.3390/batteries11020077 - 15 Feb 2025
Viewed by 769
Abstract
With the increasing number of batteries integrated into the grid, the electrification of transportation, and the importance of reusing secondary batteries to preserve natural resources, active balancing techniques are becoming critical for optimizing battery performance, ensuring safety, and extending their lifespan. There is [...] Read more.
With the increasing number of batteries integrated into the grid, the electrification of transportation, and the importance of reusing secondary batteries to preserve natural resources, active balancing techniques are becoming critical for optimizing battery performance, ensuring safety, and extending their lifespan. There is a demand for battery management solutions that can efficiently manage the balancing of battery cells across a wide range of voltage levels. This paper proposes a new inductor-based active balancing topology that achieves balancing by transferring energy from battery cells to the battery pack. One of its main advantages over existing designs is that it can operate over a wide battery cell voltage range. Moreover, multicell balancing with a balancing current independent of the imbalance level can be achieved by adjusting the width and interval of pulses. The proposed topology can be implemented using traditional low-side gate driving integrated circuits, avoiding the need for expensive isolated power modules and high-side gate drivers. Sample balancer designs for low-voltage battery cells as well as higher-voltage cells are provided. The presented experimental results verify the operation of the proposed balancer on a lithium-ion battery pack. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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18 pages, 2164 KiB  
Article
Comprehensive Investigation of the Durability of Lithium-Ion Batteries Under Frequency Regulation Conditions
by Yuxin Tian, Liye Wang, Chenglin Liao and Guifu Yan
Batteries 2025, 11(2), 75; https://doi.org/10.3390/batteries11020075 - 14 Feb 2025
Viewed by 486
Abstract
Due to the large-scale use of renewable energy generation and its lack of inertia, the frequency of the grid is extremely unstable. At the same time, with the vigorous development of new energy vehicles, large-scale power batteries have huge potential for renewable energy [...] Read more.
Due to the large-scale use of renewable energy generation and its lack of inertia, the frequency of the grid is extremely unstable. At the same time, with the vigorous development of new energy vehicles, large-scale power batteries have huge potential for renewable energy consumption. In this context, the Vehicle-to-Grid (V2G) method is proposed. Electric vehicles are used as energy storage systems to provide frequency regulation services as flexible power grid resources. However, when electric vehicles are invested in large-scale frequency regulation, their own power battery durability will also be affected. Based on this problem, the pseudo-two-dimensions (P2D) model of the battery was established in this paper, and the effects of temperature, state of charge (SOC), reported power, and frequency regulation conditions on battery capacity attenuation and negative potential distribution were explored through experiments and simulations. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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17 pages, 1349 KiB  
Article
Enhancing Mass Transport in Organic Redox Flow Batteries Through Electrode Obstacle Design
by Joseba Martínez-López, Unai Fernández-Gamiz, Eduardo Sánchez-Díez, Aitor Beloki-Arrondo and Íñigo Ortega-Fernández
Batteries 2025, 11(1), 29; https://doi.org/10.3390/batteries11010029 - 16 Jan 2025
Viewed by 957
Abstract
This study examines the impact of incorporating obstacles in the electrode structure of an organic redox flow battery with a flow-through configuration. Two configurations were compared: A control case without obstacles (Case 1) and a modified design with obstacles to enhance mass transport [...] Read more.
This study examines the impact of incorporating obstacles in the electrode structure of an organic redox flow battery with a flow-through configuration. Two configurations were compared: A control case without obstacles (Case 1) and a modified design with obstacles to enhance mass transport and uniformity (Case 2). While Case 1 exhibited marginally higher discharge voltages (average difference of 0.18%) due to reduced hydraulic resistance and lower Ohmic losses, Case 2 demonstrated significant improvements in concentration uniformity, particularly at low state-of-charge (SOC) levels. The obstacle design mitigated local depletion of active species, thereby enhancing limiting current density and improving minimum concentration values across the studied SOC range. However, the introduction of obstacles increased flow resistance and pressure drops, indicating a trade-off between electrochemical performance and pumping energy requirements. Notably, Case 2 performed better at lower flow rates, showcasing its potential to optimize efficiency under varying operating conditions. At higher flow rates, the advantages of Case 2 diminished but remained evident, with better concentration uniformity, higher minimum concentration values, and a 1% average increase in limiting current density. Future research should focus on optimizing obstacle geometry and positioning to further enhance performance. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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16 pages, 5829 KiB  
Article
Modelling of a Cylindrical Battery Mechanical Behavior under Compression Load
by Adrian Daniel Muresanu and Mircea Cristian Dudescu
Batteries 2024, 10(10), 353; https://doi.org/10.3390/batteries10100353 - 9 Oct 2024
Cited by 3 | Viewed by 1970
Abstract
The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion [...] Read more.
The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion batteries under compression load, using Finite Element Method (FEM) techniques to improve computational efficiency. A novel approach is proposed, involving the selective application of compression loads solely to the Jelly Roll and its casing, achieved by cutting the battery heads. Through this method, distinct mechanical behaviors are identified within the battery force displacement curve: an elastic region, a zone characterized by plastic deformation, and a segment exhibiting densification. By delineating these regions, our study facilitates a comprehensive understanding of the battery’s mechanical response under compression. Two battery models were employed in this study: one representing the battery as a solid volume, and another featuring the jelly roll as a solid volume enclosed by a shell representing the casing. The material utilized was LS Dyna MAT24, chosen for its piecewise characteristics’ definition, and its validation was primarily conducted through the curve fitting method applied to the force–displacement curve, taking in account the three regions of the compression force behavior. This approach not only optimizes computational resources but also offers insights crucial for enhancing the mechanical stability of Li-ion batteries in automotive applications. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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17 pages, 5949 KiB  
Article
Second-Life Assessment of Commercial LiFePO4 Batteries Retired from EVs
by Zhi Cao, Wei Gao, Yuhong Fu, Christopher Turchiano, Naser Vosoughi Kurdkandi, Jing Gu and Chris Mi
Batteries 2024, 10(9), 306; https://doi.org/10.3390/batteries10090306 - 30 Aug 2024
Viewed by 3039
Abstract
LiFePO4 (LFP) batteries are well known for their long cycle life. However, there are many reports of significant capacity degradation in LFP battery packs after only three to five years of operation. This study assesses the second-life potential of commercial LFP batteries [...] Read more.
LiFePO4 (LFP) batteries are well known for their long cycle life. However, there are many reports of significant capacity degradation in LFP battery packs after only three to five years of operation. This study assesses the second-life potential of commercial LFP batteries retired from electric vehicles (EVs) by evaluating their aging characteristics at the cell and module levels. Four LFP cells and four modules were subjected to aging tests under various conditions. The results indicate that LFP cells exhibit long life cycles with gradual capacity degradation and a minimal internal resistance increase. Module-level analysis reveals significant balance issues impacting capacity recovery. Incremental capacity analysis (ICA) and post-mortem analysis identify the loss of active materials and lithium inventory as key aging mechanisms. This study provides the optimal working conditions of second-life LFP batteries and suggests that, with proper balancing systems, LFP batteries can achieve extended second-life use in stationary energy storage applications, emphasizing the importance of effective balance management for sustainable battery utilization. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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Review

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25 pages, 4775 KiB  
Review
Sodium-Ion Batteries: Applications and Properties
by Petr Bača, Jiří Libich, Sára Gazdošová and Jaroslav Polkorab
Batteries 2025, 11(2), 61; https://doi.org/10.3390/batteries11020061 - 6 Feb 2025
Cited by 2 | Viewed by 2521
Abstract
With the growing interest in reducing CO2 emissions to combat climate change, humanity is turning to green or renewable sources of electricity. There are numerous issues associated with the development of these sources. One of the key aspects of renewable energy sources [...] Read more.
With the growing interest in reducing CO2 emissions to combat climate change, humanity is turning to green or renewable sources of electricity. There are numerous issues associated with the development of these sources. One of the key aspects of renewable energy sources is their problematic controllability, namely the control of energy production over time. Renewable sources are also associated with issues of recycling, utilization in different geographical zones, environmental impact within the required area, and so on. One of the most discussed issues today, however, is the question of efficient use of the energy produced from these sources. There are several different approaches to storing renewable energy, e.g., supercapacitors, flywheels, batteries, PCMs, pumped-storage hydroelectricity, and flow batteries. In the commercial sector, however, mainly due to acquisition costs, these options are narrowed down to only one concept: storing energy using an electrochemical storage device—batteries. Nowadays, lithium-ion batteries (LIBs) are the most widespread battery type. Despite many advantages of LIB technology, the availability of materials needed for the production of these batteries and the associated costs must also be considered. Thus, this battery type is not very ideal for large-scale stationary energy storage applications. Sodium-ion batteries (SIBs) are considered one of the most promising alternatives to LIBs in the field of stationary battery storage, as sodium (Na) is the most abundant alkali metal in the Earth’s crust, and the cell manufacturing process of SIBs is similar to that of LIBs. Unfortunately, considering the physical and electrochemical properties of Na, different electrode materials, electrolytes, and so on, are required. SIBs have come a long way since they were discovered. This review discusses the latest developments regarding the materials used in SIB technology. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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23 pages, 2200 KiB  
Review
Recent Advancements in Artificial Intelligence in Battery Recycling
by Subin Antony Jose, Connor Andrew Dennis Cook, Joseph Palacios, Hyundeok Seo, Christian Eduardo Torres Ramirez, Jinhong Wu and Pradeep L. Menezes
Batteries 2024, 10(12), 440; https://doi.org/10.3390/batteries10120440 - 11 Dec 2024
Cited by 5 | Viewed by 3472
Abstract
Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies [...] Read more.
Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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