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Batteries
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Towards a Smarter Battery Management System: 3rd Edition
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Towards a Smarter Battery Management System: 3rd Edition

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: closed (25 January 2026) | Viewed by 11970

Special Issue Editors

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
Special Issues, Collections and Topics in MDPI journals
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. 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.

Dr. Zhi Cao
Prof. Dr. Chris Mi
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 250 words) can be sent to the Editorial Office for assessment.

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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15 pages, 9699 KB  
Article
Geometry-Regulated Thermal Performance of Sedimentation-Stable MicroPCM Composite Capsules for Battery Thermal Management Systems Fabricated via 3D Printing
by Xuguang Zhang, Michael C. Halbig, Mrityunjay Singh, Amjad Almansour and Yi Zheng
Batteries 2026, 12(4), 144; https://doi.org/10.3390/batteries12040144 - 18 Apr 2026
Viewed by 779
Abstract
Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal [...] Read more.
Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal conductivity and nanoparticle sedimentation in composite systems. In this work, a composite phase change material (PCM) consisting of paraffin wax, a microencapsulated phase change material (MicroPCM 28D), and nano carbon black is developed to enhance thermal stability and suppress particle sedimentation through increased viscosity of the PCM matrix. Five capsule geometries fabricated by fused filament fabrication (FFF) 3D printing are experimentally investigated under airflow velocities ranging from 0 to 10 m s−1. Wind tunnel experiments with infrared thermography are used to evaluate the thermal response of the PCM capsules. The results show that airflow velocity and capsule geometry strongly influence heat dissipation behavior. Compared with conventional wax composites, the MicroPCM 28D composite capsules reduce peak temperature by approximately 2–4 °C under airflow velocities of 0–10 m/s. These findings provide insights into geometry-regulated convection and stable composite PCM design for lithium-ion battery thermal management systems. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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18 pages, 9292 KB  
Article
Physics-Informed Transformer Using Degradation-Sensitive Indicators for Long-Term State-of-Health Estimation of Lithium-Ion Batteries
by Sang Hoon Park and Seon Hyeog Kim
Batteries 2026, 12(2), 48; https://doi.org/10.3390/batteries12020048 - 1 Feb 2026
Cited by 1 | Viewed by 872
Abstract
Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer [...] Read more.
Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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24 pages, 9446 KB  
Article
Coupling Model and Early-Stage Internal Short Circuits Fault Diagnosis for Gel Electrolyte Lithium-Ion Batteries
by Liye Wang, Jinlong Wu, Chunxiao Ma, Xianzhong Sun, Lifang Wang and Chenglin Liao
Batteries 2026, 12(2), 45; https://doi.org/10.3390/batteries12020045 - 28 Jan 2026
Viewed by 691
Abstract
This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such [...] Read more.
This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such as dendrite formation or mechanical stress. This study provides a detailed analysis of the unique ISCs mechanism in gel-electrolytes, emphasizing the differences between gel-electrolyte and liquid-electrolyte batteries in terms of ion transport dynamics and thermal performance. Based on these characteristics, an electrochemical–thermal–ISC coupling model was developed, and an external short-circuit resistance test was conducted to validate the model’s accuracy. By simulating various ISC states using the coupling model, a comprehensive dataset of battery ISC parameters was obtained, encompassing voltage, current, temperature, SOC, capacity loss, and internal resistance. ISC prediction models were subsequently developed using BP, CNN, and LSTM networks, with a comparative analysis of their prediction accuracy. This research advances the ISC prediction framework for gel-electrolyte batteries and demonstrates the potential of CNN-based models to achieve higher accuracy in fault prediction. Accurate ISC prediction is crucial for ensuring safe battery operation in electric vehicles. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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20 pages, 8145 KB  
Article
State of Health Estimation of Lithium Cobalt Oxide Batteries Based on ARX Identification Across Different Temperatures
by Simone Barcellona, Mattia Ribera, Emanuele Fedele, Pasquale Franzese, Luigi Piegari, Lorenzo Codecasa and Diego Iannuzzi
Batteries 2026, 12(1), 2; https://doi.org/10.3390/batteries12010002 - 20 Dec 2025
Cited by 1 | Viewed by 819
Abstract
Lithium-ion batteries (LiBs) undergo degradation influenced by storage and cycling conditions. Accurate state of health (SOH) assessment is crucial for predicting battery aging, which is generally marked by a decline in capacity (energy fade) or an increase in internal resistance (power fade). This [...] Read more.
Lithium-ion batteries (LiBs) undergo degradation influenced by storage and cycling conditions. Accurate state of health (SOH) assessment is crucial for predicting battery aging, which is generally marked by a decline in capacity (energy fade) or an increase in internal resistance (power fade). This study investigates the impulse response (IR) technique for assessing the SOH of lithium cobalt oxide batteries, addressing both capacity fade and rising internal resistance. The IR method relies on a predefined dataset that records the voltage response of the LiB to pulse current inputs across various states of charge (SOC), temperatures, and aging conditions to train a series of linear auto-regressive exogenous (ARX) models. This dataset is then used as a look-up table for subsequent SOH estimation under new operating conditions. The results demonstrate that the method can capture trends in capacity fade and resistance increase only when multiple battery temperatures are incorporated into the look-up table. In contrast, estimations based on ARX models trained at a single fixed temperature fail to provide reliable predictions of battery SOH. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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25 pages, 4711 KB  
Article
Hybrid Deep Learning Approach for Fractional-Order Model Parameter Identification of Lithium-Ion Batteries
by Maharani Putri, Dat Nguyen Khanh, Kun-Che Ho, Shun-Chung Wang and Yi-Hua Liu
Batteries 2025, 11(12), 452; https://doi.org/10.3390/batteries11120452 - 9 Dec 2025
Viewed by 1128
Abstract
Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict [...] Read more.
Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict battery behavior and estimate critical states such as state of charge (SOC) and state of health (SOH). In this study, a hybrid deep learning approach has been introduced for FOM PI, representing the first application of deep learning in this domain. A simulation platform was developed to generate datasets using Sobol and Monte Carlo sampling methods. Five deep learning models were constructed: long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1DCNN), and hybrid models combining 1DCNN with LSTM and GRU. Hyperparameters were optimized using Optuna, and enhancements such as Huber loss for robustness to outliers, stochastic weight averaging, and exponential moving average for training stability were incorporated. The primary contribution lies in the hybrid architectures, which integrate convolutional feature extraction with recurrent temporal modeling, outperforming standalone models. On a test set of 1000 samples, the improved 1DCNN + GRU model achieved an overall root mean square error (RMSE) of 0.2223 and a mean absolute percentage error (MAPE) of 0.27%, representing nearly a 50% reduction in RMSE compared to its baseline. This performance surpasses that of the improved LSTM model, which yielded a MAPE of 9.50%, as evidenced by tighter scatter plot alignments along the diagonal and reduced error dispersion in box plots. Terminal voltage prediction was validated with an average RMSE of 0.002059 and mean absolute error (MAE) of 0.001387, demonstrating high-fidelity dynamic reconstruction. By advancing data-driven PI, this framework is well-positioned to enable real-time integration into battery management systems. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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27 pages, 2037 KB  
Article
Multi-Objective Sizing Method for PV-BESS Integration with EV Charging Stations and Analysis Across Different Parking Scenarios
by Francesco Maria Tiburtini, Francesco Lo Franco and Mattia Ricco
Batteries 2025, 11(11), 422; https://doi.org/10.3390/batteries11110422 - 17 Nov 2025
Cited by 2 | Viewed by 1893
Abstract
The growing adoption of electric vehicles (EVs) is driving the expansion of charging networks. Local photovoltaic (PV) systems combined with battery energy storage systems (BESS) have emerged as promising solutions to mitigate the impact of charging demand on the grid and reduce the [...] Read more.
The growing adoption of electric vehicles (EVs) is driving the expansion of charging networks. Local photovoltaic (PV) systems combined with battery energy storage systems (BESS) have emerged as promising solutions to mitigate the impact of charging demand on the grid and reduce the environmental impact of EV charging. In this context, proper sizing of PV-BESS systems is crucial to maximize their integration with charging hubs (CHs) and ensure optimal performance. This paper proposes a multi-objective sizing method to optimize the energy and economic performance of PV-BESS systems in EV charging hubs. Sizing optimization is performed using a Non-Dominated Sorting Genetic Algorithm-II. The method is applied to four CH scenarios characterized by variations in energy demand, user behavior, and location. Results indicate that while optimal PV size remains relatively consistent across scenarios, the ideal BESS configuration varies with each scenario’s characteristics. Optimized PV-BESS integration significantly improves energy performance, increasing system self-sufficiency by up to +72%. From an economic point of view, results show that in some cases, smaller BESS capacities are more advantageous due to lower capital costs, while in others, larger BESS sizes reduce overall costs by up to −50%, significantly cutting utility expenses despite higher initial investment. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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15 pages, 969 KB  
Article
Techno-Economic and Environmental Viability of Second-Life EV Batteries in Commercial Buildings: An Analysis Using Real-World Data
by Zhi Cao, Naser Vosoughi Kurdkandi and Chris Mi
Batteries 2025, 11(11), 412; https://doi.org/10.3390/batteries11110412 - 7 Nov 2025
Cited by 5 | Viewed by 2894
Abstract
The rapid growth of electric vehicle markets is producing large volumes of retired lithium-ion batteries retaining 70–80% of their original capacity, suitable for stationary energy storage. This study assesses the techno-economic and environmental viability of second-life battery energy storage systems (SLBESS) in a [...] Read more.
The rapid growth of electric vehicle markets is producing large volumes of retired lithium-ion batteries retaining 70–80% of their original capacity, suitable for stationary energy storage. This study assesses the techno-economic and environmental viability of second-life battery energy storage systems (SLBESS) in a California commercial building, using one year of operational data. SLBESS performance is compared with equivalent new battery systems under identical dispatch strategies, building load profiles, and time-of-use tariff structures. A dispatch-aware framework integrates multi-year battery simulations, degradation modeling, electricity cost analysis, and life cycle assessment based on marginal grid emissions. The economic analysis quantifies the net present value (NPV), internal rate of return (IRR), and operational levelized cost of storage (LCOSop). Results show that SLBESS achieve 49.2% higher NPV, 41.9% higher IRR, and 13.8% lower LCOSop than new batteries, despite their lower round-trip efficiency. SLBESS reduce embodied emissions by 41% and achieve 8% lower carbon intensity than new batteries. Sensitivity analysis identifies that economic outcomes are driven primarily by financial parameters (incentives, acquisition cost) rather than technical factors (degradation, initial health), providing a clear rationale for policies that reduce upfront costs. Environmentally, grid emission factors are the dominant driver. Battery degradation rate and initial state of health have minimal impact, suggesting that technical concerns may be overstated. These findings provide actionable insights for deploying cost-effective, low-carbon storage in commercial buildings. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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10 pages, 1979 KB  
Article
A Novel Approach to Day-Ahead Forecasting of Battery Discharge Profiles in Grid Applications Using Historical Daily
by Marek Bobček, Róbert Štefko, Július Šimčák and Zsolt Čonka
Batteries 2025, 11(10), 370; https://doi.org/10.3390/batteries11100370 - 6 Oct 2025
Viewed by 806
Abstract
This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are [...] Read more.
This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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Review

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32 pages, 5012 KB  
Review
A Review of Modelling, State of Charge Estimation and Management Methods of EV Lithium-Ion Batteries
by Moayad Albakri and Ahmed Darwish
Batteries 2026, 12(3), 92; https://doi.org/10.3390/batteries12030092 - 8 Mar 2026
Cited by 1 | Viewed by 1409
Abstract
Electric Vehicles (EVs) can contribute significantly to reducing greenhouse gas emissions and addressing climate change problems. Modern EVs are primarily powered by electrochemical batteries such as lead-acid (Pb-acid), nickel-metal hydride (NiMH), sodium-ion (Na-ion), solid-state and lithium-ion (Li-ion) batteries. When compared to other battery [...] Read more.
Electric Vehicles (EVs) can contribute significantly to reducing greenhouse gas emissions and addressing climate change problems. Modern EVs are primarily powered by electrochemical batteries such as lead-acid (Pb-acid), nickel-metal hydride (NiMH), sodium-ion (Na-ion), solid-state and lithium-ion (Li-ion) batteries. When compared to other battery types, Li-ion batteries are the most suitable for EV applications due to their practical features such as their high energy density, high charging and discharging efficiency and extended lifetime. However, the main risk of Li-ion batteries is that they are exposed to thermal runaway phenomena, which raises severe concerns about the safety of EV propulsion systems. Thermal runaways should be considered carefully as they cannot be stopped once they start and can lead to battery explosion. One of the main reasons leading to this phenomenon is abusing the state of charge (SoC) of the battery. Therefore, the battery management system (BMS) plays a crucial role in mitigating the stimulation of the thermal runaway process by accurately estimating and properly managing the battery cells. To help researchers and designers with understanding this matter, this paper proposes a review of the most effective SoC estimation methods for EV Li-ion batteries and links these methods with practical energy management systems in the EV market. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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