Recent Advances in Battery Management Systems

A topical collection in Batteries (ISSN 2313-0105). This collection belongs to the section "Battery Modelling, Simulation, Management and Application".

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Department of Electrical Engineering, Singapore Institute of Technology, 10 Dover Drive, Singapore 138682, Singapore
Interests: battery; engineering; electrical and electronics; energy storage; energy conversion; energy
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Topical Collection Information

Dear Colleagues,

Four major pillars drive advances in battery energy storage: (1) materials science and engineering, including electrochemistry, which enables new battery types and variants to produce a better performance at the cell level; (2) battery design and manufacturing technology, which enables reliable and cost-effective battery modules and packs; (3) battery management systems, which enable the safe and effective operation with an optimum life-cycle cost; and lastly (4) application technologies, which generate the demand and requirements for battery systems. This particular topical collection shall focus on the Battery Management System (BMS). A BMS enables a battery system to be smart, which is important to maximize the value of the battery energy storage system. The functions of a BMS are ever-growing but typically involve many of the following:

  • Monitoring of the battery states and variables
  • Controlling the charging and discharging profiles
  • Protecting the battery and ensuring safe operating regions
  • Balancing of battery cells and modules
  • Thermal management
  • Health prognosis and predictive maintenance
  • Communications to the external environment
  • Application-specific management functions

BMS technology varies in complexity and capabilities. Their topologies may be centralized, distributed, or modular. Some BMSs employ edge processing, while some incorporate computational and machine intelligence and are capable of learning.

I welcome you to contribute your articles to this important topic of Advances in Battery Management Systems.

Prof. Dr. King Jet Tseng
Collection Editor

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 collection 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 management system
  • state of charge
  • state of health
  • monitoring
  • protection
  • thermal management
  • balancing
  • communications
  • predictive maintenance

Published Papers (9 papers)

2024

Jump to: 2023, 2022

16 pages, 3310 KiB  
Article
A Study of Thermal Runaway Mechanisms in Lithium-Ion Batteries and Predictive Numerical Modeling Techniques
by Alexander Sorensen, Vivek Utgikar and Jeffrey Belt
Batteries 2024, 10(4), 116; https://doi.org/10.3390/batteries10040116 - 29 Mar 2024
Viewed by 819
Abstract
While thermal runaway characterization and prediction is an important aspect of lithium-ion battery engineering and development, it is a requirement to ensure that a battery system can be safe under normal operations and during failure events. This study investigated the current existing literature [...] Read more.
While thermal runaway characterization and prediction is an important aspect of lithium-ion battery engineering and development, it is a requirement to ensure that a battery system can be safe under normal operations and during failure events. This study investigated the current existing literature regarding lithium-ion battery thermal runaway characterization and predictive modeling methods. A thermal model for thermal runaway prediction was adapted from the literature and is presented in this paper along with a comparison of empirical data and predicted data using the model. Empirical data were collected from a Samsung 30Q 18650 cylindrical cell and from a large 20 Ah pouch cell format using accelerated rate calorimetry. The predictive model was executed in a macro-enabled Microsoft Excel workbook for simplicity and accessibility for the public. The primary purpose of using more primitive modeling software was to provide an accurate model that was generally accessible without the purchase of or training in a specific modeling software package. The modes of heat transfer during the thermal runaway event were studied and are reported in this work, along with insights on thermal management during a thermal runaway failure event. Full article
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2023

Jump to: 2024, 2022

28 pages, 1393 KiB  
Article
Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes
by Steven Neupert and Julia Kowal
Batteries 2023, 9(7), 364; https://doi.org/10.3390/batteries9070364 - 07 Jul 2023
Cited by 2 | Viewed by 2141
Abstract
State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the [...] Read more.
State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations. Full article
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30 pages, 5730 KiB  
Article
End-to-End Direct-Current-Based Extreme Fast Electric Vehicle Charging Infrastructure Using Lithium-Ion Battery Storage
by Vishwas Powar and Rajendra Singh
Batteries 2023, 9(3), 169; https://doi.org/10.3390/batteries9030169 - 14 Mar 2023
Cited by 6 | Viewed by 3521
Abstract
An urgent need to decarbonize the surface transport sector has led to a surge in the electrification of passenger and heavy-duty fleet vehicles. The lack of widespread public charging infrastructure hinders this electric vehicle (EV) transition. Extreme fast charging along interstates and highway [...] Read more.
An urgent need to decarbonize the surface transport sector has led to a surge in the electrification of passenger and heavy-duty fleet vehicles. The lack of widespread public charging infrastructure hinders this electric vehicle (EV) transition. Extreme fast charging along interstates and highway corridors is a potential solution. However, the legacy power grid based on alternating current (AC) beckons for costly upgrades that will be necessary to sustain sporadic fast charging loads. The primary goal of this paper is to propose a sustainable, low-loss, extremely fast charging infrastructure based on photovoltaics (PV) and co-located lithium-ion battery storage (BESS). Lithium-ion BESS plays a pivotal role in our proposed design by mitigating demand charges and operating as an independent 16–18 h power source. An end-to-end direct current power network with high voltage direct current interconnection is also incorporated. The design methodology focuses on comprehensive hourly EV-load models generated for different types of passenger vehicles and heavy-duty fleet charging. Appropriate PV-BESS sizing, optimum tilt, and temperature compensation techniques based on 15 years of irradiation data were utilized in the design. The proposed grid-independent DC power networks can significantly improve well-to-wheels efficiency by minimizing total system losses for fast charging networks. The network power savings for low, medium, and high voltage use cases were evaluated. Our results demonstrate 17% to 25% power savings compared to the traditional AC case. Full article
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16 pages, 914 KiB  
Article
Optimal Capacity and Cost Analysis of Battery Energy Storage System in Standalone Microgrid Considering Battery Lifetime
by Pinit Wongdet, Terapong Boonraksa, Promphak Boonraksa, Watcharakorn Pinthurat, Boonruang Marungsri and Branislav Hredzak
Batteries 2023, 9(2), 76; https://doi.org/10.3390/batteries9020076 - 23 Jan 2023
Cited by 10 | Viewed by 3538
Abstract
In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because [...] Read more.
In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because the BESS has a limited lifespan and is the most expensive component in a microgrid, frequent replacement significantly increases a project’s operating costs. This paper proposes a capacity optimization method as well as a cost analysis that takes the BESS lifetime into account. The weighted Wh throughput method is used in this paper to estimate the BESS lifetime. Furthermore, the well-known Particle Swarm Optimization (PSO) algorithm is employed to maximize battery capacity while minimizing the total net present value. According to simulation results, the optimal adjusting factor of 1.761 yields the lowest total net present value of US$200,653. The optimal capacity of the BESS can significantly reduce the net present value of total operation costs throughout the project by extending its lifetime. When applied to larger power systems, the proposed strategy can further reduce total costs. Full article
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16 pages, 16492 KiB  
Article
Data-Driven Thermal Anomaly Detection in Large Battery Packs
by Kiran Bhaskar, Ajith Kumar, James Bunce, Jacob Pressman, Neil Burkell and Christopher D. Rahn
Batteries 2023, 9(2), 70; https://doi.org/10.3390/batteries9020070 - 18 Jan 2023
Cited by 7 | Viewed by 3693
Abstract
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery [...] Read more.
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate. Full article
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2022

Jump to: 2024, 2023

18 pages, 6508 KiB  
Article
Physics-Based SoH Estimation for Li-Ion Cells
by Pietro Iurilli, Claudio Brivio, Rafael E. Carrillo and Vanessa Wood
Batteries 2022, 8(11), 204; https://doi.org/10.3390/batteries8110204 - 01 Nov 2022
Cited by 11 | Viewed by 3841
Abstract
Accurate state of health (SoH) estimation is crucial to optimize the lifetime of Li-ion cells while ensuring safety during operations. This work introduces a methodology to track Li-ion cells degradation and estimate SoH based on electrochemical impedance spectroscopy (EIS) measurements. Distribution of relaxation [...] Read more.
Accurate state of health (SoH) estimation is crucial to optimize the lifetime of Li-ion cells while ensuring safety during operations. This work introduces a methodology to track Li-ion cells degradation and estimate SoH based on electrochemical impedance spectroscopy (EIS) measurements. Distribution of relaxation times (DRT) were exploited to derive indicators linked to the so-called degradation modes (DMs), which group the different aging mechanisms. The combination of these indicators was used to model the aging progression over the whole lifetime (both in the “pre-knee” and “after-knee” regions), enabling a physics-based SoH estimation. The methodology was applied to commercial cylindrical cells (NMC811|Graphite SiOx). The results showed that loss of lithium inventory (LLI) is the main driving factor for cell degradation, followed by loss of cathode active material (LAMC). SoH estimation was achievable with a mean absolute error lower than 0.75% for SoH values higher than 85% and lower than 3.70% SoH values between 85% and 80% (end of life). The analyses of the results will allow for guidelines to be defined to replicate the presented methodology, characterize new Li-ion cell types, and perform onboard SoH estimation in battery management system (BMS) solutions. Full article
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18 pages, 4068 KiB  
Article
A Novel Synchronized Multiple Output DC-DC Converter Based on Hybrid Flyback-Cuk Topologies
by Khaled A. Mahafzah, Mohammad A. Obeidat, Ali Q. Al-Shetwi and Taha Selim Ustun
Batteries 2022, 8(8), 93; https://doi.org/10.3390/batteries8080093 - 15 Aug 2022
Cited by 11 | Viewed by 3110
Abstract
This paper proposes a new hybrid flyback-Cuk (HFC) converter. The new converter consists of a single switch, a single isolated input, and dual output based on flyback and Cuk topologies. The new HFC topology is proposed to reduce switching losses and improve the [...] Read more.
This paper proposes a new hybrid flyback-Cuk (HFC) converter. The new converter consists of a single switch, a single isolated input, and dual output based on flyback and Cuk topologies. The new HFC topology is proposed to reduce switching losses and improve the duty cycle range over which voltage can be stepped down, which would ultimately lead to an increase in efficiency. For step-down capability, the traditional single topologies (flyback or Cuk) require a less than 50% duty cycle. The low duty cycle of conventional converters leads to low operational efficiency. Therefore, the developed HFC can operate at a duty cycle of up to 85% for the same capability. The analysis, derivations, design, and simulation of the proposed HFC are thoroughly discussed for two different applications at two different power levels. The simulation results are obtained using MATLAB 2020a. The developed HFC’s efficiency as a function of the duty cycle is plotted, which reaches 89%, representing a significant efficiency improvement. The proposed converter can supply and absorb power simultaneously, giving it a significant edge over other converters. It is suitable for energy conversion and storage systems, such as renewable energy systems and electric vehicles (EV). To show the effectiveness and validate the new topology proposed, an EV along with battery energy storage (BES), is applied to charge (EV) and recharge (BES) simultaneously. The simulation results of 1.5 kW of HFC-PFC over the universal voltage range show that the proposed HFC can achieve a high power factor up to 97.5% at 260 Vrms. Moreover, the total harmonics distortion is measured between 36.25 and 27.69%. Thus, the results can achieve all required functions efficiently with minimum losses at a high range of duty cycles. Full article
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16 pages, 4112 KiB  
Article
Model for Rating a Vanadium Redox Flow Battery Stack through Constant Power Charge–Discharge Characterization
by Pavan Kumar Vudisi, Sreenivas Jayanti and Raghuram Chetty
Batteries 2022, 8(8), 85; https://doi.org/10.3390/batteries8080085 - 09 Aug 2022
Cited by 5 | Viewed by 2743
Abstract
A method for estimating the stack rating of vanadium redox flow batteries (VRFBs) through constant power characterization was developed. A stack of 22 cells, each with 1500 cm2 of nominal electrode area, was constructed and tested using constant current and constant power [...] Read more.
A method for estimating the stack rating of vanadium redox flow batteries (VRFBs) through constant power characterization was developed. A stack of 22 cells, each with 1500 cm2 of nominal electrode area, was constructed and tested using constant current and constant power protocols. Typical ratios of charging to discharging power that prevail in various applications (e.g., peak shaving, wind power/solar photovoltaic power integration) were employed in the test protocols. The results showed that fractional energy storage capacity utilization and round-trip energy efficiency varied linearly with the power at which the energy was charged or discharged. A zero-dimensional electrochemical model was proposed based on the area-specific resistance to account for the energy stored/extracted during constant power discharge in the state of charge (SoC) window of 20% to 80%. It was shown that this could be used to rate a given stack in terms of charging and discharging power from the point of view of its application as a power unit. The proposed method enables stack rating based on a single polarization test and can be extended to flow battery systems in general. Full article
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15 pages, 3366 KiB  
Article
Characteristics of Open Circuit Voltage Relaxation in Lithium-Ion Batteries for the Purpose of State of Charge and State of Health Analysis
by David Theuerkauf and Lukas Swan
Batteries 2022, 8(8), 77; https://doi.org/10.3390/batteries8080077 - 26 Jul 2022
Cited by 10 | Viewed by 7405
Abstract
Open circuit voltage relaxation to a steady state value occurs, and is measured, at the terminals of a lithium-ion battery when current stops flowing. It is of interest for use in determining state of charge and state of health. As voltage relaxation can [...] Read more.
Open circuit voltage relaxation to a steady state value occurs, and is measured, at the terminals of a lithium-ion battery when current stops flowing. It is of interest for use in determining state of charge and state of health. As voltage relaxation can take several hours, a representative model and curve fitting is necessary for practical usage. Previous studies of lithium-ion voltage relaxation investigate four characteristics: relationship between voltage relaxation magnitude and state of charge; length of relaxation required; model complexity for state of charge estimation; and model complexity for state of health evaluation. However, previous studies have inconsistent methodology or use only one type of lithium-ion cell, making comparison and generalization difficult. To address this, we conducted 3 h and 24 h voltage relaxation experiments over a range of states of charge on three different lithium ion chemistries (nickel cobalt aluminum NCA; nickel manganese cobalt NMC532; lithium iron phosphate LFP) and fitted them with a new voltage relaxation equivalent circuit model. It was found that a 3 h relaxation period was sufficient for NMC and LFP for state of charge and state of health investigations. Voltage relaxation of the NCA cell continued to evolve past 24 h. It was shown that voltage relaxation shape and magnitude changes as a function of state of charge, and the accuracy of estimating state of charge was explored. Strategically choosing a state of charge for state of health assessment can be optimized to accentuate voltage relaxation magnitude and this differs by chemistry. This suggested technique and experimental findings can be paired with battery degradation studies to determine accuracy of assessing state of health. Full article
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