Batteries

17 pages, 4381 KiB

Open AccessArticle

An Investigation into Electrolytes and Cathodes for Room-Temperature Sodium–Sulfur Batteries

by Hakeem Ademola Adeoye, Stephen Tennison, John F. Watts and Constantina Lekakou

Batteries 2024, 10(6), 216; https://doi.org/10.3390/batteries10060216 - 20 Jun 2024

Viewed by 552

In the pursuit of high energy density batteries beyond lithium, room-temperature (RT) sodium–sulfur (Na-S) batteries are studied, combining sulfur, as a high energy density active cathode material and a sodium anode considered to offer high energy density and very good standard potential. Different [...] Read more.

In the pursuit of high energy density batteries beyond lithium, room-temperature (RT) sodium–sulfur (Na-S) batteries are studied, combining sulfur, as a high energy density active cathode material and a sodium anode considered to offer high energy density and very good standard potential. Different liquid electrolyte systems, including three different salts and two different solvents, are investigated in RT Na-S battery cells, on the basis of the solubility of sulfur and sulfides, specific capacity, and cyclability of the cells at different C-rates. Two alternative cathode host materials are explored: A bimodal pore size distribution activated carbon host AC MSC30 and a highly conductive carbon host of hollow particles with porous particle walls. An Na-S cell with a cathode coating with 44 wt% sulfur in the AC MSC30 host and the electrolyte 1M NaFSI in DOL/DME exhibited a specific capacity of 435 mAh/g_S but poor cyclability. An Na-S cell with a cathode coating with 44 wt% sulfur in the host of hollow porous particles and the electrolyte 1M NaTFSI in TEGDME exhibited a specific capacity of 688 mAh/g_S. Full article

(This article belongs to the Special Issue High-Performance Materials for Sodium-Ion Batteries)

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20 pages, 10524 KiB

Open AccessArticle

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

by Vijayakanthan Damodaran, Thiyagarajan Paramadayalan, Diwakar Natarajan, Ramesh Kumar C, P. Rajesh Kanna, Dawid Taler, Tomasz Sobota, Jan Taler, Magdalena Szymkiewicz and Mohammed Jalal Ahamed

Batteries 2024, 10(6), 215; https://doi.org/10.3390/batteries10060215 - 19 Jun 2024

Viewed by 718

Abstract

Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used [...] Read more.

Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used by the BMS to estimate the battery state of charge (SOC), which is crucial for efficient charging/discharging, range calculations, and the overall safe operation of electric vehicles. Typically, the ECM approach represents the dynamic characteristics of the battery in a mathematical form with a limited number of unknown parameters. Then, the parameters are calculated from voltage and current information of the lithium-ion cell obtained from controlled experiments. In the current work, a faster and simplified first-order resistance–capacitance (RC) equivalent circuit model was developed for a commercial cylindrical cell (LGM50 21700). An analytical solution was developed for the equivalent circuit model incorporating SOC and temperature-dependent RC parameters. The solution to the RC circuit model was derived using multiple expressions for different components like open circuit voltage (OCV), instantaneous resistance (R₀), and diffusional parameters (R₁ and C₁) as a function of the SOC and operating temperature. The derived parameters were validated against the virtual HPPC test results of a validated physics-based electrochemical model for the voltage behavior. Using the developed RC circuit model, a polynomial expression is derived to estimate the temperature increase of the cell including both irreversible and reversible heat generation components. The temperature predicted by the proposed RC circuit model at different battery operating temperatures is in good agreement with the values obtained from the validated physics model. The developed method can find applications in (i) onboard energy management by the BMS and (ii) quicker evaluation of cell performance early in the product development cycle. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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26 pages, 31759 KiB

Open AccessArticle

Rule-Based Operation Mode Control Strategy for the Energy Management of a Fuel Cell Electric Vehicle

by Jokin Uralde, Oscar Barambones, Asier del Rio, Isidro Calvo and Eneko Artetxe

Batteries 2024, 10(6), 214; https://doi.org/10.3390/batteries10060214 - 19 Jun 2024

Viewed by 768

Abstract

Hydrogen, due to its high energy density, stands out as an energy storage method for the car industry in order to reduce the impact of the automotive sector on air pollution and global warming. The fuel cell electric vehicle (FCEV) emerges as a [...] Read more.

Hydrogen, due to its high energy density, stands out as an energy storage method for the car industry in order to reduce the impact of the automotive sector on air pollution and global warming. The fuel cell electric vehicle (FCEV) emerges as a modification of the electric car by adding a proton exchange membrane fuel cell (PEMFC) to the battery pack and electric motor, that is capable of converting hydrogen into electric energy. In order to control the energy flow of so many elements, an optimal energy management system (EMS) is needed, where rule-based strategies represent the smallest computational burden and are the most widely used in the industry. In this work, a rule-based operation mode control strategy for the EMS of an FCEV validated by different driving cycles and several tests at the strategic points of the battery state of charge (SOC) is proposed. The results obtained in the new European driving cycle (NEDC) show the 12 kW battery variation of 2% and a hydrogen consumption of 1.2 kg/100 km compared to the variation of 1.42% and a consumption of 1.08 kg/100 km obtained in the worldwide harmonized light-duty test cycle (WLTC). Moreover, battery tests have demonstrated the optimal performance of the proposed EMS strategy. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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17 pages, 6590 KiB

Open AccessArticle

Water/N,N-Dimethylacetamide-Based Hybrid Electrolyte and Its Application to Enhanced Voltage Electrochemical Capacitors

by Aleksandra A. Mroziewicz, Karolina Solska, Grażyna Zofia Żukowska and Magdalena Skunik-Nuckowska

Batteries 2024, 10(6), 213; https://doi.org/10.3390/batteries10060213 - 19 Jun 2024

Viewed by 444

Abstract

The growing interest in hybrid (aqueous–organic) electrolytes for electrochemical energy storage is due to their wide stability window, improved safety, and ease of assembly that does not require a moisture-free atmosphere. When it comes to applications in electrochemical capacitors, hybrid electrolytes are expected [...] Read more.

The growing interest in hybrid (aqueous–organic) electrolytes for electrochemical energy storage is due to their wide stability window, improved safety, and ease of assembly that does not require a moisture-free atmosphere. When it comes to applications in electrochemical capacitors, hybrid electrolytes are expected to fill the gap between high-voltage organic systems and their high discharge rate aqueous counterparts. This article discusses the potential applicability of aqueous–organic electrolytes utilizing water/N,N-dimethylacetamide (DMAc) solvent mixture, and sodium perchlorate as a source of charge carriers. The hydrogen bond formation between H₂O and DMAc (mole fraction x_DMAc = 0.16) is shown to regulate the original water and cation solvation structure, thus reducing the electrochemical activity of the primary aqueous solution both in the hydrogen (HER) and oxygen (OER) evolution reactions region. As a result, an electrochemical stability window of 3.0 V can be achieved on titanium electrodes while providing reasonable ionic conductivity of 39 mS cm⁻¹ along with the electrolyte’s flame retardant and anti-freezing properties. Based on the diagnostic electrochemical studies, the operation conditions for carbon/carbon capacitors have been carefully optimized to adjust the potential ranges of the individual electrodes to the electrochemical stability region. The system with the appropriate electrode mass ratio (m₊/m₋ = 1.51) was characterized by a wide operating voltage of 2.0 V, gravimetric energy of 13.2 Wh kg⁻¹, and practically a 100% capacitance retention after 10,000 charge–discharge cycles. This translates to a significant rise in the maximum energy of 76% when compared to the aqueous counterpart. Additionally, reasonable charge–discharge rates and anti-freeze properties of the developed electrolyte enable application in a broad temperature range down to −20 °C, which is demonstrated as well. Full article

(This article belongs to the Special Issue Novel Electrolytes for Batteries and Supercapacitors)

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27 pages, 3355 KiB

Open AccessArticle

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

by Sirote Khunkitti, Natsawat Pompern, Suttichai Premrudeepreechacharn and Apirat Siritaratiwat

Batteries 2024, 10(6), 212; https://doi.org/10.3390/batteries10060212 - 18 Jun 2024

Viewed by 405

Abstract

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and [...] Read more.

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations. Full article

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18 pages, 7566 KiB

Open AccessArticle

Experiment-Based Determination of Optimal Parameters in Constant Temperature–Constant Voltage Charging Technique for Lithium-Ion Batteries Using Taguchi Method

by Yu-Shan Cheng, Su-Fen Lin and Kun-Che Ho

Batteries 2024, 10(6), 211; https://doi.org/10.3390/batteries10060211 - 18 Jun 2024

Viewed by 411

Abstract

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation [...] Read more.

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. Full article

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11 pages, 2655 KiB

Open AccessArticle

Effects of Electrolyte Solvent Composition on Solid Electrolyte Interphase Properties in Lithium Metal Batteries: Focusing on Ethylene Carbonate to Ethyl Methyl Carbonate Ratios

by Paul Maldonado Nogales, Sangyup Lee, Seunga Yang and Soon-Ki Jeong

Batteries 2024, 10(6), 210; https://doi.org/10.3390/batteries10060210 - 16 Jun 2024

Viewed by 613

Abstract

This study investigated the influence of variations in the mixing ratio of ethylene carbonate (EC) to ethyl methyl carbonate (EMC) on the composition and effectiveness of the solid electrolyte interphase (SEI) in lithium-metal batteries. The SEI is crucial for battery performance, as it [...] Read more.

This study investigated the influence of variations in the mixing ratio of ethylene carbonate (EC) to ethyl methyl carbonate (EMC) on the composition and effectiveness of the solid electrolyte interphase (SEI) in lithium-metal batteries. The SEI is crucial for battery performance, as it prevents continuous electrolyte decomposition and inhibits the growth of lithium dendrites, which can cause internal short circuits leading to battery failure. Although the properties of the SEI largely depend on the electrolyte solvent, the influence of the EC:EMC ratio on SEI properties has not yet been elucidated. Through electrochemical testing, ionic conductivity measurements, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy, the formation of Li₂CO₃, LiF, and organolithium compounds on lithium surfaces was systematically analyzed. This study demonstrated that the EC:EMC ratio significantly affected the SEI structure, primarily owing to the promotion of the formation of a denser SEI layer. Specifically, the ratios of 1:1 and 1:3 facilitated a uniform distribution and prevalence of Li₂CO₃ and LiF throughout the SEI, thereby affecting cell performance. Thus, precise control of the EC:EMC ratio is essential for enhancing the mechanical robustness and electrochemical stability of the SEI, thereby providing valuable insights into the factors that either enhance or impede effective SEI formation. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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14 pages, 2628 KiB

Open AccessArticle

Study of the Suitability of Corncob Biochar as Electrocatalyst for Zn–Air Batteries

by Nikolaos Soursos, Theodoros Kottis, Vasiliki Premeti, John Zafeiropoulos, Katerina Govatsi, Lamprini Sygellou, John Vakros, Ioannis D. Manariotis, Dionissios Mantzavinos and Panagiotis Lianos

Batteries 2024, 10(6), 209; https://doi.org/10.3390/batteries10060209 - 16 Jun 2024

Viewed by 589

Abstract

There has been a recent increasing interest in Zn–air batteries as an alternative to Li-ion batteries. Zn–air batteries possess some significant advantages; however, there are still problems to solve, especially related to the tuning of the properties of the air–cathode which should carry [...] Read more.

There has been a recent increasing interest in Zn–air batteries as an alternative to Li-ion batteries. Zn–air batteries possess some significant advantages; however, there are still problems to solve, especially related to the tuning of the properties of the air–cathode which should carry an inexpensive but efficient bifunctional oxygen reduction (ORR) and oxygen evolution (OER) reaction electrocatalyst. Biochar can be an alternative, since it is a material of low cost, it exhibits electric conductivity, and it can be used as support for transition metal ions. Although there is a significant number of publications on biochars, there is a lack of data about biochar from raw biomass rich in hemicellulose, and biochar with a small number of heteroatoms, in order to report the pristine activity of the carbon phase. In this work, activated biochar has been made by using corncobs. The biomass was first dried and minced into small pieces and pyrolyzed. Then, it was mixed with KOH and pyrolyzed for a second time. The final product was characterized by various techniques and its electroactivity as a cathode was determined. Physicochemical characterization revealed that the biochar had a hierarchical pore structure, moderate surface area of 92 m² g⁻¹, carbon phase with a relatively low sp²/sp³ ratio close to one, and a limited amount of N and S, but a high number of oxygen groups. The graphitization was not complete while the biochar had an ordered structure and contained significant O species. This biochar was used as an electrocatalyst for ORR and OER in Zn–air batteries where it demonstrated a satisfactory performance. More specifically, it reached an open-circuit voltage of about 1.4 V, which was stable over a period of several hours, with a short-circuit current density of 142 mA cm⁻² and a maximum power density of 55 mW cm⁻². Charge–discharge cycling of the battery was achieved between 1.2 and 2.1 V for a constant current of 10 mA. These data show that corncob biochar demonstrated good performance as an electrocatalyst in Zn–air batteries, despite its low specific surface and low sp²/sp³ ratio, owing to its rich oxygen sites, thus showing that electrocatalysis is a complex phenomenon and can be served by biochars of various origins. Full article

(This article belongs to the Special Issue Emerging Materials and Technologies for Post-Lithium-Ion Batteries—2nd Edition)

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15 pages, 3662 KiB

Open AccessArticle

State of Charge Estimation of Lithium-ion Batteries Based on Online OCV Curve Construction

by Xuemei Wang, Ruiyun Gong, Zhao Yang and Longyun Kang

Batteries 2024, 10(6), 208; https://doi.org/10.3390/batteries10060208 - 16 Jun 2024

Viewed by 624

Abstract

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly [...] Read more.

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method’s ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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13 pages, 9735 KiB

Open AccessArticle

Low-Temperature-Tolerant Aqueous Proton Battery with Porous Ti₃C₂T_x MXene Electrode and Phosphoric Acid Electrolyte

by Jun Zhu, Xude Li, Bingqing Hu, Shanhai Ge and Jiang Xu

Batteries 2024, 10(6), 207; https://doi.org/10.3390/batteries10060207 - 14 Jun 2024

Viewed by 511

Abstract

Supercapacitors have long suffered from low energy density. Here, we present a high-energy, high-safety, and temperature-adaptable aqueous proton battery utilizing two-dimensional Ti₃C₂T_x MXenes as anode materials. Additionally, our work aims to provide further insights into the energy storage [...] Read more.

Supercapacitors have long suffered from low energy density. Here, we present a high-energy, high-safety, and temperature-adaptable aqueous proton battery utilizing two-dimensional Ti₃C₂T_x MXenes as anode materials. Additionally, our work aims to provide further insights into the energy storage mechanism of Ti₃C₂T_x in acid electrolytes. Our findings reveal that the ion transport mechanism of Ti₃C₂T_x remains consistent in both H₂SO₄ and H₃PO₄ electrolytes. The mode of charge transfer depends on its terminal groups. Specifically, the hydrogen bonding network formed by water molecules adsorbed by hydroxyl functional groups under van der Waals forces enables charge transfer in the form of naked H⁺ through the Grotthuss mechanism. In contrast, the hydrophobic channel formed by oxygen and halogen terminal groups facilitates rapid charge transfers in the form of hydronium ion via the vehicle mechanism, owing to negligible interfacial effect. Herein, we propose an aqueous proton battery based on porous hydroxy-poor Ti₃C₂T_x MXene anode and pre-protonated Cu^II[Fe^III(CN)₆]_2/3∙4H₂O (H-TBA) cathode in a 9.5 M H₃PO₄ solution. This proton battery operates through hydrated H⁺/H⁺ transfer, leading to good electrochemical performance, as evidenced by 26 Wh kg⁻¹ energy density and 162 kW kg⁻¹ power density at room temperature and an energy density of 17 Wh kg⁻¹ and a power density of 7.4 kW kg⁻¹ even at −60 °C. Full article

(This article belongs to the Special Issue Research on Aqueous Rechargeable Batteries)

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16 pages, 804 KiB

Open AccessArticle

A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves

by Mano Schmitz and Julia Kowal

Batteries 2024, 10(6), 206; https://doi.org/10.3390/batteries10060206 - 14 Jun 2024

Viewed by 473

Abstract

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based [...] Read more.

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a one-dimensional convolutional and long short-term memory neural network, and processes time, voltage, and incremental capacity of partial charging curves as time series. The model is cross-validated on different ageing scenarios, reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real-world application. Full article

(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)

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12 pages, 4328 KiB

Open AccessArticle

A Novel Reaction Rate Parametrization Method for Lithium-Ion Battery Electrochemical Modelling

by Alain Goussian, Loïc Assaud, Issam Baghdadi, Cédric Nouillant and Sylvain Franger

Batteries 2024, 10(6), 205; https://doi.org/10.3390/batteries10060205 - 14 Jun 2024

Viewed by 591

Abstract

To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at [...] Read more.

To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at different scales remains a field of research that does not provide consensus for each particular model or approach. Parametrization as part of the modelling process appears to be one of the issues when it comes to building a high-fidelity model of a target cell. In this paper, a particular parameter identification is therefore discussed. Indeed, even if Butler–Volmer is a well-known equation in the electrochemistry field, identification of its reaction rate constant or exchange current density parameters is lacking in the literature. Thus, we discuss the process described in the literature and propose a new protocol that expects to overcome certain difficulties whereas the hypothesis of calculation and measurement maintains high sensitivity. Full article

(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)

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43 pages, 6707 KiB

Open AccessReview

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

by Seyed Saeed Madani, Carlos Ziebert, Parisa Vahdatkhah and Sayed Khatiboleslam Sadrnezhaad

Batteries 2024, 10(6), 204; https://doi.org/10.3390/batteries10060204 - 13 Jun 2024

Viewed by 808

Abstract

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management [...] Read more.

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring. Full article

(This article belongs to the Special Issue Enhancement of Lithium-Ion and Post-lithium Batteries Safety: Fundamentals, Materials and Applications)

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2 pages, 164 KiB

Open AccessCorrection

Correction: Mirandona-Olaeta et al. Ionic Liquid-Laden Zn-MOF-74-Based Solid-State Electrolyte for Sodium Batteries. Batteries 2023, 9, 588

by Alexander Mirandona-Olaeta, Eider Goikolea, Senentxu Lanceros-Mendez, Arkaitz Fidalgo-Marijuan and Idoia Ruiz de Larramendi

Batteries 2024, 10(6), 203; https://doi.org/10.3390/batteries10060203 - 13 Jun 2024

Viewed by 281

Abstract

The authors wish to make the following corrections to their paper [...] Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

31 pages, 13301 KiB

Open AccessCase Report

The Long-Term Usage of an Off-Grid Photovoltaic System with a Lithium-Ion Battery-Based Energy Storage System on High Mountains: A Case Study in Paiyun Lodge on Mt. Jade in Taiwan

by Hsien-Ching Chung

Batteries 2024, 10(6), 202; https://doi.org/10.3390/batteries10060202 - 13 Jun 2024

Viewed by 1586

Abstract

Energy supply on high mountains remains an open issue since grid connection is not feasible. In the past, diesel generators with lead–acid battery energy storage systems (ESSs) were applied in most cases. Recently, photovoltaic (PV) systems with lithium-ion (Li-ion) battery ESSs have become [...] Read more.

Energy supply on high mountains remains an open issue since grid connection is not feasible. In the past, diesel generators with lead–acid battery energy storage systems (ESSs) were applied in most cases. Recently, photovoltaic (PV) systems with lithium-ion (Li-ion) battery ESSs have become suitable for solving this problem in a greener way. In 2016, an off-grid PV system with a Li-ion battery ESS was installed in Paiyun Lodge on Mt. Jade (the highest lodge in Taiwan). After operating for more than 7 years, the aging of the whole electric power system became a critical issue for its long-term usage. In this work, a method is established for analyzing the massive energy data (over 7 million rows), such as daily operation patterns, as well as the C-rate, temperature, and accumulated energy distributions, and estimating the health of the Li-ion battery system. A completed electric power improvement project dealing with power system aging is reported. Based on the long-term usage experience, a simple cost analysis model comparing lead–acid and Li-ion battery systems is built, revealing that expensive Li-ion batteries can compete with cheap lead–acid batteries for long-term usage on high mountains. This case study can provide engineers and researchers with a fundamental understanding of the long-term usage of off-grid PV ESSs and engineering on high mountains. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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11 pages, 321 KiB

Open AccessArticle

Investigations into the Charge Times of Lead–Acid Cells under Different Partial-State-of-Charge Regimes

by Max Parker and Richard McMahon

Batteries 2024, 10(6), 201; https://doi.org/10.3390/batteries10060201 - 11 Jun 2024

Viewed by 727

Abstract

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how [...] Read more.

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen. Full article

(This article belongs to the Topic Battery Design and Management)

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30 pages, 9402 KiB

Open AccessReview

Design Principles and Development Status of Flexible Integrated Thin and Lightweight Zinc-Ion Batteries

by Xuxian Liu, Yongchang Jiang, Yaqun Wang and Lijia Pan

Batteries 2024, 10(6), 200; https://doi.org/10.3390/batteries10060200 - 10 Jun 2024

Viewed by 595

Abstract

The rapid advancement of wearable devices and flexible electronics has spurred an increasing need for high-performance, thin, lightweight, and flexible energy storage devices. In particular, thin and lightweight zinc-ion batteries require battery materials that possess exceptional flexibility and mechanical stability to accommodate complex [...] Read more.

The rapid advancement of wearable devices and flexible electronics has spurred an increasing need for high-performance, thin, lightweight, and flexible energy storage devices. In particular, thin and lightweight zinc-ion batteries require battery materials that possess exceptional flexibility and mechanical stability to accommodate complex deformations often encountered in flexible device applications. Moreover, the development of compact and thin battery structures is essential to minimize the overall size and weight while maintaining excellent electrochemical performance, including high energy density, long cycle life, and stable charge/discharge characteristics, to ensure their versatility across various applications. Researchers have made significant strides in enhancing the battery’s performance by optimizing crucial components such as electrode materials, electrolytes, separators, and battery structure. This review provides a comprehensive analysis of the design principles essential for achieving thinness in zinc-ion batteries, along with a summary of the preparation methods and potential applications of these batteries. Moreover, it delves into the challenges associated with achieving thinness in zinc-ion batteries and proposes effective countermeasures to address these hurdles. This review concludes by offering insights into future developments in this field, underscoring the continual advancements and innovations that can be expected. Full article

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25 pages, 9188 KiB

Open AccessArticle

Battery Modeling for Emulators in Vehicle Test Cell

by Chris Roberts, Simon Petrovich and Kambiz Ebrahimi

Batteries 2024, 10(6), 199; https://doi.org/10.3390/batteries10060199 - 6 Jun 2024

Viewed by 554

Abstract

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified [...] Read more.

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified systems, battery emulators are used with advanced mathematical models describing the expected voltage output with respect to current load. This paper conducted research into different modeling types: electrochemical, thermal, and electronic equivalent circuit models (EECMs). EECMs were identified as the most suitable to be used in conjunction with emulation techniques. A foundation EECM was created in conjunction with a thermal part to simulate thermal dependency. Hybrid Pulse Power Characterization (HPPC) tests were conducted on an NMC Li-ion cell across a range of temperatures from −20 °C to 25 °C. Using parameter optimization techniques, the HPPC test data were used to identify the resistance, capacitance, and the open-circuit voltage of the cell across a range of state of charge bounds and across a temperature range of 0 °C to 25 °C. The foundation model was assessed using identified parameters on two current profiles derived from drive cycles across a temperature range of 0 °C to 10 °C. The FMU (Functional Mockup Unit) model format was determined as the required interface for an AVL battery emulator. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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14 pages, 4104 KiB

Open AccessArticle

State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter

by Hongli Ma, Xinyuan Bao, António Lopes, Liping Chen, Guoquan Liu and Min Zhu

Batteries 2024, 10(6), 198; https://doi.org/10.3390/batteries10060198 - 4 Jun 2024

Viewed by 639

Abstract

Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman [...] Read more.

Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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16 pages, 6762 KiB

Open AccessArticle

Transition Metal-Based Polyoxometalates for Oxygen Electrode Bifunctional Electrocatalysis

by Jadranka Milikić, Filipe Gusmão, Sara Knežević, Nemanja Gavrilov, Anup Paul, Diogo M. F. Santos and Biljana Šljukić

Batteries 2024, 10(6), 197; https://doi.org/10.3390/batteries10060197 - 3 Jun 2024

Viewed by 592

Abstract

Polyoxometalates (POMs) with transition metals (Co, Cu, Fe, Mn, Ni) of Keggin structure and lamellar-stacked multi-layer morphology were synthesized. They were subsequently explored as bifunctional electrocatalysts for oxygen electrodes, i.e., oxygen reduction (ORR) and evolution (OER) reaction, for aqueous rechargeable metal-air batteries in [...] Read more.

Polyoxometalates (POMs) with transition metals (Co, Cu, Fe, Mn, Ni) of Keggin structure and lamellar-stacked multi-layer morphology were synthesized. They were subsequently explored as bifunctional electrocatalysts for oxygen electrodes, i.e., oxygen reduction (ORR) and evolution (OER) reaction, for aqueous rechargeable metal-air batteries in alkaline media. The lowest Tafel slope (85 mV dec⁻¹) value and the highest OER current density of 93.8 mA cm⁻² were obtained for the Fe-POM electrocatalyst. Similar OER electrochemical catalytic activity was noticed for the Co-POM electrocatalyst. This behavior was confirmed by electrochemical impedance spectroscopy, where Fe-POM gave the lowest charge transfer resistance of 3.35 Ω, followed by Co-POM with R_ct of 15.04 Ω, during the OER. Additionally, Tafel slope values of 85 and 109 mV dec⁻¹ were calculated for Fe-POM and Co-POM, respectively, during the ORR. The ORR at Fe-POM proceeded by mixed two- and four-electron pathways, while ORR at Co-POM proceeded exclusively by the four-electron pathway. Finally, capacitance studies were conducted on the synthesized POMs. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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14 pages, 1630 KiB

Open AccessArticle

Research on the Human–Robot Collaborative Disassembly Line Balancing of Spent Lithium Batteries with a Human Factor Load

by Jie Jiao, Guangsheng Feng and Gang Yuan

Batteries 2024, 10(6), 196; https://doi.org/10.3390/batteries10060196 - 3 Jun 2024

Viewed by 320

Abstract

The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive [...] Read more.

The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly. Full article

(This article belongs to the Special Issue Lithium-Ion Battery Recycling)

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11 pages, 3296 KiB

Open AccessArticle

High-Performance Supercapacitors Based on Graphene/Activated Carbon Hybrid Electrodes Prepared via Dry Processing

by Shengjun Chen, Wenrui Wang, Xinyue Zhang and Xiaofeng Wang

Batteries 2024, 10(6), 195; https://doi.org/10.3390/batteries10060195 - 3 Jun 2024

Viewed by 390

Abstract

Graphene has a high specific surface area and high electrical conductivity, and its addition to activated carbon electrodes should theoretically significantly improve the energy storage performance of supercapacitors. Unfortunately, such an ideal outcome is seldom verified in practical commercial supercapacitor design and production. [...] Read more.

Graphene has a high specific surface area and high electrical conductivity, and its addition to activated carbon electrodes should theoretically significantly improve the energy storage performance of supercapacitors. Unfortunately, such an ideal outcome is seldom verified in practical commercial supercapacitor design and production. In this paper, the oxygen-containing functional groups in graphene/activated carbon hybrids, which are prone to induce side reactions, are removed in the material synthesis stage by a special process design, and electrodes with high densities and low internal resistances are prepared by a dry process. On this basis, a carbon-coated aluminum foil collector with a full tab structure is designed and assembled with graphene/activated carbon hybrid electrodes to form a commercial supercapacitor in cylindrical configuration. The experimental tests confirmed that such supercapacitors have high capacity density, power density, low internal resistance (about 0.06 mΩ), good high-current charging/discharging characteristics, and a long lifetime, with more than 80% capacity retention after 10 W cycles. Full article

(This article belongs to the Special Issue Advances in Hybrid Supercapacitors: Materials, Devices, Models, Systems, and Applications)

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22 pages, 2963 KiB

Open AccessReview

Status and Prospects of Research on Lithium-Ion Battery Parameter Identification

by Jianlin Li, Yuchen Peng, Qian Wang and Haitao Liu

Batteries 2024, 10(6), 194; https://doi.org/10.3390/batteries10060194 - 31 May 2024

Viewed by 546

Abstract

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting [...] Read more.

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. Full article

(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)

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13 pages, 4213 KiB

Open AccessArticle

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

Viewed by 529

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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13 pages, 3135 KiB

Open AccessArticle

Hierarchical Porosity and Surface Oxygenation of Carbon-Based Cathodes Enhances Discharge Capacity and Decreases Discharge Overpotential of Potassium–Oxygen Batteries

by Shikha Singh, Jannis Küpper, Ahed Abouserie, Gianluca Dalfollo, Michael Noyong and Ulrich Simon

Batteries 2024, 10(6), 192; https://doi.org/10.3390/batteries10060192 - 31 May 2024

Viewed by 457

Abstract

Potassium–oxygen batteries (KOBs) are a promising energy storage technology with high theoretical energy density, low overpotential and a long cycle life. The cathode microstructure plays a significant role in the electrochemical performance of KOB. In this article, hierarchical porosity was introduced to commercially [...] Read more.

Potassium–oxygen batteries (KOBs) are a promising energy storage technology with high theoretical energy density, low overpotential and a long cycle life. The cathode microstructure plays a significant role in the electrochemical performance of KOB. In this article, hierarchical porosity was introduced to commercially available carbon paper cathodes by thermal pretreatment in air at different pretreatment times. This pretreatment modifies the properties, such as surface area, defects, oxygen functional groups, etc. The discharge performance was determined at three different current densities, i.e., 0.1 mA/cm², 0.5 mA/cm², and 1.0 mA/cm². It has been found that an increase in specific surface area with the introduction of micropores and mesopores is beneficial for the improvement in the discharge capacity by enabling homogeneous discharge product, KO₂ distribution and high degrees of pore filling over the volume of the cathode. A reduction in the discharge overpotentials was observed, which is attributed to the introduction of oxygenic functional groups and defects. Samples treated for the longest pretreatment time of 24 h showed the highest discharge capacity of 5 mAh/cm² and lowest discharge overpotential of 0.03 V. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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17 pages, 11349 KiB

Open AccessArticle

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

by Younggill Son and Woongchul Choi

Batteries 2024, 10(6), 191; https://doi.org/10.3390/batteries10060191 - 31 May 2024

Viewed by 496

Abstract

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is [...] Read more.

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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28 pages, 9374 KiB

Open AccessArticle

Improved Thermal Management of Li-Ion Batteries with Phase-Change Materials and Metal Fins

by Pierluca Paciolla and Davide Papurello

Batteries 2024, 10(6), 190; https://doi.org/10.3390/batteries10060190 - 31 May 2024

Viewed by 532

Abstract

The continuing increase in pollutant emissions requires the use of alternative power sources. This includes the use of electric or hybrid vehicles whose energy storage system is based on batteries of various types, including lithium-ion batteries. The optimum operating temperature is between 15 [...] Read more.

The continuing increase in pollutant emissions requires the use of alternative power sources. This includes the use of electric or hybrid vehicles whose energy storage system is based on batteries of various types, including lithium-ion batteries. The optimum operating temperature is between 15 °C and 35 °C. Too high temperatures can lead to catastrophic phenomena such as thermal runaway. The thermal gradient within the system should not exceed 5 °C. An effective Battery Thermal Management System can mitigate this problem. This study analysed a lithium-ion battery with a bag structure. Temperature control was evaluated using a passive (low-cost) system with phase-change materials (PCMs). The material chosen was n-octadecane (paraffin) due to its thermophysical properties and market price. Four different cooling methods were analysed, including air, fins, pure PCM, and a mixed system of single cells and small battery packs. The results show that an undesirable temperature peak around 50 °C (323.15 K) can occur at hot spots. The best system for containing the temperature inside the battery pack is the PCM cooling system with fins. The optimum fin thickness is 1.5 mm. To contain the temperature inside the battery pack, the number of fins studied is 10, while the best temperature containment is achieved with n+ 1 plates, where n is the number of cells. Full article

(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)

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11 pages, 3640 KiB

Open AccessArticle

Fast Li⁺ Transfer Scaffold Enables Stable High-Rate All-Solid-State Li Metal Batteries

by Libo Song, Yuanyue He, Zhendong Li, Zhe Peng and Xiayin Yao

Batteries 2024, 10(6), 189; https://doi.org/10.3390/batteries10060189 - 31 May 2024

Viewed by 432

Abstract

Sluggish transfer kinetics caused by solid–solid contact at the lithium (Li)/solid-state electrolyte (SE) interface is an inherent drawback of all-solid-state Li metal batteries (ASSLMBs) that not only limits the cell power density but also induces uneven Li deposition as well as high levels [...] Read more.

Sluggish transfer kinetics caused by solid–solid contact at the lithium (Li)/solid-state electrolyte (SE) interface is an inherent drawback of all-solid-state Li metal batteries (ASSLMBs) that not only limits the cell power density but also induces uneven Li deposition as well as high levels of interfacial stress that deteriorates the internal structure and cycling stability of ASSLMBs. Herein, a fast Li⁺ transfer scaffold is proposed to overcome the sluggish kinetics at the Li/SE interface in ASSLMBs using an α-MnO₂-decorated carbon paper (CP) structure (α-MnO₂@CP). At an atomic scale, the tunnel structure of α-MnO₂ exhibits a great ability to facilitate Li⁺ adsorption and transportation across the inter-structure of α-MnO₂@CP, leading to a high critical current density of 3.95 mA cm⁻² at the Li/SE interface. Meanwhile, uniform Li deposition can be guided along the skeletons of α-MnO₂@CP with minimized volume expansion, significantly improving the structural stability of the Li/SE interface. Based on these advantages, the ASSLMBs using α-MnO₂@CP protected the Li anode and can stably cycle up to very high charge/discharge rates of 10C/10C, paving the way for developing high-power ASSLMBs. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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18 pages, 3028 KiB

Open AccessArticle

Dynamic Battery Modeling for Electric Vehicle Applications

by Renos Rotas, Petros Iliadis, Nikos Nikolopoulos, Dimitrios Rakopoulos and Ananias Tomboulides

Batteries 2024, 10(6), 188; https://doi.org/10.3390/batteries10060188 - 31 May 2024

Viewed by 556

Abstract

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit [...] Read more.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica. Full article

(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)

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32 pages, 16587 KiB

Open AccessArticle

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

by Yujuan Sun, Hao Tian, Fangfang Hu and Jiuyu Du

Batteries 2024, 10(6), 187; https://doi.org/10.3390/batteries10060187 - 30 May 2024

Viewed by 561

Abstract

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge [...] Read more.

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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