Pub Date : 2024-08-09DOI: 10.3390/batteries10080285
Bernardo Tormos, Santiago Ruiz, Jorge Alvis-Sanchez, L. I. Farfán-Cabrera
This study aims to assess the compatibility of various e-thermal fluids for immersion cooling in battery electric vehicles through a copper wire resistance corrosion test. The tested fluids include a polyalphaolefin, diester, mineral oil API G-III, transformer oil, and a fully formulated dielectric coolant. The test was conducted at 130 °C for 336 h, and the resistance of the copper wires was monitored in vapor and oil phases. By comparing the resistance variation and analyzing portions of the wires through scanning electron microscopy, it was found that the vapor phase of PAO and diester in one of the tests exhibited significant corrosion, while the dielectric coolant showed minimal corrosive effects, implying better compatibility. These results provide insights into the corrosion behavior and compatibility of the fluids with copper, which are essential for selecting suitable dielectric fluids for immersion cooling applications in electric vehicles.
本研究旨在通过铜线电阻腐蚀测试，评估电池电动汽车浸入式冷却所使用的各种电热冷却液的兼容性。测试的液体包括聚α烯烃、二元醇、矿物油 API G-III、变压器油和全配方电介质冷却液。试验在 130 °C 的温度下进行了 336 小时，监测了铜线在气相和油相的电阻。通过比较电阻变化和用扫描电子显微镜分析铜丝的部分，发现其中一项测试中 PAO 和二元醇的气相表现出明显的腐蚀性，而电介质冷却剂的腐蚀性极小，这意味着两者具有更好的兼容性。这些结果有助于深入了解液体的腐蚀行为和与铜的兼容性，这对于为电动汽车的浸入式冷却应用选择合适的介电液体至关重要。
{"title":"Copper Wire Resistance Corrosion Test for Assessing Copper Compatibility of E-Thermal Fluids for Battery Electric Vehicles (BEVs)","authors":"Bernardo Tormos, Santiago Ruiz, Jorge Alvis-Sanchez, L. I. Farfán-Cabrera","doi":"10.3390/batteries10080285","DOIUrl":"https://doi.org/10.3390/batteries10080285","url":null,"abstract":"This study aims to assess the compatibility of various e-thermal fluids for immersion cooling in battery electric vehicles through a copper wire resistance corrosion test. The tested fluids include a polyalphaolefin, diester, mineral oil API G-III, transformer oil, and a fully formulated dielectric coolant. The test was conducted at 130 °C for 336 h, and the resistance of the copper wires was monitored in vapor and oil phases. By comparing the resistance variation and analyzing portions of the wires through scanning electron microscopy, it was found that the vapor phase of PAO and diester in one of the tests exhibited significant corrosion, while the dielectric coolant showed minimal corrosive effects, implying better compatibility. These results provide insights into the corrosion behavior and compatibility of the fluids with copper, which are essential for selecting suitable dielectric fluids for immersion cooling applications in electric vehicles.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/batteries10080284
Anil Kumar M. R., Atiyeh Nekahi, Mohamed Djihad Bouguern, Dongling Ma, Karim Zaghib
Perovskite-based photo-batteries (PBs) have been developed as a promising combination of photovoltaic and electrochemical technology due to their cost-effective design and significant increase in solar-to-electric power conversion efficiency. The use of complex metal oxides of the perovskite-type in batteries and photovoltaic cells has attracted considerable attention. Because of its variable bandgap, non-rigid structure, high light absorption capacity, long charge carrier diffusion length, and high charge mobility, this material has shown promise in energy storage devices, especially Li-ion batteries (LIBs) and PBs. This review paper focuses on recent progress and comparative analysis of PBs using perovskite-based materials. The practical application of these batteries as dependable power sources faces significant technical and financial challenges because solar radiation is alternating. In order to address this, research is being performed on PBs with the integration of perovskite solar cells (PSCs) as a way to balance energy availability and demand, cut down on energy waste, and stabilize power output for wearable and portable electronics as well as energy storage applications.
{"title":"Advancements and Challenges in Perovskite-Based Photo-Induced Rechargeable Batteries and Supercapacitors: A Comparative Review","authors":"Anil Kumar M. R., Atiyeh Nekahi, Mohamed Djihad Bouguern, Dongling Ma, Karim Zaghib","doi":"10.3390/batteries10080284","DOIUrl":"https://doi.org/10.3390/batteries10080284","url":null,"abstract":"Perovskite-based photo-batteries (PBs) have been developed as a promising combination of photovoltaic and electrochemical technology due to their cost-effective design and significant increase in solar-to-electric power conversion efficiency. The use of complex metal oxides of the perovskite-type in batteries and photovoltaic cells has attracted considerable attention. Because of its variable bandgap, non-rigid structure, high light absorption capacity, long charge carrier diffusion length, and high charge mobility, this material has shown promise in energy storage devices, especially Li-ion batteries (LIBs) and PBs. This review paper focuses on recent progress and comparative analysis of PBs using perovskite-based materials. The practical application of these batteries as dependable power sources faces significant technical and financial challenges because solar radiation is alternating. In order to address this, research is being performed on PBs with the integration of perovskite solar cells (PSCs) as a way to balance energy availability and demand, cut down on energy waste, and stabilize power output for wearable and portable electronics as well as energy storage applications.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/batteries10080283
Jiwei Yao, Qiang Gao, Tao Gao, Benben Jiang, Kody M. Powell
Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accurately determining the dominant fading mechanism and predicting the average capacity fading rate. Physics–guided features, derived from the electrochemical properties and behaviors within the battery, are extracted from the first five cycles to provide meaningful, interpretable, and predictive data. Unlike previous models that rely on a single regression approach, our method utilizes two separate regression models tailored to the identified dominant fading mechanisms. Our model achieves 95.6% accuracy in determining the dominant fading mechanism using data from the second cycle and a mean absolute percentage error of 17.09% in predicting lifetime capacity fade from the first five cycles. This represents a substantial improvement over state–of–the–art models, which have an error rate approximately three times higher. This study underscores the significance of physics–guided data characterization and the necessity of identifying the primary fading mechanism prior to predicting the capacity fading rate in lithium–ion batteries.
{"title":"A Physics–Guided Machine Learning Approach for Capacity Fading Mechanism Detection and Fading Rate Prediction Using Early Cycle Data","authors":"Jiwei Yao, Qiang Gao, Tao Gao, Benben Jiang, Kody M. Powell","doi":"10.3390/batteries10080283","DOIUrl":"https://doi.org/10.3390/batteries10080283","url":null,"abstract":"Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accurately determining the dominant fading mechanism and predicting the average capacity fading rate. Physics–guided features, derived from the electrochemical properties and behaviors within the battery, are extracted from the first five cycles to provide meaningful, interpretable, and predictive data. Unlike previous models that rely on a single regression approach, our method utilizes two separate regression models tailored to the identified dominant fading mechanisms. Our model achieves 95.6% accuracy in determining the dominant fading mechanism using data from the second cycle and a mean absolute percentage error of 17.09% in predicting lifetime capacity fade from the first five cycles. This represents a substantial improvement over state–of–the–art models, which have an error rate approximately three times higher. This study underscores the significance of physics–guided data characterization and the necessity of identifying the primary fading mechanism prior to predicting the capacity fading rate in lithium–ion batteries.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A Novel Reaction Rate Parametrization Method for Lithium-Ion Battery Electrochemical Modelling","authors":"Alain Goussian, Loïc Assaud, Issam Baghdadi, Cédric Nouillant, Sylvain Franger","doi":"10.3390/batteries10060205","DOIUrl":"https://doi.org/10.3390/batteries10060205","url":null,"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 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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.3390/batteries10060206
Mano Schmitz, Julia Kowal
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.
准确估计锂离子电池（LIB）在运行期间的健康状况（SOH）对于确保最佳性能、延长电池寿命、防止意外故障或安全隐患至关重要。本研究提出了一种存储和性能优化的深度学习方法，利用恒定电流条件下部分充电曲线的原始传感器数据来估计锂离子电池基于容量的 SOH。所提出的模型基于一维卷积神经网络和长短期记忆神经网络的组合，将部分充电曲线的时间、电压和增量容量作为时间序列进行处理。该模型在不同的老化情况下进行了交叉验证，总体 MAE = 0.418%，RMSE = 0.531%，有望在实际应用中准确估计锂电池在不同使用和环境条件下的 SOH。
{"title":"A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves","authors":"Mano Schmitz, Julia Kowal","doi":"10.3390/batteries10060206","DOIUrl":"https://doi.org/10.3390/batteries10060206","url":null,"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 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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.3390/batteries10060207
Jun Zhu, Xude Li, Bingqing Hu, Shanhai Ge, Jiang Xu
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 Ti3C2Tx MXenes as anode materials. Additionally, our work aims to provide further insights into the energy storage mechanism of Ti3C2Tx in acid electrolytes. Our findings reveal that the ion transport mechanism of Ti3C2Tx 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 Ti3C2Tx MXene anode and pre-protonated CuII[FeIII(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.
{"title":"Low-Temperature-Tolerant Aqueous Proton Battery with Porous Ti3C2Tx MXene Electrode and Phosphoric Acid Electrolyte","authors":"Jun Zhu, Xude Li, Bingqing Hu, Shanhai Ge, Jiang Xu","doi":"10.3390/batteries10060207","DOIUrl":"https://doi.org/10.3390/batteries10060207","url":null,"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 Ti3C2Tx MXenes as anode materials. Additionally, our work aims to provide further insights into the energy storage mechanism of Ti3C2Tx in acid electrolytes. Our findings reveal that the ion transport mechanism of Ti3C2Tx 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 Ti3C2Tx MXene anode and pre-protonated CuII[FeIII(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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.3390/batteries10060203
Alexander Mirandona-Olaeta, E. Goikolea, Senen Lanceros-Mendez, A. Fidalgo-Marijuan, Idoia Ruiz de Larramendi
The authors wish to make the following corrections to their paper [...]
作者希望对其论文做如下更正[......]
{"title":"Correction: Mirandona-Olaeta et al. Ionic Liquid-Laden Zn-MOF-74-Based Solid-State Electrolyte for Sodium Batteries. Batteries 2023, 9, 588","authors":"Alexander Mirandona-Olaeta, E. Goikolea, Senen Lanceros-Mendez, A. Fidalgo-Marijuan, Idoia Ruiz de Larramendi","doi":"10.3390/batteries10060203","DOIUrl":"https://doi.org/10.3390/batteries10060203","url":null,"abstract":"The authors wish to make the following corrections to their paper [...]","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.3390/batteries10060204
S. S. Madani, C. Ziebert, P. Vahdatkhah, S. Sadrnezhaad
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.
近年来，锂离子电池（LIB）作为主要的储能解决方案被广泛采用，推动了交通电气化的快速发展。确保这些锂离子电池安全高效运行的迫切需求使电池管理系统（BMS）成为这一领域的关键组件。在 BMS 的各种功能中，状态和温度监测对于智能 LIB 管理至关重要。本综述将重点关注电池组健康管理的两个关键方面：健康状态（SOH）的准确预测和剩余使用寿命（RUL）的估算。实现精确的 SOH 预测不仅能延长电池组寿命，还能为优化电池使用提供宝贵的见解。此外，准确的 RUL 估计对于高效的电池管理和状态估计至关重要，尤其是在电动汽车需求持续激增的情况下。本综述强调了机器学习（ML）技术在提高 LIB 状态预测能力的同时降低计算复杂性的重要意义。通过深入探讨该领域的研究现状，综述旨在阐明在 LIB 中利用 ML 的前景广阔的未来途径。值得注意的是，综述强调了先进的 RUL 预测技术日益增长的必要性，以及这些技术在应对电动汽车需求激增所带来的挑战方面的作用。本综述指出了现有的挑战，并提出了克服这些障碍的结构化框架，强调开发专门针对可充电锂电池的机器学习应用。将人工智能（AI）技术整合到这项工作中至关重要，因为研究人员希望加快电池性能的进步，并克服目前与锂电池相关的局限性。通过采用对称的方法，人工智能与电池管理相协调，极大地促进了交通电气化的可持续发展。本研究简明扼要地概述了相关文献，深入探讨了利用 ML 技术进行锂离子电池健康监测的现状、未来前景和挑战。
{"title":"Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries","authors":"S. S. Madani, C. Ziebert, P. Vahdatkhah, S. Sadrnezhaad","doi":"10.3390/batteries10060204","DOIUrl":"https://doi.org/10.3390/batteries10060204","url":null,"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 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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3390/batteries10060201
Max Parker, Richard McMahon
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.
{"title":"Investigations into the Charge Times of Lead–Acid Cells under Different Partial-State-of-Charge Regimes","authors":"Max Parker, Richard McMahon","doi":"10.3390/batteries10060201","DOIUrl":"https://doi.org/10.3390/batteries10060201","url":null,"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 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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.3390/batteries10060200
Xuxian Liu, Yongchang Jiang, Yaqun Wang, Lijia Pan
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.
{"title":"Design Principles and Development Status of Flexible Integrated Thin and Lightweight Zinc-Ion Batteries","authors":"Xuxian Liu, Yongchang Jiang, Yaqun Wang, Lijia Pan","doi":"10.3390/batteries10060200","DOIUrl":"https://doi.org/10.3390/batteries10060200","url":null,"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 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.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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