Future Batteries最新文献

{"title":"Robust hybrid Neural–Kalman filter for real-time supercapacitor state-of-charge estimation in electric vehicles","authors":"Islam A. Sayed ,&nbsp;Yousef Mahmoud","doi":"10.1016/j.fub.2025.100115","DOIUrl":"10.1016/j.fub.2025.100115","url":null,"abstract":"<div><div>Accurate estimation of supercapacitor state-of-charge (SOC) is vital for optimal energy management in electric vehicles (EVs), particularly within hybrid energy storage systems (HESS). Challenges arise from nonlinear dynamics, self-discharge, temperature sensitivity, and aging-induced parameter drift. This study introduces KalmanNet, a neural network-enhanced Kalman filter, for supercapacitor SOC estimation. The approach integrates a three-branch equivalent circuit model with a data-driven Kalman gain learner trained solely on voltage and current measurements, without requiring synthetic Kalman gain ground truth. KalmanNet adapts dynamically to system uncertainties while preserving the recursive nature of traditional Kalman filters. Validation using experimental data from commercial supercapacitors under standard EV driving cycles, aging effects, disturbances, and computational load demonstrates its effectiveness. KalmanNet achieves a root mean square error (RMSE) of 0.35%, outperforming the Extended Kalman Filter (2%), Sigma-Point/Unscented Kalman Filters (1.1%), Particle Filters (0.8%), and Recurrent Neural Networks (2.2%). Processor-in-the-loop (PIL) tests confirm real-time feasibility with execution times well below task periods and CPU usage under 0.1%. The results demonstrate KalmanNet’s superior accuracy, robustness, and computational efficiency for real-time EV applications.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Battery-Insight-PSO: A machine learning model for accurate prediction of state of health and remaining useful life in lithium-ion batteries","authors":"Md Fazle Hasan Shiblee,&nbsp;Hannu Laaksonen","doi":"10.1016/j.fub.2025.100114","DOIUrl":"10.1016/j.fub.2025.100114","url":null,"abstract":"<div><div>Condition based monitoring (CBM) of the lithium-ion (Li-ion) battery has become very popular in recent years because of its wide usage as an energy storage for smart grids, power sources in various industrial equipment, electric vehicles (EVs), etc. As a result, predicting the state of health (SOH) and the remaining useful life (RUL) of Li-ion batteries with high accuracy ensures optimal performance and safe utilization, preventing non-scheduled failures and saving maintenance costs. This paper illustrates the significance of highly accurate SOH and RUL prediction for Li-ion batteries. This paper proposes a model called Battery-Insight-PSO, which employs the Extreme Gradient Boosting Regression (XGBoost) machine learning algorithm to forecast SOH and RUL. In this study, the Particle Swarm Optimization Algorithm (PSO) is used to optimize different parameters of XGBoost for ensuring precise and reliable predictions of SOH and RUL for Li-ion batteries. In this study, the National Aeronautics and Space Administration (NASA) Li-ion Battery Aging Datasets and the NMC LCO 18650 battery dataset from the Hawaii Natural Energy Institute (HNEI) were analyzed. Additionally, the performance of Battery-Insight-PSO was compared with other machine learning algorithms. Machine learning models were evaluated using various performance metrics. The estimation errors of Battery-Insight-PSO are very low, which means that this model can be highly accurate in predicting SOH and RUL. Moreover, the R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> scores for the training and testing sets of this model also show high consistency with 0.9998 for each dataset, demonstrating high accuracy and reliable performance.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Failure modes, safety concerns, testing protocol, and advancement in lithium-ion battery technology","authors":"Mohammad Waseem ,&nbsp;Kotha Shashidhar Reddy ,&nbsp;T. Ramamohan Rao ,&nbsp;Mohd Suhaib ,&nbsp;Mumtaz Ahmad Khan","doi":"10.1016/j.fub.2025.100113","DOIUrl":"10.1016/j.fub.2025.100113","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) play a pivotal role in electric vehicle (EV) technology due to their high energy density and efficiency. However, their vulnerability to thermal runaway, fire, and explosion remains a major barrier to widespread adoption. This study addresses the lack of integrated analysis by reviewing current research trends in LIBs, advancements in EV applications, common failure modes, safety concerns, testing protocols, and AI/ML-based safety enhancements. While previous studies have often treated these aspects separately, this paper consolidates critical issues such as overcharging, mechanical wear, separator degradation, lithium plating, and electrolyte breakdown, alongside safety testing standards like thermal, penetration, and crushing tests. It further explores emerging innovations including risk-free electrolyte chemistries, stabilized electrode interfaces, and phase change materials for thermal management. The novelty lies in its multidimensional approach, linking material degradation, diagnostics, and sustainability. The review concludes that integrating predictive AI models, improving material robustness, and adopting stringent safety protocols are essential to mitigating LIB risks and ensuring safer, more sustainable EV deployment.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing multilayer graphite-silicon anodes: A computational approach to enhancing lithium-Ion battery performance","authors":"Juan C. Rubio,&nbsp;Martin Bolduc","doi":"10.1016/j.fub.2025.100112","DOIUrl":"10.1016/j.fub.2025.100112","url":null,"abstract":"<div><div>This study evaluated the performance of multilayer anodes for lithium-ion batteries, composed of an outer graphite layer in direct contact with the electrolyte and an inner graphite–silicon composite layer, using finite-element simulations and multivariate statistical analysis. Various silicon contents such as 10, 20 percent and 30 %, layer thickness configurations including 30–30 µm, 20–40 µm and 10–50 µm, and graphite particle sizes of 2.5, 5 and 7.5 µm were systematically examined while maintaining a total anode thickness of 60 µm. In addition, the cathode material NMC 622 and the electrolyte LiPF6 in 3:7 EC:EMC were specified in the simulated cell configuration. The methodology integrated COMSOL Multiphysics® simulations with a simulation design (DOE) constructed in JMP, enabling the identification of key response parameters such as capacity loss percentage, solid-electrolyte interphase (SEI) layer thickness, potential drop across the SEI and electrolyte consumption over 2000 simulated cycles. Simulation results indicated that a 30–30 µm configuration, employing 2.5 µm graphite particles and a silicon content in the range of 20–30 % within the composite layer, substantially reduces potential drop, electrolyte consumption and SEI growth compared to modeled single-layer 100 % graphite or homogeneous silicon–graphite anodes. These findings underscore the viability of dual-layer structures for leveraging silicon’s high theoretical capacity without compromising electrochemical stability, and they highlight the crucial role of simulation-driven optimization in predicting long-term performance in batteries with enhanced energy density and extended cycle life.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Thermal Runaway in Li-ion Batteries: Pathways, Detection Techniques, and Early Warning Models","authors":"Shubham Bhoir ,&nbsp;Emanuele Michelini ,&nbsp;Jörg Moser ,&nbsp;Claudio Brivio ,&nbsp;Mario Paolone","doi":"10.1016/j.fub.2025.100110","DOIUrl":"10.1016/j.fub.2025.100110","url":null,"abstract":"<div><div>In the recent past, various accidents related to lithium battery fires have been reported worldwide, some of them being fatal. This emphasizes the need to improve battery systems’ safety for their users. To achieve this, first, an understanding of the phenomena that take place during TR is essential. Then, such an understanding may be used to identify sensors and models that can predict TR, with sufficient anticipation. Following this logic, this systematic literature review thoroughly analyzes the state-of-the-art regarding the different abuses and the consequent safety threats to lithium-based battery cells to (i) describe the various phenomena that occur when a battery cell is abused, (ii) analyze the different sensors that are found in the literature which can be used to detect a faulty or abused battery cell, and (iii) review the various models that are used to analyze the data acquired by the sensors. Since this is a systematic literature review, the methodology followed to carry out this review is rigorously described, including the literature database search string and paper-inclusion criteria used to screen the list of works and arrive at a final set of meaningful papers. Finally, in view of the findings, a combination of sensors and models is suggested to assess the safety level of a battery cell. It is concluded that temperature monitoring, along with an empirical model, is best suited for abuse detection, while voltage monitoring, along with a statistical model, should be adopted for cells’ fault detection.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in photoelectrode surface, electrolyte, and integrated configurations for solar redox flow batteries – A mini review","authors":"Kailong Li ,&nbsp;Zixing Gu ,&nbsp;Yuzhuo Qi ,&nbsp;Haochen Zhu ,&nbsp;Mengyue Lu ,&nbsp;Zhuo Li ,&nbsp;Qiang Ma ,&nbsp;Huaneng Su ,&nbsp;Weiwei Yang ,&nbsp;Qian Xu","doi":"10.1016/j.fub.2025.100104","DOIUrl":"10.1016/j.fub.2025.100104","url":null,"abstract":"<div><div>Under the background of the increasing contradiction between global energy supply and demand as well as large-scale application of renewable energy, as an application of flow battery technology in solar energy storage, solar redox flow batteries (SRFBs) have demonstrated rapid development owing to their high-efficiency photoelectrochemical energy conversion and adaptable storage characteristics. Although significant progress has been made in photoelectrode surface regulation, electrolyte optimization and battery integration design, improvements in system efficiency and efforts toward engineering application still face multiple challenges. In this review, the working mechanism of SRFBs is briefly introduced, and then the mechanism of improving photocurrent density and energy conversion efficiency through multi-dimensional optimization strategies such as morphology optimization, defect doping coordination, heterojunction construction and surface modification is systematically summarized from the photoelectrode interface engineering. Meanwhile, the key role of the electrolyte and illumination synergistic optimization is discussed. Finally, the breakthroughs of SRFBs in carrier separation efficiency and mass transfer dynamics optimization are analyzed in combination with innovative structures such as the cell system structure and flow channel design. This review aims to provide theoretical references for interface engineering of SRFBs photoelectrodes, synergistic optimization of electrolyte and illumination, and cell structure design. It is pointed out that the development of non-biased high-efficiency photoelectrodes, low loss electrolyte transmission systems and full-spectral-response devices represent the core direction of future technological breakthroughs.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review on sustainable strategies for lithium recovery from spent lithium-ion batteries","authors":"J. Jayamuthunagai ,&nbsp;R. Mary Nancy Flora ,&nbsp;K. Senthilkumar ,&nbsp;B. Bharathiraja","doi":"10.1016/j.fub.2025.100105","DOIUrl":"10.1016/j.fub.2025.100105","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) are essential to today's energy storage technology, powering from handheld devices to electric vehicles and grid-scale renewable energy installations. With demand for LIBs rising ever more strongly—fuelled by the global clean energy shift—the demand for sustainable lithium recovery has accelerated. Extraction of lithium using traditional mining is energy-wasting, environmentally disruptive, and not viable given dwindling natural reserves. Thus, recycling lithium from retired LIBs is critical not just for resource security but also for environmental minimization. Herein is an overview of existing technologies applied for lithium extraction from spent LIBs, with emphasis on four dominant methods: pyrometallurgy, hydrometallurgy, electrochemical extraction, and bioleaching. The technologies are compared based on commercialization stage, efficiency in lithium extraction, cost, environmental impact, and applicability. Although pyrometallurgy and hydrometallurgy are more conventional, they may require high energy expenditure and toxic chemical utilization. For comparison, new technologies such as bioleaching and electrochemical extraction provide less polluting and more selective processes but are yet to be developed for large-scale use. Newly developed innovations in deep eutectic solvent-aided leaching, mechanochemical treatment, and bio-electrochemical systems have exhibited potential in enhancing lithium extraction efficiency while reducing environmental footprint. In spite of advances in technology, &lt; 1 % of lithium is recycled world-wide, and there is an urgent need for optimizing and integrating the available technologies. This review paper contrasts these technologies and presents directions for the enhancement of lithium recycling techniques with the aims of realizing higher recovery rates, cost-effectiveness, and eco-friendliness. Eventually, designing effective recycling schemes will be important for underpinning a circular economy of lithium and in delivering long-term energy security.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-empowered modeling and management of flow batteries: A mini-review","authors":"Qiang Zheng ,&nbsp;Xingyi Shi ,&nbsp;Yuze Cai ,&nbsp;Liang An ,&nbsp;Dongxiao Zhang","doi":"10.1016/j.fub.2025.100107","DOIUrl":"10.1016/j.fub.2025.100107","url":null,"abstract":"<div><div>Flow batteries are pivotal for grid-scale renewable energy storage due to their scalability and decoupled energy-power design, yet they still face challenges in cost reduction and efficiency improvement, which necessitates advanced modeling to accelerate development as a complement to experiments. However, traditional numerical modeling is not efficient, restricting its application to optimal management. Artificial intelligence (AI) is revolutionizing this field by enabling accelerated simulations that integrate predictive accuracy and computational efficiency, while data-driven modeling empowers intelligent optimization of input design parameters. Beyond static modeling, AI techniques facilitate dynamic management through real-time state estimation and adaptive control strategies that respond to complex operating conditions. This review summarizes advances in recent five years of AI applications for flow batteries, and critically examine how the AI approaches address fundamental limitations in modeling and management paradigms, while identifying key challenges in model robustness and practical implementation that guide future research directions in developing intelligent flow battery systems.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stable Zn metal deposition/stripping in Zn-Li dual-ion batteries achieved by acetonitrile-water co-solvent enhanced acetamide-based deep eutectic electrolytes","authors":"Chun-Jern Pan ,&nbsp;Shih-Che Lin ,&nbsp;Bing-Joe Hwang ,&nbsp;Wei-Hsiang Huang ,&nbsp;Chun-I Lee","doi":"10.1016/j.fub.2025.100108","DOIUrl":"10.1016/j.fub.2025.100108","url":null,"abstract":"<div><div>Zinc batteries have emerged as potential candidates for next-generation energy storage due to their high safety, environmental friendliness, and abundant raw material. However, zinc dendrite formation and water-related parasitic reaction occurred during zinc metal deposition/stripping, resulting in limited batteries cycle life. To address these challenges, this study developed an acetamide-based deep eutectic electrolytes (DEEs) with acetonitrile and water as co-solvents to improve the cyclability of Zn metal deposition/stripping. The co-solvents optimized DEEs is being examined first with Zn//Cu asymmetric cell, delivering high Zn deposition/stripping average coulombic efficiency (CE) of 99.79 % for over 2800 cycles. The addition of co-solvents effectively increases the exchange current density and decrease charge transfer resistance for Zn deposition/stripping. The dual ion batteries using LiMn₂O₄ (LMO) as cathode and Zn metal anode were assembled and subject to electrochemical evaluation. The battery delivers an initial capacity of 53 mAh g⁻¹ and &gt; 30 mAh g⁻¹ after 1200 cycles, stably operating for over 2400 cycles with average CE &gt; 99 % and 24.7 mAh g⁻¹ capacity. An organic/inorganic hybrid interfacial layer composed of Zn-N and amide-related structures is found on the surface of cycled LMO cathode. The layer could effectively suppress parasitic oxidative reactions between the solvent and active materials, leading to high CE and maintaining high Mn³⁺ /Mn⁴⁺ ratio. This study demonstrates that co-solvents design in DEEs offers a promising strategy for high-performance Zn-based hybrid batteries.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of recent advances, current limitations, and remedies of lithium-ion batteries for advanced technological applications","authors":"Cyril Ikechukwu Idu ,&nbsp;Uwa Orji Uyor ,&nbsp;Abimbola P.I. Popoola ,&nbsp;Olawale M. Popoola ,&nbsp;Sani Mohammed Adams","doi":"10.1016/j.fub.2025.100109","DOIUrl":"10.1016/j.fub.2025.100109","url":null,"abstract":"<div><div>Sustainable energy has become a focal point of innovation in recent years. Lithium-ion batteries (LIBs), the most prevalent energy storage systems, are widely used in automobiles, consumer electronics, and renewable energy applications. However, traditional, commercially available LIBs have both advantages and significant limitations. These limitations arise from various reactions occurring within the cell that hinder their application scope and effectiveness. Continuous charging and discharging induce stress in the electrodes, while heat generation destabilizes active materials. Additionally, electrode-electrolyte interactions lead to the degradation of both components. These factors collectively contribute to the poor performance often experienced in LIBs. To address these issues, researchers have explored modifying existing materials through additives, stabilizers, reinforcements, and surface coatings. New materials, such as metal-oxide-based electrodes, alloys, composites, nanomaterials, and advanced electrolytes, have also been developed, capable of withstanding stress, operate across a wide temperature range, and reduce impedance by improving electrode-electrolyte interactions. They also aim to offer high-capacity storage and long cycle life. However, a research gap is found where little report has been made in regards to combining 3D electrode architectures and solid-state electrolytes (SSEs). This review goes on to show that synergizing these new materials holds the potential to deliver highly stable cells without compromising structural integrity (the electrode’s mechanical framework and interfacial cohesion under the stresses of lithiation/delithiation, temperature swings, and volume changes) and storage capacity over prolonged usage periods.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}