Journal of energy storage最新文献

{"title":"Case study on flywheel energy storage systems: LPTN-based transient thermal analysis","authors":"Yuanyuan Jiao ,&nbsp;Xingjian Dai ,&nbsp;Yifei Wang ,&nbsp;Lu Wei ,&nbsp;Hualiang Zhang ,&nbsp;Haisheng Chen","doi":"10.1016/j.est.2025.116319","DOIUrl":"10.1016/j.est.2025.116319","url":null,"abstract":"<div><div>This study established a lumped parameter thermal network model for vertical flywheel energy storage systems, considering three critical gaps in conventional thermal modeling: oversimplified radiative heat transfer within sealed chambers, unquantified dynamic losses, including rotor eddy currents and flywheel friction, and transient hysteresis in stator water jacket cooling. Validated through extreme continuous charge-discharge experiments of 200–400 kW, the simulated temperatures exhibit a maximum deviation of 2 °C at steady-state, demonstrating superior accuracy in capturing both spatial distributions of the system and the temporal variations of coolant temperatures. Furthermore, most of the exergy loss in the heat transfer process is concentrated in the motor stator, casing, and cooling water. Improving the heat transfer paths in these areas would help enhance overall system efficiency. The outcomes offer valuable insights for correlative research on flywheel energy storage system thermal management technologies, providing significant references for the design and optimization of the system.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116319"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The environmental impact and eco-efficiency analysis of retired power battery recycling in China","authors":"Siyu Hou,&nbsp;Fan Yang,&nbsp;Xuning Zhuang,&nbsp;Xiaolong Song,&nbsp;Yongqi Dou,&nbsp;Wenjie Wu","doi":"10.1016/j.est.2025.116483","DOIUrl":"10.1016/j.est.2025.116483","url":null,"abstract":"<div><div>With the rapid development of electric vehicles (EVs) industry, recycling of the retired power batteries (RPBs) has become a significant topic with the concern of EVs sustainable development. This study evaluates the environmental impact and economic performance of RPBs recycling in China, including four recycling technologies for ternary lithium iron (NCM) batteries and two recycling technologies for lithium iron phosphate (LFP) batteries with different echelon use rates, based on life cycle assessment (LCA) and life cycle cost analysis (LCC) methods. The concept of eco-efficiency is innovatively introduced to further analyze the relationship between environmental impact and economic performance of RPBs recycling. Results show that both NCM and LFP batteries recycling exhibit significant environmental and economic benefits, which are both significantly affected by the echelon use rate. Under the current situation, LFP recycling with higher echelon use rate shows better environmental and economic benefits than NCM recycling. But if recycled with the same echelon use rate, NCM recycling exhibits better eco-efficiency performance than LFP recycling. For technologies, NCM recycling with pyro-hydrometallurgy and LFP recycling with physical method exhibit the best environmental and economic benefits. However, from the viewpoint of eco-efficiency, NCM recycling with hydrometallurgy A and LFP recycling with hydrometallurgy perform the best eco-efficiency. Overall, further improving the echelon use rate (especially for LFP batteries) would significantly enhance the environmental, economic and eco-efficiency performance of RPBs recycling. The results identify the importance of recycling technology selection in the overall environmental and economic performance of RPBs recycling process. The study contributes to identifying the critical role of echelon use rate and recycling technology selection as well as the key affecting factors, so as to improve the overall environmental and economic benefits of RPBs recycling in China.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116483"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging IoT-enabled machine learning techniques to enhance electric vehicle battery state-of-health prediction","authors":"Hesham A. Sakr ,&nbsp;Abdelfattah A. Eladl ,&nbsp;Magda I. El-Afifi","doi":"10.1016/j.est.2025.116409","DOIUrl":"10.1016/j.est.2025.116409","url":null,"abstract":"<div><div>State of Health (SOH) is a critical parameter for evaluating and managing electric vehicle (EV) battery systems, directly impacting battery reliability, safety, and lifespan, and accurate SOH estimation is essential for optimizing EV performance and efficiency. Machine learning (ML) has recently become a promising approach for SOH prediction, utilizing large datasets, enhanced storage, and advanced computing power to develop sophisticated predictive models. This study introduces an IoT-Fog-Cloud system to enhance SOH prediction by providing a structured approach for selecting and customizing ML models tailored to specific SOH challenges. By analyzing the unique characteristics of EV battery data and SOH estimation accuracy requirements, this research identifies optimal ML algorithms that improve prediction performance. Furthermore, the study emphasizes the need for advancements in battery management systems (BMS) for IoT-integrated EVs, including exploration of novel ML architectures for enhanced predictive accuracy, integration of diverse sensor data for improved real-time monitoring, and development of robust evaluation metrics to ensure model reliability. These contributions are crucial for advancing SOH estimation methods and ultimately extending the efficiency, performance, and longevity of EV batteries.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116409"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Porous nickel-manganese selenide composite nanosheets as binder-free electrode for ultra-stable hybrid supercapacitors","authors":"Ampasala Surya Kiran,&nbsp;Edugulla Girija Shankar,&nbsp;Manchi Nagaraju,&nbsp;Jae Su Yu","doi":"10.1016/j.est.2025.116371","DOIUrl":"10.1016/j.est.2025.116371","url":null,"abstract":"<div><div>Modernization has led to an exponential increase in the use of fossil fuels, thereby negatively impacting the environment. In this regard, electrochemical energy storage devices, such as supercapacitors (SCs), have been an attractive solution. Herein, we report the synthesis of binder-free multiphase SC electrode material synthesized based on potential transition metals, such as nickel (Ni) and manganese (Mn), coupled with a prominent chalcogen, such as Se (the resulting material being Ni₃Se₄-Mn₃O₄ composite, NMS). The synthesized NMS electrode material exhibited a high surface area owing to its nanoflake structure. The electrode showed high areal capacity and specific capacitance of 407.3 μAh cm⁻² and 40.7 mAh g⁻¹ at 3 mA cm⁻², respectively. For comparison, the optimized NMS electrode showed a higher discharge time than the other synthesized electrodes, exhibiting good cycling stability of 88.14 % and 98.9 % coulombic efficiencies (CEs) over 10,000 cycles. The NMS electrode was then coupled with a negative electrode (activated carbon/Ni foam) to fabricate a hybrid SC (HSC) device. This HSC device delivered a high areal capacitance of 661.2 mF cm⁻². The HSC device exhibited a cycling stability of 113.3 %, with 93.8 % CE over 10,000 cycles. The device exhibited energy and power density values of 206.6 μWh cm⁻² and 19,500 μW cm⁻², respectively. Also, the fabricated HSC device was used to power real-time electronic devices.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116371"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive technical and economic evaluations of using second-life batteries as energy storage in off-grid applications: A customized cost analysis","authors":"Feyza Turan ,&nbsp;Ali Rifat Boynuegri ,&nbsp;Tunç Durmaz","doi":"10.1016/j.est.2025.116379","DOIUrl":"10.1016/j.est.2025.116379","url":null,"abstract":"<div><div>The widespread use of lithium-ion batteries has raised significant waste management challenges, exacerbated by limited integration into recycling systems. When functional batteries are discarded rather than repurposed, valuable resources are lost. The emerging second-life battery (SLB) market presents a promising solution. However, uncertainties in SLB pricing significantly impact their economic viability and feasibility. Accurate pricing of SLB can mitigate substantial losses faced by electric vehicle (EV) users during battery replacements, addressing a major barrier to wider EV adoption. This study introduces a novel approach by employing a dynamic degradation model to determine the cycle life and optimize the capacity of both new and second-life batteries, while also considering round-trip efficiency. A comprehensive economic evaluation using the Net Present Value (NPV) method incorporates detailed cost factors, including purchase, testing, renovation, transportation, replacement costs (calculated using the Sinking Fund method), recycling revenues, and the often-overlooked opportunity cost. Additionally, government incentives are integrated into the analysis, resulting in a robust mathematical framework for pricing SLB. SLB market price under 0 %, 25 %, and 50 % government incentives are calculated as 88.05 €/kWh, 105.5 €/kWh, and 131.60 €/kWh, respectively, representing 34.1 % to 54.1 % of new battery purchase costs. The OEM ownership model shows that accounting for opportunity costs makes SLB 44.9 % more profitable than new batteries. Additionally, a sensitivity analysis was conducted to examine the impact of testing, renovation, and transportation costs, recycling revenue, new battery purchase cost, and discount rate parameters on the NPV of off-grid SLB applications and SLB market pricing. The analysis reveals that the new battery purchase cost has the greatest influence on SLB pricing, while the impact of other parameters is significantly lower. This study highlights the importance of dynamic modeling and comprehensive economic evaluation, emphasizing the need for tailored analyses for each application. These findings provide valuable insights into realistic SLB pricing, enhancing their market potential and supporting broader EV adoption.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116379"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RUL-Mamba: Mamba-based remaining useful life prediction for lithium-ion batteries","authors":"Jiahui Huang ,&nbsp;Lei Liu ,&nbsp;Hongwei Zhao ,&nbsp;Tianqi Li ,&nbsp;Bin Li","doi":"10.1016/j.est.2025.116376","DOIUrl":"10.1016/j.est.2025.116376","url":null,"abstract":"<div><div>Lithium-ion batteries play a crucial role in the fields of renewable energy and electric vehicles. Accurately predicting their Remaining Useful Life (RUL) is essential for ensuring safe and reliable operation. However, achieving precise RUL predictions poses significant challenges due to the complexities of degradation mechanisms and the impact of operational noise, particularly the capacity regeneration phenomenon. To address these issues, we propose a lithium-ion battery RUL prediction model named RUL-Mamba, which is based on the Mamba-Feature Attention Network (FAN)-Gated Residual Network (GRN). This model employs an encoder-decoder architecture that effectively integrates the Mamba module, FAN network, and GRN network. Mamba demonstrates superior temporal representation capabilities alongside efficient inference properties. The constructed FAN network leverages a feature attention mechanism to efficiently extract key features at each time step, enabling the Mamba block in the encoder to effectively capture information related to capacity regeneration from historical capacity sequences. The designed GRN network adaptively processes the decoded features output by the Mamba blocks in the decoder through a gating mechanism, accurately modeling the nonlinear mapping relationship between the decoded feature vector and the prediction target. Compared to state-of-the-art (SOTA) time series forecasting models on three battery degradation datasets from NASA, Oxford and Tongji University, the proposed model not only achieves SOTA predictive performance across various prediction starting points, with a maximum accuracy improvement of 42.5 % over existing models, but also offers advantages such as efficient training, fast inference and being less influenced by the prediction starting point. The source code and datasets are available at <span><span>https://github.com/USTC-AI4EEE/RUL-Mamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116376"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on overcharge mitigations and thermal runaway risk of 18650 lithium-ion batteries","authors":"W.H. Yan ,&nbsp;W.X. Huang ,&nbsp;Y. Yang ,&nbsp;Z.W. Wei ,&nbsp;H.S. Zhen ,&nbsp;Y. Lin","doi":"10.1016/j.est.2025.116372","DOIUrl":"10.1016/j.est.2025.116372","url":null,"abstract":"<div><div>The safety issues of lithium-ion batteries are becoming increasingly severe, and overcharging is one of the primary abuse conditions that can lead to safety incidents in lithium batteries. Overcharging can cause the internal heat of the battery to rise uncontrollably, potentially resulting in thermal runaway under extreme conditions. To thoroughly analyze the impact of protection devices on the overcharging behavior of lithium batteries under different charging rates, this study selected eight types of commercial 18650 ternary lithium batteries. The study systematically evaluated the thermal runaway risk of these batteries under overcharge conditions of 10 V-3 A low current and 10 V-6 A high current. After the overcharge experiments, the batteries were disassembled to analyze the performance of their thermal runaway protection mechanisms during overcharging. The results indicate that under the 10 V-3 A low-current overcharge conditions, the overcharge protection mechanisms of the batteries were effective in preventing thermal runaway. However, under the 10 V-6 A high-current overcharge conditions, the overcharge protection mechanisms failed to effectively curb the occurrence of thermal runaway. Furthermore, the study analyzed the thermal runaway risk of multiple parallel cells and assessed the impact of two typical faults, short charging and interrupted charging, on the thermal runaway risk of the battery pack. These findings provide a scientific basis for further enhancing the safety of lithium-ion batteries.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116372"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust circuit parameter estimator in noise-outliers environments based on RANSAC merged with RLS for reliable LiFePO4 state-of-charge","authors":"Eunjin Kang,&nbsp;Sangwoo Cho,&nbsp;Miyoung Lee,&nbsp;Anshul Nagar,&nbsp;Jonghoon Kim","doi":"10.1016/j.est.2025.116416","DOIUrl":"10.1016/j.est.2025.116416","url":null,"abstract":"<div><div>As the electric vehicles (EVs) market continues to expand and evolve, ensuring the optimal performance, safety, and efficiency of EVs becomes increasingly critical. In real-world EV driving scenarios, the accuracy of state-of-charge (SoC) estimation, based on equivalent circuit models, often suffers from outliers and measurement noise. To address this challenge, A novel approach that integrates the random sample consensus (RANSAC) algorithm with recursive least squares (RLS) estimation is proposed. This integrated RANSAC-RLS method robustly determines the optimal model, even in environments where outliers and noise occur. Furthermore, it adeptly manages anomalies, which pose challenges for conventional methods while facilitating efficient and precise appraisal in practical driving environments. Comparative analysis shows that the RANSAC-RLS method is effective against spurious data and yields accurate SoC estimation under real-world conditions. Additionally, analysis of the computational trade-off revealed that although RANSAC-RLS requires approximately twice the execution time of standard RLS, it maintains a root mean square error (RMSE) below 1.725 % even in noisy and outlier-contaminated environments, significantly enhancing robustness.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116416"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scheduling optimization of park integrated energy system with a flywheel-based hybrid energy storage system and thermal power deep peak shaving","authors":"Chong Gao ,&nbsp;Ran Zhang ,&nbsp;Qinliang Tan ,&nbsp;Yao Jin ,&nbsp;Jianwei Gao","doi":"10.1016/j.est.2025.116363","DOIUrl":"10.1016/j.est.2025.116363","url":null,"abstract":"<div><div>As the penetration of renewable energy continues to rise in global power systems, energy storage technologies offer significant advantages in addressing the volatility of renewable energy and enhancing the operational stability of power systems. However, current approaches to utilizing energy storage as a flexibility resource often overlook the coordinated application of multiple energy storage systems for peak shaving and frequency regulation, as well as effective optimization scheduling across various energy forms. Therefore, this study introduces a flywheel-based hybrid energy storage system within PIES, coupling it with flexible thermal power to ensure stable system operation. Subsequently, by treating compensation revenue as opportunity cost, and a dual-objective economic and environmental function is formulated to achieve coordinated optimization scheduling of multiple energy forms within the PIES. After that, an improved multi-objective particle swarm optimization algorithm is then introduced to solve the complex multi-objective problems of the system. Finally, simulations are conducted in the actual system of three seasons and four Cases in northern China. The results indicate that this model and method can achieve resource complementarity among various energy forms in PIES, reduce reliance on external markets, and realize cost savings through more efficient energy management while ensuring the reliability of the system in responding to fluctuations.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116363"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced thermal performance of binary eutectic chloride salt by integrating nanoparticles: A molecular dynamics study","authors":"Zhening Zhang,&nbsp;Meiqian Chen","doi":"10.1016/j.est.2025.116423","DOIUrl":"10.1016/j.est.2025.116423","url":null,"abstract":"<div><div>Chloride salts are considered the most promising high-temperature thermal energy storage media for next-generation concentrating solar power (CSP) plants. To enhance the thermal performance of NaCl-KCl eutectic salt phase change material (PCM), composite phase change materials (CPCMs) were developed by separately integrating Al₂O₃, CuO, and SiO₂ with PCM. The microstructure evolution and thermophysical properties of the materials were investigated by means of molecular dynamics (MD) simulations, the simulation results were in good agreement with the available experimental data, and the mechanism of thermal performance enhancement was elucidated from a microscopic perspective. The figure of merit (<em>FOM</em>) was introduced as an evaluation criterion for the thermal characteristics of the materials. CuO nanoparticles exhibited the most significant effect on the thermal conductivity of PCM, with a maximum increase of 28.1 %. Al₂O₃ nanoparticles appeared the best performance in improving the specific heat capacity of PCM, with an increase of 9.2 %. Although the integration of nanoparticles increased material viscosity, Al₂O₃ nanoparticles induced only a 14.2 % rise in viscosity of the base salt at high temperature. Based on microscopic energy analysis, the thermal performance enhancement was attributed to the increase in average potential energy of the atoms in the system, which would strengthen the interatomic interactions. The <em>FOM</em> confirmed that Al₂O₃ nanoparticles held the greatest potential for application in the optimization of the overall performance of CSP power plants. This study is expected to provide valuable guidance for the design and application of molten salt-based high-temperature thermal energy storage materials.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"120 ","pages":"Article 116423"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}