Journal of energy storage最新文献

The enhancement of metal hydride hydrogen storage performance using novel triple-branched fin

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-26 DOI: 10.1016/j.est.2025.116659

Puchanee Larpruenrudee , Nick S. Bennett , Robert Fitch , Emilie Sauret , YuanTong Gu , M.J. Hossain , Mohammad S. Islam

The advantages of a high storage capacity and safety of metal hydride hydrogen storage (MHHS) have widely attracted people's interest in hydrogen storage. The improvement of the heat transfer performance is one of the key parameters to improve the overall MHHS performance. Various heat exchangers with complex structures have been developed for this purpose. However, the drawback of these heat exchangers is huge pressure losses. Therefore, this study aims to enhance the MHHS performance by considering the heat transfer improvement and maintaining the pressure loss inside the heat exchanger. To fulfil the requirement of heat transfer efficiency instead of using complex heat exchangers, a novel triple-branched fin is designed to attach to the simple straight tube heat exchanger. The effect of pressure losses due to the complex heat exchangers is analysed and compared with the simple straight tube. The novel fin heat exchanger's performance is also compared to conventional fins. Moreover, an enhancement of the novel fin geometries is considered with the parametric studies to achieve superior MHHS performance. The results indicate that the pressure losses are reduced by 31 % when using the straight tube instead of other complex heat exchangers. The novel triple-branched fin obtains the best heat transfer performance compared to other fin designs, including the quadrilateral fin and Y-shaped fin. After the geometrical enhancement of this novel fin, the duration of the absorption-desorption cycle is reduced by 25 % compared to the quadrilateral fin. Under the parametric study, heat transfer fluid temperature significantly affects the desorption process, while the heat transfer coefficient greatly affects the absorption process.

{"title":"The enhancement of metal hydride hydrogen storage performance using novel triple-branched fin","authors":"Puchanee Larpruenrudee , Nick S. Bennett , Robert Fitch , Emilie Sauret , YuanTong Gu , M.J. Hossain , Mohammad S. Islam","doi":"10.1016/j.est.2025.116659","DOIUrl":"10.1016/j.est.2025.116659","url":null,"abstract":"<div><div>The advantages of a high storage capacity and safety of metal hydride hydrogen storage (MHHS) have widely attracted people's interest in hydrogen storage. The improvement of the heat transfer performance is one of the key parameters to improve the overall MHHS performance. Various heat exchangers with complex structures have been developed for this purpose. However, the drawback of these heat exchangers is huge pressure losses. Therefore, this study aims to enhance the MHHS performance by considering the heat transfer improvement and maintaining the pressure loss inside the heat exchanger. To fulfil the requirement of heat transfer efficiency instead of using complex heat exchangers, a novel triple-branched fin is designed to attach to the simple straight tube heat exchanger. The effect of pressure losses due to the complex heat exchangers is analysed and compared with the simple straight tube. The novel fin heat exchanger's performance is also compared to conventional fins. Moreover, an enhancement of the novel fin geometries is considered with the parametric studies to achieve superior MHHS performance. The results indicate that the pressure losses are reduced by 31 % when using the straight tube instead of other complex heat exchangers. The novel triple-branched fin obtains the best heat transfer performance compared to other fin designs, including the quadrilateral fin and Y-shaped fin. After the geometrical enhancement of this novel fin, the duration of the absorption-desorption cycle is reduced by 25 % compared to the quadrilateral fin. Under the parametric study, heat transfer fluid temperature significantly affects the desorption process, while the heat transfer coefficient greatly affects the absorption process.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116659"},"PeriodicalIF":8.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874697","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}

引用次数: 0

A framework for the design of battery energy storage systems in Power-to-X processes

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-26 DOI: 10.1016/j.est.2025.116744

Andrea Isella, Davide Manca

Energy storage has become increasingly crucial as more industrial processes rely on renewable power inputs to achieve decarbonization targets and meet stringent environmental standards. Storage systems are essential for mitigating the fluctuations in plant operations that result from the discontinuity of renewables, allowing for a smooth reconciliation of renewable power with the steadiness of the process. This paper introduces a general and systematic framework, qualifying as a self-consistent analytical tool rather than a competitive alternative to traditional optimization techniques, to identify the optimal delivery policies minimizing the capacity of battery energy storage systems in Power-to-X processes. Specifically, we propose an optimal supply schedule that converts the arbitrarily fluctuating electric power availability from renewable sources into an optimally fluctuating electric power output. This way, the required storage capacity is minimized while concurrently meeting various operating requirements, such as ramping rates and load flexibility constraints. The main novelty of this framework lies in its numerically explicit formulation, which requires little effort to be implemented and a short computational time to be run, making it a handy shortcut method for designing battery storage systems. Finally, the framework's effectiveness is validated through a case study involving the design optimization of a renewable-powered industrial facility for green hydrogen production: precisely, the optimal configuration attains a levelized cost of hydrogen equal to 2.92 USD/kg, featuring solar and wind installed capacities of 50 MW and 150 MW, respectively; an electrolyzer capacity of 69.88 MW; and an electricity storage capacity of 28.20 MWh (California, 2023).

{"title":"A framework for the design of battery energy storage systems in Power-to-X processes","authors":"Andrea Isella, Davide Manca","doi":"10.1016/j.est.2025.116744","DOIUrl":"10.1016/j.est.2025.116744","url":null,"abstract":"<div><div>Energy storage has become increasingly crucial as more industrial processes rely on renewable power inputs to achieve decarbonization targets and meet stringent environmental standards. Storage systems are essential for mitigating the fluctuations in plant operations that result from the discontinuity of renewables, allowing for a smooth reconciliation of renewable power with the steadiness of the process. This paper introduces a general and systematic framework, qualifying as a self-consistent analytical tool rather than a competitive alternative to traditional optimization techniques, to identify the optimal delivery policies minimizing the capacity of battery energy storage systems in Power-to-X processes. Specifically, we propose an optimal supply schedule that converts the arbitrarily fluctuating electric power availability from renewable sources into an optimally fluctuating electric power output. This way, the required storage capacity is minimized while concurrently meeting various operating requirements, such as ramping rates and load flexibility constraints. The main novelty of this framework lies in its numerically explicit formulation, which requires little effort to be implemented and a short computational time to be run, making it a handy shortcut method for designing battery storage systems. Finally, the framework's effectiveness is validated through a case study involving the design optimization of a renewable-powered industrial facility for green hydrogen production: precisely, the optimal configuration attains a levelized cost of hydrogen equal to 2.92 USD/kg, featuring solar and wind installed capacities of 50 MW and 150 MW, respectively; an electrolyzer capacity of 69.88 MW; and an electricity storage capacity of 28.20 MWh (California, 2023).</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116744"},"PeriodicalIF":8.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874821","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}

引用次数: 0

A cost-effective integration and operation methodology for battery energy storage systems in active distribution networks via a master–slave optimization strategy

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-26 DOI: 10.1016/j.est.2025.116639

Brandon Cortés-Caicedo , Oscar Danilo Montoya , Luis Fernando Grisales-Noreña , Elvis Eduardo Gaona-García , Jorge Ardila-Rey

This document proposes a master–slave optimization approach for the integration and operation of energy storage technologies (ESTs) in active distribution networks (ADNs), combining the multiverse optimizer (for selecting the optimal location and type of EST) with the vortex search algorithm (for determining the hourly operation scheme). This method accounts for the variability of distributed generation (DG) and the fluctuating power consumption patterns of ADN users, aiming to minimize system costs—including energy purchasing, investment, maintenance, and replacement expenses—over a 20-year planning horizon. The approach was validated on 33-bus and 69-bus test systems, both adapted to the demand and generation conditions of Medellín, Colombia, and compared against five metaheuristics: particle swarm optimization, the Monte Carlo method, the Chu & Beasley genetic algorithm, the salp swarm optimization algorithm, and population-based incremental learning. As observed in MATLAB simulations for the 33-bus system, the proposed methodology achieved the greatest savings, reducing annual costs by up to 14,138 USD and outperforming all methods. It also obtained the best average cost (2,965,728.33 USD) with a notably low standard deviation of 0.020%, while maintaining moderate processing times (170 min). In the 69-bus network, it similarly yielded the best cost results and confirmed its scalability to larger, more complex ADNs. These findings demonstrate that the master–slave synergy of the multiverse optimizer and vortex search algorithm offers network operators a robust, repeatable solution to reduce the total cost of ADNs when integrating ESTs under varying renewable energy and demand conditions.

{"title":"A cost-effective integration and operation methodology for battery energy storage systems in active distribution networks via a master–slave optimization strategy","authors":"Brandon Cortés-Caicedo , Oscar Danilo Montoya , Luis Fernando Grisales-Noreña , Elvis Eduardo Gaona-García , Jorge Ardila-Rey","doi":"10.1016/j.est.2025.116639","DOIUrl":"10.1016/j.est.2025.116639","url":null,"abstract":"<div><div>This document proposes a master–slave optimization approach for the integration and operation of energy storage technologies (ESTs) in active distribution networks (ADNs), combining the multiverse optimizer (for selecting the optimal location and type of EST) with the vortex search algorithm (for determining the hourly operation scheme). This method accounts for the variability of distributed generation (DG) and the fluctuating power consumption patterns of ADN users, aiming to minimize system costs—including energy purchasing, investment, maintenance, and replacement expenses—over a 20-year planning horizon. The approach was validated on 33-bus and 69-bus test systems, both adapted to the demand and generation conditions of Medellín, Colombia, and compared against five metaheuristics: particle swarm optimization, the Monte Carlo method, the Chu & Beasley genetic algorithm, the salp swarm optimization algorithm, and population-based incremental learning. As observed in MATLAB simulations for the 33-bus system, the proposed methodology achieved the greatest savings, reducing annual costs by up to 14,138 USD and outperforming all methods. It also obtained the best average cost (2,965,728.33 USD) with a notably low standard deviation of 0.020%, while maintaining moderate processing times (170 min). In the 69-bus network, it similarly yielded the best cost results and confirmed its scalability to larger, more complex ADNs. These findings demonstrate that the master–slave synergy of the multiverse optimizer and vortex search algorithm offers network operators a robust, repeatable solution to reduce the total cost of ADNs when integrating ESTs under varying renewable energy and demand conditions.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116639"},"PeriodicalIF":8.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874698","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}

引用次数: 0

Control and dynamic analysis of a BLDC-based pico-pump hydro energy storage system in a utility-interactive wind-based AC/DC microgrid

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-26 DOI: 10.1016/j.est.2025.116773

Edapha Rhema Jones Chullai , Haricharan Nannam , Priyankar Roy , Rakesh Roy , Atanu Banerjee

This study presents a novel integration of a variable-speed Brushless DC (BLDC) machine within a pico-Pump Hydro Energy Storage (pPHES) system, integrated into a utility-interactive wind-based AC/DC microgrid. The Third-Order Field-Oriented Sliding Mode Control (TOFOSMC) interfaced with a 3-level Neutral Point Clamp (NPC) converter is employed for efficient regulation of the variable-speed pPHES (VS-pPHES) system under fluctuating wind and grid load conditions focusing on transitions between pumping, generating, and disconnecting modes. The Maximum Power Point Tracking (MPPT) is employed for both VS-pPHES reversible pump-turbine and wind energy to maximize extraction efficiency. The performance evaluations demonstrate notable improvements: the TOFOSMC exhibits lower rotor speed standard deviation (STD) during transition, compared to conventional field-oriented control with proportional-integral controller (FOC-PI) in both Case 1 (1.8188 vs. 2.4587) and Case 3 (1.6106 vs. 1.8427), highlighting its enhanced ability to minimize speed fluctuations. Additionally, TOFOSMC outperforms FOC-PI in transient response, achieving faster peak time (0.0464 s vs. 0.0570 s), reduced overshoot (34.37 % vs. 52.27 %), and lower RMSE (6.1590 vs. 6.9527), which collectively ensures improved tracking accuracy. Moreover, the system's operational efficiency exceeds 60 % in both pumping and generating modes, with total harmonic distortion maintained below 5 %, ensuring compliance with IEEE-519 standards. These results are simulated in MATLAB/Simulink, and experimental validation in laboratory prototype confirms its effectiveness and practicality.

{"title":"Control and dynamic analysis of a BLDC-based pico-pump hydro energy storage system in a utility-interactive wind-based AC/DC microgrid","authors":"Edapha Rhema Jones Chullai , Haricharan Nannam , Priyankar Roy , Rakesh Roy , Atanu Banerjee","doi":"10.1016/j.est.2025.116773","DOIUrl":"10.1016/j.est.2025.116773","url":null,"abstract":"<div><div>This study presents a novel integration of a variable-speed Brushless DC (BLDC) machine within a pico-Pump Hydro Energy Storage (pPHES) system, integrated into a utility-interactive wind-based AC/DC microgrid. The Third-Order Field-Oriented Sliding Mode Control (TOFOSMC) interfaced with a 3-level Neutral Point Clamp (NPC) converter is employed for efficient regulation of the variable-speed pPHES (VS-pPHES) system under fluctuating wind and grid load conditions focusing on transitions between pumping, generating, and disconnecting modes. The Maximum Power Point Tracking (MPPT) is employed for both VS-pPHES reversible pump-turbine and wind energy to maximize extraction efficiency. The performance evaluations demonstrate notable improvements: the TOFOSMC exhibits lower rotor speed standard deviation (STD) during transition, compared to conventional field-oriented control with proportional-integral controller (FOC-PI) in both Case 1 (1.8188 vs. 2.4587) and Case 3 (1.6106 vs. 1.8427), highlighting its enhanced ability to minimize speed fluctuations. Additionally, TOFOSMC outperforms FOC-PI in transient response, achieving faster peak time (0.0464 s vs. 0.0570 s), reduced overshoot (34.37 % vs. 52.27 %), and lower RMSE (6.1590 vs. 6.9527), which collectively ensures improved tracking accuracy. Moreover, the system's operational efficiency exceeds 60 % in both pumping and generating modes, with total harmonic distortion maintained below 5 %, ensuring compliance with IEEE-519 standards. These results are simulated in MATLAB/Simulink, and experimental validation in laboratory prototype confirms its effectiveness and practicality.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116773"},"PeriodicalIF":8.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874976","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}

引用次数: 0

An adaptive bi-level optimization model for market integration of community energy storage in local trading and upstream energy and regulation services

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116715

Sobhan Dorahaki , Nima Amjady , S.M. Muyeen

The emergence of community energy storage (CES) in smart energy systems presents a critical opportunity to enhance flexibility within local energy markets while also enabling participation in both local energy trading and upstream energy and regulation markets. Advanced CES systems, comprising integrated battery and hydrogen storage units alongside fuel cells and electrolyzers, play a pivotal role in enabling energy communities to optimize resource utilization, offer storage services to prosumers, and participate in upstream markets. This study models the competitive interaction between the CES operator and prosumers using a Stackelberg game-theoretic framework, where the CES operator acts as the leader and the prosumers as followers. The CES maximizes its profit through local energy trading and participation in upstream energy and regulation markets, while prosumers minimize their billing costs by trading energy with the CES and participating in demand response programs. The proposed structure is modeled using a mixed integer linear programming (MILP) approach and solved with the CPLEX solver, allowing for precise optimization of CES operations. Various scenarios are analyzed to assess system performance under diverse market and operational conditions. The results demonstrate that the adaptive bi-level optimization model effectively integrates CES into energy trading and regulation markets, providing reliable reserve services with minimal impact on profitability. This approach highlights the potential of CES in advancing sustainable and economically viable energy communities.

{"title":"An adaptive bi-level optimization model for market integration of community energy storage in local trading and upstream energy and regulation services","authors":"Sobhan Dorahaki , Nima Amjady , S.M. Muyeen","doi":"10.1016/j.est.2025.116715","DOIUrl":"10.1016/j.est.2025.116715","url":null,"abstract":"<div><div>The emergence of community energy storage (CES) in smart energy systems presents a critical opportunity to enhance flexibility within local energy markets while also enabling participation in both local energy trading and upstream energy and regulation markets. Advanced CES systems, comprising integrated battery and hydrogen storage units alongside fuel cells and electrolyzers, play a pivotal role in enabling energy communities to optimize resource utilization, offer storage services to prosumers, and participate in upstream markets. This study models the competitive interaction between the CES operator and prosumers using a Stackelberg game-theoretic framework, where the CES operator acts as the leader and the prosumers as followers. The CES maximizes its profit through local energy trading and participation in upstream energy and regulation markets, while prosumers minimize their billing costs by trading energy with the CES and participating in demand response programs. The proposed structure is modeled using a mixed integer linear programming (MILP) approach and solved with the CPLEX solver, allowing for precise optimization of CES operations. Various scenarios are analyzed to assess system performance under diverse market and operational conditions. The results demonstrate that the adaptive bi-level optimization model effectively integrates CES into energy trading and regulation markets, providing reliable reserve services with minimal impact on profitability. This approach highlights the potential of CES in advancing sustainable and economically viable energy communities.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116715"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868598","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}

引用次数: 0

Quantitative prediction model for lithium-ion battery life uncertainty based on DAE-CNN-BiGRU quantile regression

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116771

Shengli Wu , Dan Li , Wenting Xing , Ying Liu

To ensure the safety and reliability of lithium-ion battery management systems (BMS), accurately predicting the remaining useful life (RUL) is essential. However, during the operation of lithium-ion batteries, various uncertainties, including energy regeneration and localized fluctuations, introduce significant challenges, making it difficult to predict RUL with the desired accuracy. In this paper, we develop a quantitative model for predicting the uncertainty in the remaining life of lithium-ion batteries. To be specific, the approach begins by employing a denoising auto-encoder (DAE) to reconstruct the original signal during data preprocessing. Next, a one-dimensional convolutional neural network (1D-CNN) is utilized to deeply analyze the capacity data of the lithium-ion batteries. The representative features extracted by the CNN are then fed into a bidirectional gated recurrent unit (BiGRU) network. A quantile regression (QR) layer is integrated into the BiGRU architecture to generate the final predictions of the battery's remaining service life. The quantile regression loss function is applied during the network training process to enhance the accuracy of the remaining service life predictions. Performance evaluation was conducted using publicly available datasets from NASA and CALCE, with comparisons against other prediction methods. Experimental results indicate that the quantile regression approach enhances the accuracy of the gated recurrent unit (GRU) neural network, demonstrating superior predictive performance.

{"title":"Quantitative prediction model for lithium-ion battery life uncertainty based on DAE-CNN-BiGRU quantile regression","authors":"Shengli Wu , Dan Li , Wenting Xing , Ying Liu","doi":"10.1016/j.est.2025.116771","DOIUrl":"10.1016/j.est.2025.116771","url":null,"abstract":"<div><div>To ensure the safety and reliability of lithium-ion battery management systems (BMS), accurately predicting the remaining useful life (RUL) is essential. However, during the operation of lithium-ion batteries, various uncertainties, including energy regeneration and localized fluctuations, introduce significant challenges, making it difficult to predict RUL with the desired accuracy. In this paper, we develop a quantitative model for predicting the uncertainty in the remaining life of lithium-ion batteries. To be specific, the approach begins by employing a denoising auto-encoder (DAE) to reconstruct the original signal during data preprocessing. Next, a one-dimensional convolutional neural network (1D-CNN) is utilized to deeply analyze the capacity data of the lithium-ion batteries. The representative features extracted by the CNN are then fed into a bidirectional gated recurrent unit (BiGRU) network. A quantile regression (QR) layer is integrated into the BiGRU architecture to generate the final predictions of the battery's remaining service life. The quantile regression loss function is applied during the network training process to enhance the accuracy of the remaining service life predictions. Performance evaluation was conducted using publicly available datasets from NASA and CALCE, with comparisons against other prediction methods. Experimental results indicate that the quantile regression approach enhances the accuracy of the gated recurrent unit (GRU) neural network, demonstrating superior predictive performance.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116771"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868599","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}

引用次数: 0

Dimension-performance integrated assembly analysis method considering battery module’s post-welding rebound mechanism

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116772

Xinan Zhou , Jing Zou , Zhiyang Cheng , Minghui Han , Hangyu Li , Cong Cheng , Sun Jin

Dimensional deviations in battery module length can stem from the internal forces release after the assembly process. This manuscript presents the Dimension-Performance Integrated Assembly Analysis Method, which considers both post-assembly dimensional distributions and post-service residual life. The modified Hill strain energy function is employed to characterize the hyperelastic mechanical behavior of the aerogel-based thermal insulation pads, alongside establishing a process-based model for the battery module's post-welding rebound. Furthermore, an equivalent model is introduced to compute the equilibrium positions of the compression and tension curves to ensure both precision and efficiency. The dimensional measurements validate the accuracy of the process-based model and the equivalent model, and a thorough analysis of the module's rebound mechanism sheds light on the interplay between the equivalent model parameters and the structure mechanical properties or initial dimensions. Accounting for the battery module's post-welding rebound mechanism, the Dimension-Performance Integrated Assembly Analysis Method assesses the impact of key dimensions and process parameters on target dimensional distributions and structural failure probability. This method enables the design of battery thickness tolerance band and process parameter ranges to ensure zero failure probability within the target charge-discharge cycles.

{"title":"Dimension-performance integrated assembly analysis method considering battery module’s post-welding rebound mechanism","authors":"Xinan Zhou , Jing Zou , Zhiyang Cheng , Minghui Han , Hangyu Li , Cong Cheng , Sun Jin","doi":"10.1016/j.est.2025.116772","DOIUrl":"10.1016/j.est.2025.116772","url":null,"abstract":"<div><div>Dimensional deviations in battery module length can stem from the internal forces release after the assembly process. This manuscript presents the Dimension-Performance Integrated Assembly Analysis Method, which considers both post-assembly dimensional distributions and post-service residual life. The modified Hill strain energy function is employed to characterize the hyperelastic mechanical behavior of the aerogel-based thermal insulation pads, alongside establishing a process-based model for the battery module's post-welding rebound. Furthermore, an equivalent model is introduced to compute the equilibrium positions of the compression and tension curves to ensure both precision and efficiency. The dimensional measurements validate the accuracy of the process-based model and the equivalent model, and a thorough analysis of the module's rebound mechanism sheds light on the interplay between the equivalent model parameters and the structure mechanical properties or initial dimensions. Accounting for the battery module's post-welding rebound mechanism, the Dimension-Performance Integrated Assembly Analysis Method assesses the impact of key dimensions and process parameters on target dimensional distributions and structural failure probability. This method enables the design of battery thickness tolerance band and process parameter ranges to ensure zero failure probability within the target charge-discharge cycles.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116772"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874819","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}

引用次数: 0

Non-flammable, conductive, and stretchable organic-ionogel for solid-state polymer lithium metal batteries

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116762

Yanan Li , Tingting Xiao , Shunchao Ma , Zhanxin Chen , Silin Chen , Yutong Yang , Yu Zhang , Jianli Cao , Yulong Liu , Lina Cong , Haiming Xie

Gel polymer electrolytes (GPEs) have been brought into the spotlight as next-generation electrolytes for batteries in the realm of flexible and wearable electronic devices. However, challenges remain in achieving a trade-off between high safety, high ionic conductivity, and superior mechanical stretchability concurrently. To conquer it, the ionic liquid (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide; EMIM-TFSI), coupled with the organic fluorinated solvent (N,N-dimethyl trifluoroacetamide; FDMAC), is proposed for designing a neotype organic-ionogel (OIG) with a rigid-flexible vinylene carbonate (VEC)-butyl acrylate (BA) cross-linked copolymer as the polymer skeleton. By structural optimization and component regulation, the optimal OIG-15 % presents the enhanced ionic conductivity (5.5 × 10⁻⁴ S cm⁻¹), non-flammability, and prominent mechanical stretchability, especially sustaining an elongation at break of 427 % and volumetric compressibility exceeding 85 %. Consequently, the Li||Li symmetric cell assembled with OIG-15 % can demonstrate stable cycling for over 2000 h at a current density of 0.1 mA cm⁻², while the Li|OIG-15 %|LiFePO₄ cell can still maintain an exceptional capacity retention of nearly 100 % after 450 cycles at 0.5C. This work provides a valuable construction strategy for the development of high-quality gel electrolyte materials, paving the way for breakthroughs in the widespread application for solid-state polymer lithium metal batteries.

{"title":"Non-flammable, conductive, and stretchable organic-ionogel for solid-state polymer lithium metal batteries","authors":"Yanan Li , Tingting Xiao , Shunchao Ma , Zhanxin Chen , Silin Chen , Yutong Yang , Yu Zhang , Jianli Cao , Yulong Liu , Lina Cong , Haiming Xie","doi":"10.1016/j.est.2025.116762","DOIUrl":"10.1016/j.est.2025.116762","url":null,"abstract":"<div><div>Gel polymer electrolytes (GPEs) have been brought into the spotlight as next-generation electrolytes for batteries in the realm of flexible and wearable electronic devices. However, challenges remain in achieving a trade-off between high safety, high ionic conductivity, and superior mechanical stretchability concurrently. To conquer it, the ionic liquid (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide; EMIM-TFSI), coupled with the organic fluorinated solvent (<em>N</em>,<em>N</em>-dimethyl trifluoroacetamide; FDMAC), is proposed for designing a neotype organic-ionogel (OIG) with a rigid-flexible vinylene carbonate (VEC)-butyl acrylate (BA) cross-linked copolymer as the polymer skeleton. By structural optimization and component regulation, the optimal OIG-15 % presents the enhanced ionic conductivity (5.5 × 10<sup>−4</sup> S cm<sup>−1</sup>), non-flammability, and prominent mechanical stretchability, especially sustaining an elongation at break of 427 % and volumetric compressibility exceeding 85 %. Consequently, the Li||Li symmetric cell assembled with OIG-15 % can demonstrate stable cycling for over 2000 h at a current density of 0.1 mA cm<sup>−2</sup>, while the Li|OIG-15 %|LiFePO₄ cell can still maintain an exceptional capacity retention of nearly 100 % after 450 cycles at 0.5C. This work provides a valuable construction strategy for the development of high-quality gel electrolyte materials, paving the way for breakthroughs in the widespread application for solid-state polymer lithium metal batteries.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116762"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868705","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}

引用次数: 0

A Stackelberg game model with cloud energy storage operators: A multi-user, multi-scenario analysis, adopting the time-based pricing strategy

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116672

Ling-Ling Li , Jia-Rui Pei , Ming K. Lim , Kanchana Sethanan , Ming-Lang Tseng

This study establishes a Stackelberg game model with Cloud Energy Storage Operators (CESO) as the leader, collaborating with industrial park users to achieve mutual benefit. CESO and industrial park user. The cloud energy storage (CES) effectively addresses the high self-investment costs and underutilization of resources in the energy internet context. This study proposes a time-based pricing strategy for CES leasing services. CESO determines the hourly capacity and power leasing prices over 24 h The aim is to minimize the discrepancy between user declarations and actual usage through penalty measures. Industrial Park users determine leased energy storage capacity and charging/discharging power based on CESO's prices, their own loads, and renewable energy availability. This study proposes an improved snow ablation optimizer (ISAO) to obtain the global optimal solution, i.e., the optimal price for the time-based pricing of CES leasing services. In a multi-user, multi-scenario analysis, adopting this strategy increased CESO's benefits by 44.80 % and users' benefits by 6.76 %. Users increased the electricity sales during peak hours by 51.53 % and reduced the electricity purchases during valley hours by 19.9 %.

{"title":"A Stackelberg game model with cloud energy storage operators: A multi-user, multi-scenario analysis, adopting the time-based pricing strategy","authors":"Ling-Ling Li , Jia-Rui Pei , Ming K. Lim , Kanchana Sethanan , Ming-Lang Tseng","doi":"10.1016/j.est.2025.116672","DOIUrl":"10.1016/j.est.2025.116672","url":null,"abstract":"<div><div>This study establishes a Stackelberg game model with Cloud Energy Storage Operators (CESO) as the leader, collaborating with industrial park users to achieve mutual benefit. CESO and industrial park user. The cloud energy storage (CES) effectively addresses the high self-investment costs and underutilization of resources in the energy internet context. This study proposes a time-based pricing strategy for CES leasing services. CESO determines the hourly capacity and power leasing prices over 24 h The aim is to minimize the discrepancy between user declarations and actual usage through penalty measures. Industrial Park users determine leased energy storage capacity and charging/discharging power based on CESO's prices, their own loads, and renewable energy availability. This study proposes an improved snow ablation optimizer (ISAO) to obtain the global optimal solution, i.e., the optimal price for the time-based pricing of CES leasing services. In a multi-user, multi-scenario analysis, adopting this strategy increased CESO's benefits by 44.80 % and users' benefits by 6.76 %. Users increased the electricity sales during peak hours by 51.53 % and reduced the electricity purchases during valley hours by 19.9 %.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116672"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874815","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}

引用次数: 0

High-entropy spinel oxides as efficient ORR catalysts towards enhanced kinetics for zinc-air batteries

IF 8.9 2区工程技术 Q1 ENERGY & FUELS

Journal of energy storage

Pub Date : 2025-04-25 DOI: 10.1016/j.est.2025.116784

Wolong Li , Yong Wang , Na Xu , Yongcun Li

Efficient oxygen electrocatalysts for the oxygen reduction reaction (ORR) are vital for energy conversion and storage devices, but slow kinetics remain a significant challenge. There is an urgent need to develop a new electrocatalyst with high active sites. Here, we report the synthesis of high-entropy spinel oxide ((FeMnNiCuCr)₃O₄) loaded onto reduced graphene oxide (rGO) via a co-precipitation hydrothermal method. The multi-elemental mechanism of high-entropy spinel oxide and the role of rGO in enhancing catalytic activity are elucidated. The prepared electrocatalyst demonstrates efficient ORR catalytic performance (E_1/2 = 0.83 V) and reaction kinetics (100.2 mV dec^-1). It exhibits excellent catalytic activity in a liquid zinc-air battery (ZAB) with a low gap voltage (∼0.75 V) and long cycle stability (1162 h). This catalytic performance can be attributed to the cooperative mechanism of high-entropy-driven polymetallic elements, wherein Cr³⁺ regulates the electronic structure of Fe²⁺ at the tetrahedral site, serving as the primary ORR active site, while rGO facilitates electron transfer, thereby enhancing ORR catalytic activity. This study presents a feasible approach to improving the slow ORR kinetics in ZABs using a high-entropy strategy.

{"title":"High-entropy spinel oxides as efficient ORR catalysts towards enhanced kinetics for zinc-air batteries","authors":"Wolong Li , Yong Wang , Na Xu , Yongcun Li","doi":"10.1016/j.est.2025.116784","DOIUrl":"10.1016/j.est.2025.116784","url":null,"abstract":"<div><div>Efficient oxygen electrocatalysts for the oxygen reduction reaction (ORR) are vital for energy conversion and storage devices, but slow kinetics remain a significant challenge. There is an urgent need to develop a new electrocatalyst with high active sites. Here, we report the synthesis of high-entropy spinel oxide ((FeMnNiCuCr)<sub>3</sub>O<sub>4</sub>) loaded onto reduced graphene oxide (rGO) via a co-precipitation hydrothermal method. The multi-elemental mechanism of high-entropy spinel oxide and the role of rGO in enhancing catalytic activity are elucidated. The prepared electrocatalyst demonstrates efficient ORR catalytic performance (E<sub>1/2</sub> = 0.83 V) and reaction kinetics (100.2 mV dec<sup>-1</sup>). It exhibits excellent catalytic activity in a liquid zinc-air battery (ZAB) with a low gap voltage (∼0.75 V) and long cycle stability (1162 h). This catalytic performance can be attributed to the cooperative mechanism of high-entropy-driven polymetallic elements, wherein Cr<sup>3+</sup> regulates the electronic structure of Fe<sup>2+</sup> at the tetrahedral site, serving as the primary ORR active site, while rGO facilitates electron transfer, thereby enhancing ORR catalytic activity. This study presents a feasible approach to improving the slow ORR kinetics in ZABs using a high-entropy strategy.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"123 ","pages":"Article 116784"},"PeriodicalIF":8.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868703","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}

引用次数: 0