Energy Storage最新文献

{"title":"Flame-Resistant Phase Change Material-Integrated Clay Bricks for Enhanced Thermal Energy Storage and Thermal Buffering","authors":"Elham Alamdari,&nbsp;Golnoosh Abdeali,&nbsp;Ahmad Reza Bahramian,&nbsp;Sadegh Dardaei","doi":"10.1002/est2.70497","DOIUrl":"https://doi.org/10.1002/est2.70497","url":null,"abstract":"<div>\u0000 \u0000 <p>The incorporation of phase change materials (PCMs) into building components has attracted considerable attention as a passive approach for improving thermal energy storage and reducing heat transfer through building envelopes. However, the practical application of organic PCMs is often limited by leakage during phase transition and inadequate fire resistance. In this study, a flame-resistant PCM composite was developed using a polyethylene glycol (PEG 600/PEG 2000) blend supported by polyurethane (PU) foam and protected by a chloroprene rubber coating containing kaolin as a flame-retardant additive. The PCM system was subsequently integrated into perforated clay bricks by filling selected cavities to enhance their thermal performance. Differential scanning calorimetry (DSC) revealed a dual-stage phase-transition behavior, providing thermal energy storage over a broad temperature range. Thermal performance tests demonstrated that PCM-filled bricks reduced surface temperatures by approximately 3°C–5°C, at highest temperature, compared with unmodified bricks, with the highest thermal-buffering effect achieved when the PCM was positioned closest to the heat source. The composite also exhibited suitable leakage resistance, showing no observable leakage after exposure to 80°C for 3 h. Furthermore, the incorporation of 20 wt.% kaolin significantly improved fire resistance, reducing burning time by approximately 90% compared with the kaolin-free coating. The developed PCM-integrated brick combines thermal energy storage, leakage prevention, and enhanced flame resistance within a single system, showing its potential for passive thermal regulation and improved energy efficiency in building-envelope applications.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 6","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784986","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":"Robust and Rapid Diagnosis of Early Internal Short Circuits in Lithium-Ion Batteries Based on an Improved Circuit Model","authors":"Xiulan Liu,&nbsp;Qian Zhang,&nbsp;Shengjia Li,&nbsp;Xi Chen,&nbsp;Liye Wang","doi":"10.1002/est2.70473","DOIUrl":"https://doi.org/10.1002/est2.70473","url":null,"abstract":"<div>\u0000 \u0000 <p>Internal short circuits (ISCs) in lithium-ion batteries (LIBs) pose a significant safety threat, necessitating rapid and reliable diagnostic methods. This paper proposes an improved diagnostic model that leverages the electrochemical characteristics of the constant current constant voltage (CCCV) charging phase. By using the pseudo-steady-state properties of the constant current stage, the proposed model reduces the system state vector from four dimensions to two dimensions and directly observes the short-circuit current as a state variable. A Recursive Least Squares (RLS) algorithm is then employed to identify the short-circuit resistance while suppressing sensor-noise interference. Experimental results show that the proposed method substantially shortens the detection time and improves resistance-identification accuracy compared with the traditional second-order RC-model-based method. In addition, the proposed model reduces the theoretical computational cost from 553 to 129 FLOPs per iteration, supporting online implementation in battery management systems.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 6","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783824","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":"Analytical Metrics for Quality Assessment of Vanadium Electrolytes in Vanadium Flow Batteries: A Case Study","authors":"Nataliya Roznyatovskaya,&nbsp;Matthias Fühl,&nbsp;Martin Joos,&nbsp;Florian Geier,&nbsp;Jens Noack,&nbsp;Thomas Beyer","doi":"10.1002/est2.70495","DOIUrl":"https://doi.org/10.1002/est2.70495","url":null,"abstract":"<p>This case study investigates practical approaches for evaluating commercially available vanadium electrolytes (VEL) for vanadium flow battery applications. From the perspective of a potential electrolyte customer, a range of wet chemical and instrumental analytical methods was applied to characterize commercial electrolyte samples obtained from multiple suppliers. The results demonstrate that potentiometric titration, inductively coupled plasma optical emission spectroscopy (ICP-OES), and inductively coupled plasma mass spectrometry (ICP-MS) provide a robust analytical framework for determining the main electrolyte components and inorganic impurities, although method-dependent deviations were observed for selected parameters. In contrast, short-term galvanostatic charge–discharge testing showed limited sensitivity for differentiating electrolytes of similar composition and purity. Thermal stability testing of electrolyte at high states of charge revealed clear differences between samples and indicated that phosphate concentration and the ratio of total vanadium to sulfate concentration (<i>C</i>_<i>V</i>/<i>C</i>_<i>S</i>) are important parameters influencing electrolyte stability. Furthermore, gas phase analysis of hydrogen evolution from vanadium(II)-containing electrolytes proved to be a useful method for detecting impurity-related effects that are not captured by conventional electrochemical testing. Overall, the study identifies a set of practical analytical metrics and complementary testing approaches that enable a more structured and application relevant assessment of vanadium electrolyte quality in a developing commercial market.</p>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 6","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/est2.70495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Validation and Cross-Scale Evaluation of Battery Capacity Degradation Techniques for Electric Vehicles","authors":"Maharshi Singh,&nbsp;K. Janardhan Reddy","doi":"10.1002/est2.70494","DOIUrl":"https://doi.org/10.1002/est2.70494","url":null,"abstract":"<div>\u0000 \u0000 <p>Electric vehicles are key to reducing emissions and promoting sustainable mobility. In electric vehicles, accurate capacity degradation prediction is essential to ensure reliability, extend battery life, and reduce range anxiety under real-world conditions. Accurate prediction of battery capacity degradation remains a challenging task due to nonlinear aging behavior, variability in operating conditions, and the complex electrochemical processes involved in lithium-ion batteries. This research presents a unified, data-driven approach for forecasting capacity degradation at both the pack and cell levels using experimental data from a 26 Ah two-wheeler battery pack and its 18 650 cells, tested under identical thermal and electrical conditions. Three data driven techniques were evaluated which include machine learning, statistical learning and reliability-based approaches. Each model used the same input experimental data of both cell and pack level testing. The models were implemented in MATLAB software, and their performance was compared using standardized evaluation metrics including RMSE, MAE, and <i>R</i>². These challenges necessitate robust and reliable modeling approaches for effective battery health monitoring. Model-specific novel architectures included SVR with an RBF kernel and optimized hyperparameters, GPR with a squared exponential kernel and Bayesian tuning, ANN with a feedforward network of 15 hidden neurons trained using the Levenberg–Marquardt algorithm, Polynomial Regression with second-degree terms and ridge regularization (<i>λ</i> = 0.1) and Weibull fitting with nonlinear least squares. Among all, SVR achieved the best performance (<i>R</i>² = 0.9995 pack level, 0.9988 cell level) followed closely by GPR and ANN. Reliability models showed reduced accuracy at the cell and pack scale. These findings represent the potential of the proposed data-driven framework for integration into real-time Battery Management Systems (BMS), enabling accurate state-of-health monitoring and reducing range uncertainty in electric vehicle applications. These challenges necessitate robust and reliable modeling approaches for effective battery health monitoring.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 6","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719334","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 Novel Approach for Optimal and Reliable Planning Framework of Multi-Vector Hybrid Renewable Energy Systems for Sustainable Electric Vehicle Charging","authors":"Khaliq Ahmed,&nbsp;Manoranjan Kumar Sinha,&nbsp;Piyush Chouhan","doi":"10.1002/est2.70490","DOIUrl":"https://doi.org/10.1002/est2.70490","url":null,"abstract":"<div>\u0000 \u0000 <p>The green energy revolution is being propelled by hybrid energy systems that incorporate renewable energy sources. These systems are also becoming more and more important in promoting sustainable transportation by providing infrastructure for electric vehicle (EV) charging. For a stochastic EV load, this work entails the careful design of a reliable and optimized multi-vector hybrid energy configuration consisting of solar panels, wind turbines, and fuel cells. Importantly, the study offers a novel approach that combines the Bat algorithm (BA) with the Tunicate Swarm Algorithm (TSA), offering an advanced tunicate swarm Bat algorithm (TSBA) optimization technique. Key metrics including net present cost (NPC), levelized cost of energy (LCOE), and reliability indicators like loss of load probability (LOLP), loss of load expectation (LOLE), and loss of energy expected (LOEE) serve as the foundation for the evaluation process. A reliable hybrid energy system with the fewest renewable energy components and promising reliability has been described using the suggested hybrid technique. Financially speaking, the hybrid system is made viable by the NPC, LCOE, and LOE values. Additionally, the energy oriented reliability indices, LOEE and LOLE, have drastically decreased. The developed system delivers an advanced optimization framework which operates reliably while providing sustainable and cost effective solutions for green energy technology.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 6","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719333","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":"Techno-Economic and Uncertainty Analyses of an Optimized Levelized Cost of Hybrid Energy Storage System Model","authors":"Idris Mahmood,&nbsp;Lanre Olatomiwa,&nbsp;Jacob Tsado,&nbsp;Omokhafe James Tola","doi":"10.1002/est2.70426","DOIUrl":"https://doi.org/10.1002/est2.70426","url":null,"abstract":"<div>\u0000 \u0000 <p>This work provides a techno-economic framework for evaluating a levelized cost of hybrid energy storage system (LCOHESS) model. Four battery chemistries—lithium iron phosphate (LFP), nickel manganese cobalt oxide (NMC), valve-regulated lead-acid (VRLA) and flooded lead-acid (FLA)—are hybridized with supercapacitors (SCs) to evaluate the model. The model integrates an energy management strategy (EMS) designed to: provide optimal energy to the system through an optimal SC power allocation, estimate HESS charging cost and temperature-dependent self-discharge rates. Each of the hybrid energy storage system (HESS) configurations is optimized using genetic algorithm (GA), particle swarm optimization (PSO), and Grey wolf optimizer (GWO). PSO emerges as the best optimizer across the configurations, reaching the global minimum faster than GWO and GA. Optimization results show that the NMC-SC has the lowest LCOHESS, thereby making it the most cost-effective configuration. The model is simulated using load profile data from a solar-powered mini-grid in Gwam, a rural community in Niger State, Nigeria. Effects of battery and SC degradations, and aging due to the thermal self-discharge rates are investigated to prove the model's fidelity and superiority over a benchmark study. A post-optimization uncertainty analysis is conducted using What-if analysis and MCS to assess the effects of parameter variations on the LCOHESS. The What-if analysis identifies SC capital cost, discount rate, and charging cost as the most sensitive parameters influencing the LCOHESS. The subsequent MCS using beta, normal, and a combined normal-beta distribution shows that the lithium-ion HESS configurations, particularly the NMC-SC, are more robust than the lead-acid HESS configurations. This validates the cost-effectiveness of the NMC-SC as the optimal configuration. It is expected that the findings of this research provide investors with actionable insights, enabling informed HESS selection, justification of investment choices, and assessment of potential profitability and investment risks.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387031","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 Decoupled Multistage Parameter Estimation Framework for Lithium-Ion Battery Models","authors":"Moises Machado Santos,&nbsp;Tasadeek Hassan Dar,&nbsp;Bruno Muraro Perondi,&nbsp;Paulo Sérgio Sausen,&nbsp;Airam Teresa Zago Romcy Sausen","doi":"10.1002/est2.70454","DOIUrl":"https://doi.org/10.1002/est2.70454","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a novel Decoupled Optimization Algorithm (DOA) to estimate lithium-ion battery equivalent circuit parameters. The algorithm precisely models the dynamic behavior of LIBs during both the discharge and relaxation phases. Its main innovation lies in its ability to estimate parameters for each operating state independently, resulting in a more accurate model. The DOA is notably robust, as it naturally avoids premature convergence without requiring empirical hyperparameter tuning or training data. In addition, the model includes boundary conditions that guarantee a mathematically and physically sound solution, which streamlines the search process. Experimental validation in five different discharge rates confirmed that the DOA achieves superior accuracy in parameter estimation compared to established algorithms such as GA, PSO, CSA, and VLCS. These results underscore its potential for practical battery management applications.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386923","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":"Optimized Nonlinear Control for Efficient Power Management in Electric Vehicle Charging Using a Four-Switch Converter","authors":"Muhammad Imran,&nbsp;Rimsha Ghias,&nbsp;Arsalan Farooq,&nbsp;Atif Rehman","doi":"10.1002/est2.70453","DOIUrl":"https://doi.org/10.1002/est2.70453","url":null,"abstract":"<div>\u0000 \u0000 <p>As renewable energy systems become increasingly integrated with electric vehicle (EV) infrastructure, advanced power management strategies are required to ensure system stability and efficiency. This paper presents a photovoltaic (PV)-battery-EV energy management system based on a four-switch buck-boost converter. A novel condition-based integral terminal super-twisting sliding mode controller (CBITSTSMC) is proposed to mitigate windup effects, improve transient response, and enhance robustness under varying operating conditions. An artificial neural network (ANN)-based maximum power point tracking (MPPT) scheme is employed to maximize PV energy harvesting under dynamic irradiance conditions, while controller parameters are optimized using an Improved Gray Wolf Optimization (IGWO) algorithm. The effectiveness of the proposed approach is validated through Controller-in-the-Loop (CIL) implementation using a Delfino microcontroller platform. Simulation and CIL results demonstrate superior performance compared with conventional sliding mode control (SMC) and super-twisting sliding mode control (STSMC). The proposed CBITSTSMC achieves RMSE values of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>5.46</mn>\u0000 <mo>×</mo>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>4</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ 5.46times {10}^{-4} $$</annotation>\u0000 </semantics></math>, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.0054</mn>\u0000 </mrow>\u0000 <annotation>$$ 0.0054 $$</annotation>\u0000 </semantics></math>, and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.0094</mn>\u0000 </mrow>\u0000 <annotation>$$ 0.0094 $$</annotation>\u0000 </semantics></math> for the PV, battery, and EV subsystems, respectively, while reducing tracking errors by up to 95%. Furthermore, the controller exhibits fast convergence, minimal overshoot, and improved robustness, demonstrating its suitability for efficient energy management in integrated PV–battery–EV systems.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387014","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":"An Impedance Model for Lithium-Ion Capacitors Incorporating Voltage, Temperature, and Aging Effects","authors":"Joya Maria Saade,&nbsp;Nagham El Ghossein","doi":"10.1002/est2.70455","DOIUrl":"https://doi.org/10.1002/est2.70455","url":null,"abstract":"<div>\u0000 \u0000 <p>A lithium-ion capacitor (LIC) is a hybrid energy storage device combining both the capacitive behavior of supercapacitors (SCs) and the battery-like response of lithium-ion batteries. This article presents an equivalent circuit model (ECM) for the impedance capturing the dual electrochemical behavior of LICs across different operating voltages, extending conventional models of SCs to include the non-ideal capacitive effects of the lithiated negative electrode included in the hybrid technology. The parameters of the ECM were found to be highly dependent on voltage and temperature values, which were directly correlated with the electrochemical phenomena inside the cells. Impedance data extracted during accelerated aging tests were also used to analyze the effects of aging on the parameters of the ECM. A generalized model that can incorporate an Arrhenius-based degradation factor to account for the combined effects of temperature and aging was developed. The model was validated by comparing experimental data to estimated ones, with root mean square error values below 5%, demonstrating its suitability for accurately predicting LICs behavior under varying voltages, temperatures, and life cycles.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387013","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":"Engineering Polypyrrole With Varying Concentration of Zinc Molybdate for Supercapacitor Electrode Materials","authors":"Batistalang Myrthong,&nbsp;Ram Bilash Choudhary","doi":"10.1002/est2.70448","DOIUrl":"https://doi.org/10.1002/est2.70448","url":null,"abstract":"<div>\u0000 \u0000 <p>Conducting polymer-bimetallic oxide composites have emerged as promising electrode materials due to their exceptional electrochemical performance for energy storage supercapacitor applications. In this work, we report the synthesis of ZnMoO₄, PPy, and PPy/ZnMoO₄ via hydrothermal and in situ chemical oxidative polymerization methods. PPy/ZnMoO₄ composites are prepared by varying (2.5 to 15 wt%) of ZnMoO₄ content by weight of pyrrole monomer. XRD, FTIR, FESSEM, HRTEM, XPS, BET, and electrochemical analysis were employed to investigate the structural, morphological, and electrochemical properties of the electrode materials. It was found that the optimized PPy/ZnMoO₄ (PZ-5) composite delivered the highest specific capacitance of 272.5 F g⁻¹ at 1 A g⁻¹ with a capacitance retention of 96.9% after 2000 cycles at 5 A g⁻¹. The fabricated symmetric system supercapacitor achieved a specific capacitance of 144.5 F g⁻¹ at 1 A g⁻¹ and retained 95.2% of its initial capacitance after 2000 cycles, demonstrating remarkable cyclic stability. The symmetric supercapacitor also exhibited a high energy density of 12.8 Wh kg⁻¹ at a power density of 1594.3 W kg⁻¹. This work highlights the effects of incorporating ZnMoO₄ into PPy for enhancing capacitance and offering a feasible approach to enhance the electrochemical performance of the PPy-based electrode materials for supercapacitor applications.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324726","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}