Energy Storage最新文献

{"title":"Impact Analysis of Combining Distributed Generation With Charging Loads for Electric Vehicles Accounting Reliability","authors":"Khaliq Ahmed,&nbsp;Devkaran Sakravdia,&nbsp;Chandrakant Sharma","doi":"10.1002/est2.70138","DOIUrl":"https://doi.org/10.1002/est2.70138","url":null,"abstract":"<div>\u0000 \u0000 <p>This work is concerned with the penetration of renewable energy-based distributed generation along with electric vehicle (EV) charging loads into power distribution systems. This presents a new optimization procedure integrating Particle Swarm Optimization and the Andean Condor Algorithm (PSO-ACA) into a high-performing route for system design. It analyzes the performance of the system in terms of minimizing the loss of power and maximizing reliability. The study evaluates reliability indices and power loss reductions in detail by utilizing benchmark 33-bus and 69-bus test systems. The findings indicate that for the 33-bus system, the active power loss reduction obtained is 64.3 KW, with real power loss showing a 68% reduction, whereas for the 69-bus system, real power loss decrease is 72% (62.8 KW). This led to a substantial reduction in reliability indices thus enhancing the overall system performance as hybrid optimization techniques improved the reliability of the system remarkably. These results show the immense potential that advanced hybrid optimization approaches combined with reliability analyses have for delivering the economically viable, sustainable renewable energy systems of the future. This collaboration is in line with the larger objectives of promoting sustainable energy solutions and establishing a more resilient and efficient energy framework.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380581","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":"Enhanced Strategies of Electric Vehicle Fast Charging Stations and Reliability Assessment in Distribution Networks With Solar-Based Distributed Generation","authors":"Abhishek Kumar Singh,&nbsp;Ashwani Kumar","doi":"10.1002/est2.70127","DOIUrl":"https://doi.org/10.1002/est2.70127","url":null,"abstract":"<div>\u0000 \u0000 <p>Industry insiders who want to lower the greenhouse gas emissions linked to conventional fuel cars consider electric vehicles (EVs) as a practical alternative for mobility. EVs are a potential problem even though their performance is limited by their low battery power, long service charging times, and high resource costs. To improve the EV performance, this manuscript presents the hybrid technique for the optimal position of electric vehicles fast-charging stations (EVFCSs) in the distribution network. The proposed scheme is a joined execution of Wild Horse Optimizer (WHO) and Gradient Boosting Decision Tree (GBDT), which is commonly named the WHO-GBDT technique. The primary goal of the research is to decrease loss of power and voltage deviation. The optimal position for an electric vehicle charging station (EVCS) is determined using the WHO method. GDBT is used to predict the load demand. The proposed WHO-GDDT regulates the placement of EVCS, balancing their integration with distributed generation while enhancing the sustainability and reliability of distribution networks. The proposed WHO-GBDT algorithm is actualized in the MATLAB platform and compared their performance with various existing strategies like the Forensic Investigation Algorithm, Archimedean Optimization Algorithm (FBIAOA), Tunicate Swarm Algorithm (TSA), and Cuttlefish Algorithm (CA). The simulation findings of the proposed scheme are validated under three cases in the IEEE 33 bus system, like load 1, load 2 and load 3. From the result, the proposed method effectively reduced loss of power and voltage variation by 58.24% and 90.47%, respectively.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380679","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":"Early Prediction of the Remaining Useful Life of Lithium-Ion Cells Using Ensemble and Non-Ensemble Algorithms","authors":"Femilda Josephin J.S.,&nbsp;Ankit Sonthalia,&nbsp;Thiyagarajan Subramanian,&nbsp;Fethi Aloui,&nbsp;Dhowmya Bhatt,&nbsp;Edwin Geo Varuvel","doi":"10.1002/est2.70133","DOIUrl":"https://doi.org/10.1002/est2.70133","url":null,"abstract":"<div>\u0000 \u0000 <p>Lithium-ion cells have become an important part of our daily lives. They are used to power mobile phones, laptops and more recently electric vehicles (both two- and four-wheelers). The chemical behavior of the cells is rather complex and non-linear. For reliable and sustainable use of the cells for practical applications, it is imperative to predict the precise pace at which their capacity will degrade. More importantly, the lifetime of the cells must be predicted at an early stage, which would accelerate development and design optimization of the cells. However, most of the existing methods cannot predict the lifetime at an early stage, since there is a weak correlation between the cell capacity and lifetime. In this study for accurate forecasting of the battery lifetime, the patterns of the parameters such as cell current, voltage, temperature, charging time, internal resistance, and capacity were examined during charging and discharging cycle of the cell. Twelve manually crafted features were prepared from these parameters. The dataset for the features was created using the raw data of the first 100 cycles of 124 cells. Six ensemble and non-ensemble machine learning algorithms, namely, multiple linear regression (MLR), decision tree, support vector machine (SVM), gradient boosting machine (GBM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were trained with the features for predicting the life-cycle of the cells. The <i>R</i>² and root mean squared error (RMSE) values of MLR, decision tree, SVM, GBM, LGBM, and XGBoost were found to be 0.72 and 201, 0.83 and 155, 0.85 and 146, 0.92 and 100, 0.9 and 112, and 0.94 and 95, respectively. The prediction accuracy of lithium-ion cell life-time was found to be the best with the XGBoost algorithm. This shows that only first 100 cycles are required foraccurately predicting the number of cycles the lithium-ion cell can work for. Lastly, the results of the study were compared with the available studies in the literature. Three studies were chosen, and the RMSE of the method proposed in this study was found to be higher than the three studies by 43, 17, and 20. Therefore, the proposed method is a suitable option for predicting the lifetime of lithium-ion cells during the early stages of its development.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111658","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":"Multi-Objective Optimization of a Spherical Thermal Storage Tank Using a Student Psychology-Based Approach","authors":"Xi Wang,&nbsp;Rupp Carriveau,&nbsp;David S.-K. Ting,&nbsp;David Brown,&nbsp;Andrew McGillis","doi":"10.1002/est2.70136","DOIUrl":"https://doi.org/10.1002/est2.70136","url":null,"abstract":"<p>Energy storage technologies often store heat, with water as a preferred medium due to its availability and low cost. However, maintaining water in a liquid state at high temperatures requires large pressure vessels, posing significant design challenges. Balancing thermal storage capacity with pressure constraints is essential. This paper explores the dynamics of thermal storage water tanks, aiming to optimize their design using a multi-criteria approach. An equilibrium thermodynamic model was developed to evaluate the impact of geometric structure and operating parameters. The results show that optimizing a single variable is insufficient to minimize pressure swing, reduce heat loss, and maximize storage capacity. To address these trade-offs, a multi-objective student psychology-based optimization (SPBO) algorithm was employed for three-objective optimization, outperforming particle swarm optimization (PSO) in convergence. The technique for order preference by similarity to ideal solution (TOPSIS) method was applied to the Pareto frontier, yielding ideal solutions using both data-driven and manually weighted approaches. Compared with the initial design, the data-driven weighted (entropy-weighted and coefficient of variation methods) optimal designs improved all objectives, reducing pressure swing by 12.8% and 19.8%, respectively. A manually weighted approach reduced pressure swing by up to 86.7%, albeit with a decrease in thermal storage capacity.</p>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/est2.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111685","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":"Thermodynamic Parameters (Enthalpy and Entropy) of Hydrogen Storage in Ultrasound-Assisted Synthesized Selenium Decorated Fullerene","authors":"Tiba A. Zaben,&nbsp;Hadla A. Zaben,&nbsp;Dina S. Mohamed,&nbsp;Mustafa A. Alheety,&nbsp;Ahmed R. Mahmood","doi":"10.1002/est2.70132","DOIUrl":"https://doi.org/10.1002/est2.70132","url":null,"abstract":"<div>\u0000 \u0000 <p>This study includes the synthesis of a novel fullerene composite for hydrogen storage application. In the first step of this work the fullerene of the type C₆₀ was used to prepare its composite with selenium nanoparticles (Se Nps). The synthesis method was novel as it includes the synthesis of fullerene-Se nanocomposite via ultrasound at 750 W using ascorbic acid as reducing agent to convert selenium ion into selenium nanoparticles. Different techniques (XRD, SEM, TEM) were used to diagnose the composition, size and morphology of the prepared composite. The characterization results from SEM and TEM demonstrate the formation of sphere-like structures decorated on rod nanoparticles, proving the formation of required nanocomposite. Moreover, the XRD pattern demonstrates the existence of fullerene and selenium nanoparticles peaks with high purity. The important of this nanocomposite was comprehended from the using it in the hydrogen energy application. The study was conducted at 77, 173, 223 and 273 K and 0–90 bar and the study proves the physical adsorption at 55 bar as it showed 4.1 wt% storage with enthalpy of 0.13 KJ/(mol H₂) and entropy of 0.70 J/mol H₂. K.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121217","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":"Effect of Thickness on Performance of Thermal Management System for a Prismatic Lithium-Ion Battery Using Phase Change Material","authors":"Uğur Morali","doi":"10.1002/est2.70135","DOIUrl":"https://doi.org/10.1002/est2.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>Phase change material cooling method as a zero energy consumption cooling system has a good prospect in battery thermal management systems. Therefore, the thermal response of the lithium-ion battery in the presence of a phase change material was explored in this study. The lithium-ion battery was subjected to high discharge conditions (5 C-rate) at high ambient temperatures (35°C and 40°C). The results showed that the usage of phase change materials with a thickness of 1 mm can reduce battery temperature and deliver a better temperature difference in a lithium-ion battery. Results also revealed that for a 14.6 Ah lithium-ion battery under a 5 C discharge rate, a phase change material cooling system with a thickness of 1 mm can reduce the maximum temperature from 51.64°C at 35°C and 55.85°C at 40°C ambient temperatures (without phase change material) to 43.04°C and 43.81°C, respectively. It was concluded that the phase change material system with a thickness of 1 mm can manage the maximum battery temperature and temperature difference in the desirable range at an extreme discharge rate of 5 C. The results obtained from this study can be used as guidance in the design of battery thermal management systems including phase change materials.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120480","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":"Effective Charging Scheduling of Electric Vehicles Using a Hybrid Deep Learning Network","authors":"J. P. Ananth,&nbsp;Pankaj Kumar,&nbsp;M. Belsam Jeba Ananth,&nbsp;R. Cristin","doi":"10.1002/est2.70120","DOIUrl":"https://doi.org/10.1002/est2.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>Electric vehicles (EVs) are developed by diverse industries as a substitute for vehicles with internal combustion engines, with many compensations that are environment-friendly. The amount of EVs is likely to rise fast in the approaching ages. However, uncoordinated vehicle charging may significantly stress the power grid. The main objective of the devised model is to minimize the charging time and waiting time for EVs by distributing equal power resources. Therefore, an energy-aware multi-objective system in a cloud-internet of things (IoT)-based electric vehicular network for a priority-based charge-scheduling scheme is proposed here and established as follows. Initially, the network with the EV location as well as the charge station (CS) location is simulated. Then, the charging planning is performed by determining the CS selection using the fractional spotted hyena jellyfish optimization (FSHJSO) considering a multi-objective function. Subsequently, the charge scheduling is performed using the established hybrid deep learning (DL) approach namely MobileNet neural network (MNN-Net) based on various objectives. The integration of MobileNet with deep neural network (DNN) forms the MNN-Net. By employing deep neuro-fuzzy network (DNFN), the power prediction is done. The efficiency of the developed MNN-Net is validated with some methods and achieved superior performance with an average waiting time of 11.796 s, distance 0.067 m, available power 53.657 W and number of EVs charged 63.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120053","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":"Microgrid Management of Hybrid Energy Sources Using a Hybrid Optimization Algorithm","authors":"V. Saravanakumar,&nbsp;V. J. Vijayalakshmi","doi":"10.1002/est2.70070","DOIUrl":"https://doi.org/10.1002/est2.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>The microgrid of the renewable energy sources are used as photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), micro turbines (MT), diesel generators (DG), and battery energy storage systems (ESS), offers a promising solution. The issues posed by microgrid operators (MGOs) in managing energy from multiple sources, device as a storage, and response demand programs are addressed in this research study, which proposes a finest dispatch of energy approach for connected grid and microgrid freestanding. In order to accomplish successful energy management, the suggested strategy takes into account not only the minimization of operational expenses but also the reduction of power losses and greenhouse gas emissions. For microgrid energy management (MGEM), a new multi-objective solution integrating a demand response program is incorporated into a mixed-integer linear programming model. The optimization issue illustrates the techno-commercial benefits and assesses the effect of demand response on optimal energy dispatch. Furthermore, a hybrid optimization technique combining the African Vultures Optimization technique (AVOA) and Artificial Rabbits Optimization (ARO) is projected to holder the problem of optimization, and an optimized fuzzy interface is built for scheduling the ESS. Finding the best trade-offs between costs, emissions, and power losses is made possible by the algorithm, which offers insightful information for the microgrid energy management system. The outcome of the renewable energy sources of the all categories are examined.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120052","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":"Numerical Investigation of the Effect of the Mushy Zone Parameter and the Thermal Properties of Paraffin-Based PCMs on Solidification Modeling Under T-History Conditions","authors":"Milad Tajik Jamal-Abad,&nbsp;Cristóbal Cortés,&nbsp;Arnold Martínez,&nbsp;Mauricio Carmona","doi":"10.1002/est2.70130","DOIUrl":"https://doi.org/10.1002/est2.70130","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Phase change materials (PCMs) are widely used in various critical applications because of their capacity to store thermal energy and regulate temperature effectively. A review of the literature on PCM solidification and melting simulations reveals that the accuracy of these simulations is highly dependent on the input parameters and underlying assumptions used in the software. Among the key factors influencing precise simulation results are the parameter of mushy zone (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mtext&gt;mushy&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {A}_{mushy} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and the thermal properties of the material. This study numerically investigated the impact of the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mtext&gt;mushy&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {A}_{mushy} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and thermal properties on the solidification behavior of a paraffin in the test tube under T-history conditions. The analysis was conducted using the commercial CFD software ANSYS Fluent and the enthalpy-porosity method is applied to simulation the solidification process. To accurately reflect the conditions of the T-history experiment, radiative heat transfer between surfaces was employed for the boundary conditions, ensuring a realistic representation of the experimental setup. An evaluation of four thermal properties—thermal conductivity, density, latent heat, and specific heat—indicates that while an increase in latent heat, density, and specific heat slows down the rate of solidification, an increase in thermal conductivity has the opposite effect, accelerating the solidification process. The results further emphasize that selecting an appropriate value for &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mtext&gt;mushy&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {A}_{mushy} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is crucial for achieving accurate solidification simulations. Increasing &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mtext&gt;mushy&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {A}_{mushy} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotatio","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120064","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 Real-Time Adaptive Machine Learning Charging and Neural Network Balancing Mechanism of Lithium-Ion Battery Pack","authors":"Gaurav Malik,&nbsp;Manish Kumar Saini","doi":"10.1002/est2.70131","DOIUrl":"https://doi.org/10.1002/est2.70131","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, a real-time novel adaptive deep neural network (A-DNN) charging scheme is proposed which increases the life of the batteries by controlling the heating impact inside the battery. The input variables used in the charging algorithm are state of charge (SoC), state of health (SoH), voltage (<i>V</i>), current (<i>I</i>), and temperature (<i>T</i>) which makes the algorithm adaptive toward the temperature deviation and reduces the peak overshoot of the temperature at different SoH of the batteries. The parameters of the battery 1-RC model are estimated by the forgetting factor recursive least square (FF-RLS) method. The SoC and SoH are estimated by the dual-particle filter (D-PF) algorithm. Furthermore, a DNN balancing mechanism sensitive to SoC and SoH is developed to avoid the fault in the battery during the charging process. The A-DNN charging algorithm is compared with the constant current constant voltage (CC-CV), constant current pulse charging (CC-PC), and deep neural network (DNN) charging algorithms at 40°C, 45°C, and 50°C. The A-DNN outperforms in terms of peak temperature, incremental life, and charging time of the batteries at 45°C. The proposed charging methodology reduces the economic cost of the EVs by increasing the life of the battery by 34.41% at 45°C as compared to the other algorithms.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119961","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}