Applied Energy最新文献

{"title":"Real-time estimation of battery SoC through neural networks trained with model-based datasets: Experimental implementation and performance comparison","authors":"Giovanni Chianese ,&nbsp;Luigi Iannucci ,&nbsp;Ottorino Veneri ,&nbsp;Clemente Capasso","doi":"10.1016/j.apenergy.2025.125783","DOIUrl":"10.1016/j.apenergy.2025.125783","url":null,"abstract":"<div><div>Data-driven methods have been widely investigated to estimate battery SoC due to their great potential in solving regression problems. However, expensive experimental campaigns are generally required to collect large training datasets. To address this need, this paper demonstrates the advantages of using a validated battery simulation model to easily generate data for training neural networks (NNs) estimating SoC. Such a procedure drastically reduces the number of experiments, which are only required to calibrate/validate a physics-based battery model and to test the NNs in real driving operative conditions. A Li-NMC storage cell for automotive applications was considered as case study to verify the presented methodology. The analysis was performed in a wide range of operative conditions in terms of temperatures and load dynamics. Offline tests, based on data collected during experiments, showed that the trained NNs were able to predict the SoC with an accuracy comparable to NNs trained with standard experimental-based procedures. In the end, the trained NNs were implemented on a microcontroller to prove their real-time applicability in BMS boards.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125783"},"PeriodicalIF":10.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Development of Fibrous Materials for Electrocatalytic Water Splitting","authors":"Weijian Xiao ,&nbsp;Yuanyuan Li ,&nbsp;Yan Zhang ,&nbsp;Yang Gao ,&nbsp;Ning Qi ,&nbsp;Ping Wang","doi":"10.1016/j.apenergy.2025.125809","DOIUrl":"10.1016/j.apenergy.2025.125809","url":null,"abstract":"<div><div>Fiber materials have demonstrated significant competitive advantages in the field of electrocatalytic water splitting. Their unique characteristics—including high specific surface area, robust conductive networks, customizable properties, and the potential for hybridization with metal materials—make them prime candidates for catalytic applications. This review begins by exploring the fundamental principles of electrocatalytic water splitting, then categorizes the various types of fibers currently employed in this domain, and finally highlights the diverse roles that fiber materials play. In summary, the article outlines future research trajectories, potential applications, and anticipated challenges of fiber materials in electrocatalytic water splitting. This comprehensive review aims to foster a deeper understanding of this critical field and ultimately advance clean energy technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125809"},"PeriodicalIF":10.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electromechanical modeling and experimental validation of an origami-structured triboelectric vibration energy harvester","authors":"Zicheng Liu ,&nbsp;Guobiao Hu ,&nbsp;Yawei Wang ,&nbsp;Heesoo Yoon ,&nbsp;Chaoyang Zhao ,&nbsp;Xin Li ,&nbsp;Yaowen Yang","doi":"10.1016/j.apenergy.2025.125761","DOIUrl":"10.1016/j.apenergy.2025.125761","url":null,"abstract":"<div><div>This study presents a novel electromechanical model and its experimental validation for an origami-structured triboelectric energy harvester (OTEH) designed to scavenge kinetic energy from vibration. OTEHs are recognized for their enhanced electrical output due to increased contact areas from stacked structures, but their electromechanical modeling remains largely unexplored due to geometric complexity. Furthermore, few studies have investigated the use of OTEHs for harvesting vibration energy. To address these gaps, we developed an electromechanical model specifically tailored for vibration energy harvesting using OTEHs. The model integrates an electrical model for triangular non-parallel contact surfaces with a lumped-parameter mechanical model, incorporating origami geometric parameters for broad applicability. We experimentally validated the model using a bellow-origami-structured triboelectric energy harvester (BOTEH) designed for vibration energy harvesting. Benefiting from axial elasticity and a guiding rod, the BOTEH structure can be excited by harmonic base vibration, enabling a systematic investigation of its vibration-energy-harvesting potential. The model, validated through time-domain and frequency-domain voltage and displacement responses under various vibratory conditions, accurately predicts the BOTEH's dynamic behavior. Additionally, under base forcing vibration, the BOTEH demonstrated practical utility by triggering an Internet-of-Things (IoT) temperature sensor and illuminating 24 LEDs, achieving a maximum output power of 119 μW at 6.6 Hz and 0.8 g with an 80 MΩ external resistor.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125761"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional variable-order observer-based method for state-of-charge estimation of lithium-ion batteries","authors":"Xiaobo Wu ,&nbsp;Liping Chen ,&nbsp;António M. Lopes ,&nbsp;Hongli Ma ,&nbsp;Chaolong Zhang ,&nbsp;Penghua Li ,&nbsp;Wenliang Guo ,&nbsp;Lisheng Yin","doi":"10.1016/j.apenergy.2025.125775","DOIUrl":"10.1016/j.apenergy.2025.125775","url":null,"abstract":"<div><div>In most fractional-order equivalent circuit models (ECM) of lithium-ion batteries (LIB), the order of the constant phase element is fixed, which usually translates into inaccuracies when describing the strongly nonlinear behavior of the voltage–current (U–I) characteristics of the batteries. In this paper, the problem is addressed by a novel fractional variable-order ECM (FVO-ECM) of LIB, where the order of the capacitor is a function of the state-of-charge (SOC). An improved chaotic adaptive fractional-order particle swarm optimization (CAFPSO) algorithm is designed to identify the FVO-ECM parameters, and its accuracy is verified with different models, parameter identification methods and under sub-zero cold environments. Then, a fractional variable-order observer (FVOO) is proposed for SOC estimation, and the dynamics of the error system are proven to be stable in the sense of Lyapunov. Finally, the proposed SOC estimation scheme is assessed using LIB experimental data, revealing its robustness under different test cycle conditions and temperatures. The experimental results show that the new method can work normally under various test cycles and different temperatures, exhibiting higher accuracy than existing alternative methods. The SOC estimation error is limited to a narrow band of <span><math><mo>±</mo><mn>0.02</mn></math></span>, and the root mean square error (RMSE) can be kept within 1 %. Moreover, the proposed approach can overcome the divergence caused by incorrect initial SOC values and random noise interference, revealing good robustness.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125775"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy in turmoil: Industry resilience to uncertainty during the global energy crisis","authors":"Jan Jakub Szczygielski ,&nbsp;Ailie Charteris ,&nbsp;Lidia Obojska ,&nbsp;Janusz Brzeszczyński","doi":"10.1016/j.apenergy.2025.125351","DOIUrl":"10.1016/j.apenergy.2025.125351","url":null,"abstract":"<div><div>We investigate the resilience of global industry groups to energy price uncertainty (ENPU) during the global energy crisis. Diversified financials reflect the greatest return response, being vulnerable to investment delays and discretionary spending, whereas industry groups producing necessities are most resilient. Volatility triggering is highest for automobiles &amp; components due to ambiguous risk-return prospects requiring greater investor learning whereas the food &amp; staples retailing group is most resilient. Differences in ranked return and volatility responses point towards distinct transmission channels. We expound a measure, the ‘Overall Impact of Uncertainty’ (OIU), that considers both effects jointly and reflects a dominant effect. According to the OIU, the most and least impacted groups are automobiles &amp; components and food &amp; staples retailing, respectively. Energy prices have a relatively weak impact relative to ENPU, suggesting that ENPU reflects a broader transmission channel, encompassing other forms of uncertainty.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125351"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of wind energy harvesting technology: Civil engineering resource, theory, optimization, and application","authors":"Bo Su ,&nbsp;Tong Guo ,&nbsp;Md. Mahbub Alam","doi":"10.1016/j.apenergy.2025.125771","DOIUrl":"10.1016/j.apenergy.2025.125771","url":null,"abstract":"<div><div>The structural performance of civil infrastructure deteriorates over time, highlighting the need for data from wireless sensor networks installed on tall buildings, tunnels, and long-span bridges for effective maintenance. However, ensuring a sustainable power supply for these structural health monitoring systems remains a significant challenge. A promising solution is the development of self-powered wireless sensor networks using micro harvesters that harness ambient wind energy, particularly through flow-induced vibrations. This area has recently garnered considerable attention, particularly due to advancements in miniaturized wind turbines. This paper comprehensively reviews the latest advancements and research trends in wind energy harvesting technologies relevant to civil infrastructure. First, we investigate wind resources from high-rise buildings, bridges, and tunnels associated with high-speed trains, summarizing their characteristics and optimization strategies for efficient energy harvesting, in addition to an analysis of wind field and power generation. Next, we outline the fundamental energy conversion mechanisms for wind harvesting, which are crucial for designing and developing new energy harvesters. The characteristics of the three main energy conversion mechanisms, including electromagnetic generator (EMG), triboelectric nanogenerator (TENG), and piezoelectric conversion are investigated, along with their hybrid methods. Further, we also delve into various wind-induced vibrations, including vortex-induced vibrations, flutter, galloping, and wake-induced vibrations, along with their respective coupling mechanisms. Additionally, we provide a comparative analysis of some efficient harvesters, including their operational principles and quantitative analysis of their start-up velocity, outpour voltage, and power. In particular, several representative chips are reviewed, with a qualitative analysis provided as a reference for the circuit design of self-powered wireless sensors. Finally, we address methods for energy conversion and storage methods with low power consumption for structural health monitoring systems, supported by relevant case studies, including hybrid prototypes of small wind turbines to power monitoring sensors. These include a hybrid prototype of small wind turbines for powering monitoring sensors in railway tunnels and an innovative wind harvester, the “Inverted Flag” designed to power a temperature sensor. This work promotes a comprehensive understanding of energy harvesting technologies in civil engineering and supports the development of practical, self-powered monitoring systems for infrastructure.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125771"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-module robust method for transient stability assessment against false label injection cyberattacks","authors":"Hanxuan Wang ,&nbsp;Na Lu ,&nbsp;Yinhong Liu ,&nbsp;Zhuqing Wang ,&nbsp;Zixuan Wang","doi":"10.1016/j.apenergy.2025.125768","DOIUrl":"10.1016/j.apenergy.2025.125768","url":null,"abstract":"<div><div>The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a <u>M</u>ulti-<u>M</u>odule <u>R</u>obust (MMR) TSA method is proposed to rectify the supervised training process misguided by FLI attacks in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternately trained to improve the clustering friendliness of representation leaning, achieving accurate clustering assignments. By leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and correct the misguided supervised classification, thereby improving the performance of deep TSA models. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMR-HIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are selected and re-labeled by a TSA expert-guided annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI attacks. Moreover, the contaminated labels can be effectively corrected, demonstrating superior resilience of the proposed methods.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125768"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Total cost of ownership for battery electric vehicles: The role of energy prices","authors":"Johannes Bauer,&nbsp;Peter Letmathe,&nbsp;Richard Woeste","doi":"10.1016/j.apenergy.2025.125764","DOIUrl":"10.1016/j.apenergy.2025.125764","url":null,"abstract":"<div><div>The wide usage of battery electric vehicles (BEVs) is one of the most promising pathways to reduce greenhouse gas emissions in the mobility sector. The German target of having 15 million BEVs registered by 2030 is an ambitious challenge, as the market share of these vehicles is only growing slowly. This can be attributed to a variety of cost factors that are related to their purchase and operation. Therefore, this study analyzes the total cost of ownership (TCO) for privately owned vehicles. To assess future energy price developments, a break-even model is developed and applied to the TCO results. Further sensitivity analyses consider the effects of subsidies and charging profiles. The results reveal that the optimal vehicle drive technology depends strongly on the vehicle segment considered. The implementation of reasonable measures to make BEVs economically advantageous for consumers would enable Germany's transition to electric mobility.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125764"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interconnected tides: Analyzing European energy markets dynamics in the post-COVID era","authors":"Ummara Razi ,&nbsp;Muhammad Ramzan","doi":"10.1016/j.apenergy.2025.125803","DOIUrl":"10.1016/j.apenergy.2025.125803","url":null,"abstract":"<div><div>The COVID-19 pandemic has had a profound impact on European energy markets, driving volatility, price surges, and heightened market integration, exacerbating investment challenges. Thus, this study evaluates the post-COVID-19 interconnected dynamics of the energy markets in Europe, focusing on energy return connectedness across 12 key European countries through static and dynamic Quantile Vector Autoregression (QVAR) and VAR models. The analysis revealed the substantial directional connectedness with Spain, Poland and Finland as the major transmitters of energy market shocks. Whereas, Sweden, France and Norway emerged as net receivers. They highlight the heterogeneous yet interdependent European energy markets, shaped by local energy policies, geopolitical uncertainties and market volatility. The study, through a detailed examination of mean and variance connectedness, reveals energy infrastructure vulnerabilities, in particular in highly interconnected markets, amplified shock transmission across crises. Based on the findings, the study suggested actionable policy recommendations, including strengthened cross-border energy infrastructure, greater renewable integration, targeted development of strategic energy storage, and EU-wide crisis management. By contributing to the energy market integration literature, this research offers important implications to the policymakers concerned with the improvement of energy security and stability in Europe's linked energy landscape.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125803"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales","authors":"Yulong Yu ,&nbsp;Qiang Zheng ,&nbsp;Tianyi Zhang ,&nbsp;Zhengyan Li ,&nbsp;Lei Chen ,&nbsp;Wen-Quan Tao","doi":"10.1016/j.apenergy.2025.125763","DOIUrl":"10.1016/j.apenergy.2025.125763","url":null,"abstract":"<div><div>Proton exchange membrane fuel cell (PEMFC) represents a significant technology for hydrogen energy conversion and are widely utilized in renewable energy systems. However, their performance tends to degrade over time during operation. Accurate prediction of PEMFCs performance is critical for optimizing hydrogen energy efficiency and ensuring the reliability of renewable energy systems. Meanwhile, the monitoring data collected from PEMFCs exhibit characteristics of diverse types, varying time resolutions, and distinct operating conditions, which complicate accurate predictions. To address this challenge, the feature-fusion and feature-attention blocks are developed to amalgamate interactive information and emphasize key features across various monitoring datasets. Based on the blocks, the feature-fusion and feature-attention deep learning (FFA-DL) framework that incorporates convolutional long short-term memory (ConvLSTM) networks is proposed. To validate the proposed framework, real-world data from two operation conditions, FC1 and FC2, are employed. The results demonstrate that the FFA-DL framework effectively extracts valuable information from complex monitoring data, thereby enhancing the accuracy of PEMFCs performance prediction. FFA-DL significantly enhanced prediction performance of the embedding models for both FC1 and FC2, and the FFA-enhanced ConvLSTM (FFA-ConvLSTM) outperformed other models with R² of 0.9631 and 0.9946 for FC1 and FC2, respectively. Additionally, the FFA-ConvLSTM exhibited excellent robustness and accuracy for data under varying time resolutions, with R² exceeding 0.9200 and 0.9800 for FC1 and FC2, respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"389 ","pages":"Article 125763"},"PeriodicalIF":10.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}