Energy and AI最新文献

{"title":"Genetic modification optimization technique: A neural network multi-objective energy management approach","authors":"Mutaz AlShafeey ,&nbsp;Omar Rashdan","doi":"10.1016/j.egyai.2024.100417","DOIUrl":"10.1016/j.egyai.2024.100417","url":null,"abstract":"<div><p>In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100417"},"PeriodicalIF":9.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000831/pdfft?md5=ffc108d24674c837b79a3f21e4d7d837&pid=1-s2.0-S2666546824000831-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076782","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":"Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability","authors":"Xiao-Ying Ma ,&nbsp;Wen-Ke Zhang ,&nbsp;Ying Yin ,&nbsp;Kailong Liu ,&nbsp;Xiao-Guang Yang","doi":"10.1016/j.egyai.2024.100416","DOIUrl":"10.1016/j.egyai.2024.100416","url":null,"abstract":"<div><p>Electrified transportation requires batteries with high energy density and high-rate capability for both charging and discharging. Li-ion batteries (LiBs) face a dilemma: increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer path. Tailoring cell design parameters to balance these metrics is essential but challenging. Here, we present a multi-objective optimization framework targeting energy density, fast charging, high-rate discharging, and lifespan simultaneously. Four cell parameters—cathode areal capacity, N-P ratio, cathode porosity, and anode porosity—along with operating temperature, are selected as design variables. A physics-based pseudo-2D model, validated against experimental data, generates data to train the surrogate model, which is combined with the NSGA-II algorithm for rapid optimization. Three different objective calculation methods are compared to identify the maximum sum of energy densities, lowest polarization, and most balanced performance, respectively. Cell design parameters are optimized at different temperatures using the most balanced optimization method. Results demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density, mitigating the energy-power trade-off and broadening battery design parameter ranges.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100416"},"PeriodicalIF":9.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400082X/pdfft?md5=d3ce0c5b9c8dc1128ba8b4e5cbafe72c&pid=1-s2.0-S266654682400082X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087862","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":"Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model","authors":"Lizhen Wu ,&nbsp;Zhefei Pan ,&nbsp;Shu Yuan ,&nbsp;Xiaoyu Huo ,&nbsp;Qiang Zheng ,&nbsp;Xiaohui Yan ,&nbsp;Liang An","doi":"10.1016/j.egyai.2024.100411","DOIUrl":"10.1016/j.egyai.2024.100411","url":null,"abstract":"<div><p>Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100411"},"PeriodicalIF":9.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000776/pdfft?md5=9b46103bb566dce9bcfe4afe271d8e12&pid=1-s2.0-S2666546824000776-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050351","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":"BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes","authors":"Thanadol Tuntiwongwat ,&nbsp;Sippawit Thammawiset ,&nbsp;Thongchai Rohitatisha Srinophakun ,&nbsp;Chawalit Ngamcharussrivichai ,&nbsp;Somboon Sukpancharoen","doi":"10.1016/j.egyai.2024.100414","DOIUrl":"10.1016/j.egyai.2024.100414","url":null,"abstract":"<div><p>This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe₂O₃-based ฺBCLpro combining steam gasification for H₂ production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H₂ yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe₂O₃/Al₂O₃ mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H₂ yield in BCLpro systems. Access to the tool can be obtained through the following link: <span><span>http://bclh2pro.pythonanywhere.com/</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100414"},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000806/pdfft?md5=1bf861d91694bc24b779a2308fd3e75c&pid=1-s2.0-S2666546824000806-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096811","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":"Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles","authors":"Adithya Legala ,&nbsp;Matthew Kubesh ,&nbsp;Venkata Rajesh Chundru ,&nbsp;Graham Conway ,&nbsp;Xianguo Li","doi":"10.1016/j.egyai.2024.100415","DOIUrl":"10.1016/j.egyai.2024.100415","url":null,"abstract":"<div><p>Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100415"},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000818/pdfft?md5=082215c925da11b34457d3b7bdf0c6fb&pid=1-s2.0-S2666546824000818-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006357","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":"Learning the optimal power flow: Environment design matters","authors":"Thomas Wolgast,&nbsp;Astrid Nieße","doi":"10.1016/j.egyai.2024.100410","DOIUrl":"10.1016/j.egyai.2024.100410","url":null,"abstract":"<div><p>To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100410"},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000764/pdfft?md5=9a476707ca477944ae06662f8d552385&pid=1-s2.0-S2666546824000764-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992926","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":"Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance","authors":"Akihiro Yamashita ,&nbsp;Sascha Berg ,&nbsp;Egbert Figgemeier","doi":"10.1016/j.egyai.2024.100409","DOIUrl":"10.1016/j.egyai.2024.100409","url":null,"abstract":"<div><p>Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100409"},"PeriodicalIF":9.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000752/pdfft?md5=4fe4525928ca81e3686b18c3d211341f&pid=1-s2.0-S2666546824000752-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006454","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":"Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries","authors":"Slimane Arbaoui ,&nbsp;Ahmed Samet ,&nbsp;Ali Ayadi ,&nbsp;Tedjani Mesbahi ,&nbsp;Romuald Boné","doi":"10.1016/j.egyai.2024.100413","DOIUrl":"10.1016/j.egyai.2024.100413","url":null,"abstract":"<div><p>This study addresses the crucial challenge of monitoring the State of Health (SOH) of Lithium-Ion Batteries (LIBs) in response to the escalating demand for renewable energy systems and the imperative to reduce CO2 emissions. The research introduces deep learning (DL) models, namely Encoder-Long Short-Term Memory (E-LSTM) and Convolutional Neural Network-LSTM (CNN–LSTM), each designed to forecast battery SOH. E-LSTM integrates an encoder for dimensionality reduction and an LSTM model to capture data dependencies. CNN–LSTM, on the other hand, employs CNN layers for encoding followed by LSTM layers for precise SOH estimation. Significantly, we prioritize model explainability by employing a game-theoretic approach known as SHapley Additive exPlanations (SHAP) to elucidate the output of our models. Furthermore, a method based on pattern mining was developed, synergizing with the model, to identify patterns contributing to abnormal SOH decrease. These insights are presented through informative plots. The proposed approach relies on the battery dataset from the Massachusetts Institute of Technology (MIT) and showcases promising results in accurately estimating SOH values, in which the E-LSTM model outperformed the CNN–LSTM model with a Mean Absolute Error (MAE) of less than 1%.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100413"},"PeriodicalIF":9.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400079X/pdfft?md5=2f6d3403ffc70047c7693e2edcf06cd9&pid=1-s2.0-S266654682400079X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992925","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":"Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰","authors":"Zhichao Gong ,&nbsp;Bowen Wang ,&nbsp;Mohamed Benbouzid ,&nbsp;Bin Li ,&nbsp;Yifan Xu ,&nbsp;Kai Yang ,&nbsp;Zhiming Bao ,&nbsp;Yassine Amirat ,&nbsp;Fei Gao ,&nbsp;Kui Jiao","doi":"10.1016/j.egyai.2024.100412","DOIUrl":"10.1016/j.egyai.2024.100412","url":null,"abstract":"<div><p>Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100412"},"PeriodicalIF":9.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000788/pdfft?md5=eea03f9690b3b69a433d3bdeadbcbad8&pid=1-s2.0-S2666546824000788-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142011770","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":"Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring","authors":"Philipp Pelger ,&nbsp;Johannes Steinleitner ,&nbsp;Alexander Sauer","doi":"10.1016/j.egyai.2024.100407","DOIUrl":"10.1016/j.egyai.2024.100407","url":null,"abstract":"<div><p>This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100407"},"PeriodicalIF":9.6,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000739/pdfft?md5=1ae6290c0db1d3d6779ce8eb7568918e&pid=1-s2.0-S2666546824000739-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964207","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}