Etransportation最新文献

{"title":"Mechanism of battery expansion failure due to excess solid electrolyte interphase growth in lithium-ion batteries","authors":"Dongdong Qiao ,&nbsp;Xuezhe Wei ,&nbsp;Jiangong Zhu ,&nbsp;Guangxu Zhang ,&nbsp;Shuai Yang ,&nbsp;Xueyuan Wang ,&nbsp;Bo Jiang ,&nbsp;Xin Lai ,&nbsp;Yuejiu Zheng ,&nbsp;Haifeng Dai","doi":"10.1016/j.etran.2025.100450","DOIUrl":"10.1016/j.etran.2025.100450","url":null,"abstract":"<div><div>Revealing the aging and failure mechanisms of lithium-ion batteries is crucial for extending battery life and improving battery safety. This paper presents a mechanism of solid electrolyte interphase (SEI) film overgrowth and battery failure caused by deep aging of cylindrical batteries. Firstly, multiple 18650-type cylindrical battery accelerated aging experiments were designed. Differential voltage analysis (dV/dQ) and electrochemical impedance spectroscopy (EIS) are used to investigate battery degradation mechanisms non-destructively. Secondly, batteries under different degradation degrees were disassembled, and the scanning electron microscope (SEM), liquid nitrogen cooled argon-ion cross-sectional polishing, and X-ray photoelectron spectroscopy (XPS) technology were used to investigate the surface and cross-sectional SEI evolution of electrodes. Obvious increases in SEI thickness and resistance occur when the battery capacity fade is less than 30 %. Finally, the mechanism of excessive growth of SEI on the graphite negative electrode surface of the cylindrical battery, leading to the expansion and rupture failure of the metal shell, was revealed. This work provides crucial insights for the safe service, management, and residual value assessment of lithium-ion batteries throughout their entire lifecycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100450"},"PeriodicalIF":15.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623667","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":"Enhancing safety of electric aircraft Batteries: Degradation and thermal runaway behavior at extreme altitudes","authors":"Wenjie Jiang ,&nbsp;Canbing Li ,&nbsp;Xinxi Li ,&nbsp;Yuhang Wu ,&nbsp;Yunjun Luo ,&nbsp;Dequan Zhou ,&nbsp;Zhaowei Lin ,&nbsp;Kang Xiong ,&nbsp;Jianzhe Liu","doi":"10.1016/j.etran.2025.100448","DOIUrl":"10.1016/j.etran.2025.100448","url":null,"abstract":"<div><div>The operating performance and thermal safety of lithium-ion batteries (LIBs) in high-altitude scenarios are prime concerns for their reliable applications in various fields. High-altitude environments, characterized by low ambient pressure and temperature, can accelerate LIB degradation and increase the risk of thermal runaway (TR). Unlike previous studies focusing solely on ambient pressure or ambient temperature, this work quantifies their high-altitude coupled effects on the battery performance as well as the TR characteristics. Herein, experiments and simulations are combined to analyze the hybrid pulse charge/discharge behavior, direct current internal resistance (DCIR), over-discharge and recharge/re-discharge performance, and TR characteristics of 26650 NiCoMn LIBs under various ambient pressure and temperature conditions. The results show that low ambient pressure at 20 kPa increases the DCIR of LIB by 7.16 mΩ, raises battery temperature by 4.3 °C, lowers energy efficiency to 92.2 %, and advances TR occurrence with mass loss increasing to 7.5 g. Low ambient temperature at 50 °C causes abrupt changes in battery voltage (up to 6.8009 V during pulse charge and down to 2.0641 V during pulse discharge) and increases the DCIR to 284.8 mΩ. When low ambient pressure and low ambient temperature are combined, energy efficiency decreases to 93.5 % and the peak TR temperature of LIB reduces to 214.2 °C at 20 kPa &amp; −50 °C. The research elucidates the relationship between performance/TR behaviors of LIB and individual/coupled environmental factors, shedding new insights into the operation and safety of LIB in the aviation sector. This facilitates to establishing tailored LIB designs and adaptive thermal management strategies to mitigate failure risks in high-altitude applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100448"},"PeriodicalIF":15.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604499","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":"Mechanical information enhanced battery state-of-health estimation","authors":"Xubo Gu ,&nbsp;Xinyuan Wang ,&nbsp;Yao Ren ,&nbsp;Wenqing Zhou ,&nbsp;Xun Huan ,&nbsp;Jason Siegel ,&nbsp;Weiran Jiang ,&nbsp;Ziyou Song","doi":"10.1016/j.etran.2025.100440","DOIUrl":"10.1016/j.etran.2025.100440","url":null,"abstract":"<div><div>Accurate estimation of the state of health (SOH) is crucial for the safe operation of batteries. Mechanical features, in particular, offer significant potential for improving SOH estimation by directly reflecting key internal processes within batteries. However, research on the contribution of mechanical features to SOH estimation remains limited. This study demonstrates the effectiveness of mechanical features for SOH estimation in pouch cells under various operating conditions and scenarios. The results show that mechanical features provide reliable SOH estimates across different temperatures, C-rates, and charging profiles, and they are especially robust under real-world driving conditions. The mechanical features typically achieve at least a 28.26% reduction in prediction error. Notably, in the driving scenario, the mean absolute percentage error reaches an impressive low of 0.65%. Furthermore, this work introduces an evaluation framework to systematically benchmark features derived from electrical, thermal, and mechanical signals based on their overall predictive capabilities. Finally, detailed physical interpretations are provided to explain the effectiveness of mechanical features.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100440"},"PeriodicalIF":15.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572421","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":"Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network","authors":"Shaopeng Li ,&nbsp;Hui Zhang ,&nbsp;Daniela Anna Misul ,&nbsp;Federico Miretti ,&nbsp;Matteo Acquarone ,&nbsp;Naikan Ding ,&nbsp;Dingan Ni ,&nbsp;Ninghao Hou ,&nbsp;Yanjie He ,&nbsp;Yijun Zhang ,&nbsp;Yifan Sun","doi":"10.1016/j.etran.2025.100445","DOIUrl":"10.1016/j.etran.2025.100445","url":null,"abstract":"<div><div>For preventing thermal runaway accidents in electric vehicles (EVs), it is crucial to conduct early warning for temperature anomaly in battery pack. Based on data collected by a naturalistic driving experiment with 20 EVs, this study proposes a temporal convolutional network (TCN) algorithm for battery temperature anomaly prediction. Firstly, 40 features encompassing battery signals, thermal management state, ambient temperature, and driving condition are extracted from micro-segments. Then, the most effective input features are selected between the 40 features through maximum information coefficient (MIC) correlation analysis, and the principal component analysis (PCA). After obtaining the optimal hyperparameters, the TCN model is trained using the data from four EVs. The model's performance in predicting temperature is assessed over the data of the remaining 16 vehicles. The results demonstrate that the model achieves accurate prediction with the maximum and minimum mean relative error (MRE) of 0.0132 and 0.0072 across the 16 test vehicles. Moreover, the model proves to be robust against different testing seasons, SOCs, and traffic conditions. Compared to convolutional neural network (CNN), long short-term memory network (LSTM), and CNN-LSTM models with same hyperparameters, the developed TCN model consistently obtains the lowest MRE on both training and testing. For two kinds of scenarios where the probe temperature changes slowly and rapidly, the TCN model can predict an impending temperature anomaly up to 40 min in advance, and forecast the temperature anomaly within the future 8 min, respectively. Among the 16 vehicles, 81.25 % demonstrate a high prognosis accuracy, with an average F1 score of 0.951 across 10 of the vehicles. Thus, the proposed method can provide accurate battery temperature anomaly early warning for EVs under actual driving conditions.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100445"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623777","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":"Dynamic bus charge scheduling by model predictive control to maximize local PV surplus power utilization","authors":"Fumiaki Osaki ,&nbsp;Yu Fujimoto ,&nbsp;Yutaka Iino ,&nbsp;Yuto Ihara ,&nbsp;Masataka Mitsuoka ,&nbsp;Yasuhiro Hayashi","doi":"10.1016/j.etran.2025.100441","DOIUrl":"10.1016/j.etran.2025.100441","url":null,"abstract":"<div><div>As the electrification of public transport and the adoption of variable renewable energy accelerate the transition to carbon neutrality, integrating local photovoltaic (PV) surplus power into electric bus charging operations becomes increasingly critical. However, uncertainties in PV generation and traffic delays often reduce the effective utilization of PV surplus due to missed charging opportunities. To address these challenges, this study proposes a dynamic charging scheduling method based on model predictive control (MPC), which adaptively updates the schedule using quasi-real-time, district-scale information. The framework integrates real-time traffic delays in the General Transit Feed Specification format (GTFS Realtime), smart meter measurements, and meteorological satellite observations—data sources currently available in real cities. At each update step, the system forecasts PV surplus power using a machine learning model that captures temporal weather conditions and localized PV surplus trends around charging stations, while detecting bus delays at each station. Based on this information, the optimal charging schedule is updated every 30 min to adaptively maximize PV surplus utilization. Numerical experiments simulating an entire year demonstrate the effectiveness of the proposed method. Compared to a fixed day-ahead schedule and a rule-based charging method, it improves the annual average PV surplus utilization rate by up to 11.9% and reduces annual average grid power purchases by up to 15.6%. These results highlight the potential of combining MPC with quasi-real-time, district-scale data to proactively and robustly integrate renewable energy into public electric bus operations under uncertainty.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100441"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588837","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":"Hybrid fusion for battery degradation diagnostics using minimal real-world data: Bridging laboratory and practical applications","authors":"Yisheng Liu ,&nbsp;Boru Zhou ,&nbsp;Tengwei Pang ,&nbsp;Guodong Fan ,&nbsp;Xi Zhang","doi":"10.1016/j.etran.2025.100446","DOIUrl":"10.1016/j.etran.2025.100446","url":null,"abstract":"<div><div>Unpredictability of battery lifetime has been a key stumbling block to technology advancement of safety-critical systems such as electric vehicles and stationary energy storage systems. In this work, we present a novel hybrid fusion strategy that combines physics-based and data-driven approaches to accurately predict battery capacity. This strategy, implemented via a convolutional neural network, achieves an average estimation error of only 0.63 % over the entire battery lifespan, utilizing merely 45 real-world data segments along with over 1.7 million simulated data segments derived from random partial charging cycles. By leveraging a thoroughly validated reduced-order electrochemical model, we extract typical aging patterns from laboratory aging data and extend them into a more comprehensive parameter space, encompassing diverse battery aging states in potential real-world applications while accounting for practical cell-to-cell variations. By bridging the gap between controlled laboratory experiments and real-world usage scenarios, this method highlights the significant potential of transferring underlying knowledge from high-fidelity physics-based models to data-driven models for predicting the behavior of complex dynamical systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100446"},"PeriodicalIF":15.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549436","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":"Dynamic control of stack temperature prevents abnormal flooding in 60 kW PEM fuel Cells: Modeling and 2000h road validation","authors":"Shuai Zhu,&nbsp;Po Hong,&nbsp;Pingwen Ming,&nbsp;Cunman Zhang,&nbsp;Bing Li,&nbsp;Weibo Zheng","doi":"10.1016/j.etran.2025.100447","DOIUrl":"10.1016/j.etran.2025.100447","url":null,"abstract":"<div><div>Water content inside the stack affects durability of the proton exchange membrane fuel cell in vehicle. Gas temperature and relative humidity at stack inlet are important factors affecting the water content. This paper proposes a model-based dynamic control of stack temperature to prevent abnormal flooding in a 60 kW PEM fuel cell stack with experiment validation. To be specific, a hydrothermal dynamic model of air supply subsystem including gas-gas humidifier is established by taking into consideration heat exchange between air supply subsystem and environment, heat capacity of humidifier and influence of liquid water at stack outlet on exchange of heat and water in humidifier. Simulation result shows that during load change, liquid water at stack outlet and thermal response of parts of air supply subsystem (particularly the humidifier) dominate large latency and multi-stage dynamic response of gas temperature and relative humidity at stack inlet. Experiment is performed on a 60 kW fuel cell system. During load increase, gas temperature at stack inlet rises in four stages, which is consistent with simulation result. During load decrease, average high frequency impedance, air temperature at stack inlet and average cell voltage of the stack are gradually decreased and reach stable state in about 2000s. Experiment result validates the dynamic model and discovers abnormal phenomenon of flooding for the stack at 87A. Accordingly, a control strategy for water management by adjusting stack temperature is further developed to adapt to variable environment condition. Finally, road test indicates that the water management strategy effectively reduces degradation rate of cell voltage to −2.18μV/h within 2000h from winter to autumn.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100447"},"PeriodicalIF":15.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623666","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":"In-situ estimation of nitrogen concentration in fuel cell systems via anode pressure drop modeling","authors":"Naiyuan Yao ,&nbsp;Tiancai Ma ,&nbsp;Weikang Lin ,&nbsp;Lei Shi ,&nbsp;Yanbo Yang ,&nbsp;Ruitao Li ,&nbsp;Ziheng Gu ,&nbsp;Jinxuan Qi ,&nbsp;Enyong Li ,&nbsp;Qiyuan Guo","doi":"10.1016/j.etran.2025.100444","DOIUrl":"10.1016/j.etran.2025.100444","url":null,"abstract":"<div><div>Optimizing hydrogen supply control is critical to enhancing the efficiency and lifespan of fuel cell systems. Nitrogen permeation across the membrane dilutes hydrogen concentration and increases the risk of hydrogen starvation. However, the absence of real-time, cost-effective methods to monitor or estimate nitrogen concentration hinders efforts to optimize hydrogen utilization and mitigate hydrogen starvation. To address these challenges, this study establishes an anode pressure drop model incorporating key operational parameters, including nitrogen concentration. Then, a series of experiments under various operating conditions are conducted on a 130 kW full-scale fuel cell system to validate the model, with the ultrasonic sensor employed to measure the flow rate and gas concentration within the hydrogen recirculation loop. Finally, a nitrogen concentration estimation algorithm based on the model is proposed and experimentally verified. Results demonstrate that the mean absolute error of the estimated nitrogen concentration is around 1 vol% under steady-state and dynamic conditions. This work employs a mechanistic model based on the relationship between gas composition and viscosity to elucidate the coupled variation of anode pressure drop and nitrogen concentration. Compared with existing solutions, the proposed nitrogen concentration estimation algorithm features high accuracy, low cost, and robustness against stack degradation, and can be implemented in controllers for in-situ nitrogen concentration estimation. These advancements enable predictive hydrogen supply regulation, which is anticipated to improve the system's durability and efficiency.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100444"},"PeriodicalIF":15.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513722","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":"LSTM-augmented DRL for generalisable energy management of hydrogen-hybrid ship propulsion systems","authors":"Ailong Fan ,&nbsp;Hanyou Liu ,&nbsp;Peng Wu ,&nbsp;Liu Yang ,&nbsp;Cong Guan ,&nbsp;Taotao Li ,&nbsp;Richard Bucknall ,&nbsp;Yuanchang Liu","doi":"10.1016/j.etran.2025.100442","DOIUrl":"10.1016/j.etran.2025.100442","url":null,"abstract":"<div><div>Enhancing the generalisation of energy management strategies is crucial for hybrid ship power systems to adapt to unknown navigation conditions effectively. A long short-term memory (LSTM)-based data augmentation method is employed to mitigate uncertainty in propulsion power, thereby enhancing the generalisation of energy management strategies based on deep reinforcement learning (DRL). Simulations using a hybrid propulsion model and operational data from “Three Gorges Hydrogen Boat No.1” compared DQN and DDPG algorithms with and without LSTM integration. By evaluating the DRL strategy's performance in reducing fuel cell operating pressure and energy consumption before and after data augmentation, the quality of generalisation performance is characterised. Results show that optimisation target weights affect training convergence and performance under unknown test conditions. Data enhancement via the LSTM model improves DRL generalisation in unknown navigation conditions. Compared to original DDPG, LSTM-DDPG reduces FC operating pressure by 5.82 % and 1.86 %, and cuts hydrogen consumption by 0.80 % and 2.13 % under two days of unknown conditions. This research offers guidance for designing energy management strategies with high generalisation, addressing adaptability issues with real-world data uncertainty.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100442"},"PeriodicalIF":15.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549437","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":"High-performance solid-state sodium-ion batteries for lightweight electric vehicles: A closed-loop feedback-optimized composite electrolyte design","authors":"Liu Pei ,&nbsp;Ying Han ,&nbsp;Jingjing Dong ,&nbsp;Yifei Wang ,&nbsp;Yan Liu ,&nbsp;Xingliang Liu ,&nbsp;Zhidan Diao ,&nbsp;Jian Liu ,&nbsp;Xindong Wang","doi":"10.1016/j.etran.2025.100439","DOIUrl":"10.1016/j.etran.2025.100439","url":null,"abstract":"<div><div>The composite solid electrolytes integrate the merits of polymers and inorganic materials, which provide a strong guarantee for the safety and stability of solid-state sodium-ion battery systems. In this work, based on the multicomponent synergistic effect in the composite electrolytes, the system composition of the electrolytes was regulated by the closed-loop feedback strategy, and the novel composite solid electrolytes modified by NASICON(Na Super Ionic Conductor) active fillers were constructed, and the sodium ion transport pathways in the system were explored in detail. Meanwhile, N'N-dimethylformamide (DMF) was chosen as the solvent to form [DMF-Na⁺] as the transport carriers, and the co-allocation competition state between -CN and TFSI- anions enhanced the dissociation process of sodium salts. The introduction of Na_3.4Zr_1.8Ni_0.2Si₂PO₁₂ improved the migration ability of sodium ions in the electrolytes, thereby significantly improving the performance of the composite solid electrolytes. The prepared PNS/NZSP-Ni0.2 (PVDF/NaTFSI/SN/Na_3.4Zr_1.8Ni_0.2Si₂PO₁₂) electrolytes exhibit high sodium ion conductivity of 1.02 × 10⁻³ S cm⁻¹ at room temperature. The further assembled Na|PNS/NZSP-Ni0.2|Na₃V₂(PO₄)₃ solid-state sodium-ion battery shows excellent cycling stability, with a capacity retention rate of 90 % after 700 cycles at a current density of 0.5C. This work advances the development of safe, high-performance batteries for transportation electrification and grid-scale energy storage, while the closed-loop feedback strategy has been applied for the first time to the optimization of sodium-ion solid electrolytes, overcoming the bottleneck of single-component optimization of electrolytes.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100439"},"PeriodicalIF":15.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534461","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}