Etransportation最新文献

{"title":"Multi-physics simulation and risk analysis of internal thermal runaway propagation in lithium-ion batteries","authors":"Yan Ding ,&nbsp;Li Lu ,&nbsp;Huangwei Zhang","doi":"10.1016/j.etran.2025.100427","DOIUrl":"10.1016/j.etran.2025.100427","url":null,"abstract":"<div><div>This study investigates internal thermal runaway propagation (TRP) mechanism in lithium-ion batteries (LIBs) triggered by hotspots, focusing on the TRP dynamics and thermal interactions between internal short circuits (ISC) and side reactions within the TRP front. An integrated electrical-electrochemical-thermal-chemical model, incorporating a novel ISC model, is developed within the in-house <strong><em>BatteryFOAM</em></strong> solver to simulate global thermal runaway initiation and TRP behaviors. A new TRP front multi-zone model is built to analyze the coupling between heat conduction, ISC-driven ignition, and side reactions. The results show that the TRP occurs when the separator melt failure temperature (<span><math><mrow><msub><mi>T</mi><mrow><mi>s</mi><mi>e</mi><mi>p</mi></mrow></msub></mrow></math></span>) is reached before the maximum temperature gradient, allowing ISC Joule heating to maintain a high temperature gradient propagating from the hotspot to the normal zone. Therefore, a first-ever dimensionless risk coefficient (<span><math><mrow><mi>f</mi></mrow></math></span>) is introduced to quantify the balance between heat generation and dissipation, identifying high-risk TRP fronts where <span><math><mrow><mi>f</mi></mrow></math></span> ranges from 1 to 1e5, with cathode reactions and electrolyte decomposition dominating TRP acceleration. Model validation against the experiments confirms the predictive accuracy. Simulations demonstrate a TRP velocity of 7.5 mm/s, a width of 2.8 mm, and a maximum temperature of 690 K. Notably, the TRP velocity is, for the first time, revealed to be correlated with the square root of the thermal diffusivity, and an equation linking velocity with <span><math><mrow><msub><mi>T</mi><mrow><mi>s</mi><mi>e</mi><mi>p</mi></mrow></msub></mrow></math></span> is derived to guide LIB safety implementations. This study provides quantitative insights for designing safer LIBs, particularly in electric vehicles and large-scale energy storage.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100427"},"PeriodicalIF":15.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873955","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":"Performance investigation of the cascade heat pump system with waste heat recovery for electric vehicle thermal management systems on energy, economic and environmental impact","authors":"Hongseok Choi ,&nbsp;Jaehyun Song ,&nbsp;Sangwook Lee ,&nbsp;Yongjoo Jun ,&nbsp;Hoseong Lee","doi":"10.1016/j.etran.2025.100422","DOIUrl":"10.1016/j.etran.2025.100422","url":null,"abstract":"<div><div>This study addresses the inefficiencies of traditional EV thermal management systems that use a single compressor for both battery and cabin thermal needs. This configuration often results in inefficient energy utilization due to the differing thermal demands of the battery and cabin. To address these challenges, a cascade heat pump system was proposed, featuring two compressors and two independent refrigerant cycles to manage battery and cabin thermal loads separately. Additionally, the system reutilized waste heat for cabin heating under winter conditions. A simulation model, validated with experimental data, was developed to evaluate energy consumption under various scenarios, including diverse charging conditions and driving cycles. The results demonstrated that the cascade system significantly reduced energy consumption compared to conventional single-compressor systems. During battery charging, adaptive compressor control based on temperature achieved an average energy reduction of 50.2 % in summer and 25.9 % in winter. During electric vehicle operation, the cascade system consistently reduced total energy consumption across all driving cycles, improving driving range efficiency from 4.33 to 6.15 km/kWh under summer NEDC conditions. Over a 10-year period, the reduced energy consumption translated to a 16.6 % economic benefit and a 23.6 % reduction in CO₂ emissions. These findings highlight the cascade heat pump system's ability to optimize energy usage in both summer and winter, offering enhanced economic and environmental benefits for electric vehicles.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100422"},"PeriodicalIF":15.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859201","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":"Representative battery load profile synthesis leveraging multi-objective optimization heuristics","authors":"Konrad Katzschke ,&nbsp;Tamás Kurczveil ,&nbsp;Andreas Rausch","doi":"10.1016/j.etran.2025.100419","DOIUrl":"10.1016/j.etran.2025.100419","url":null,"abstract":"<div><div>Automotive high-voltage batteries show distinct reactions depending on their concurrent states of demanded power, temperature and <span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span>. To aid development, representative load profiles are frequently derived. Besides velocity-based cycles, literature also proposes the generation of electrical power trajectories. However, current methods fail to represent simultaneous thermo-electrical usage dynamics. Moreover, fitness functions based on highly aggregated parameters do not account for complex battery dynamics. This work presents a methodology to synthesize coupled <span><math><mi>P</mi></math></span>, <span><math><mi>T</mi></math></span>, and <span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span> trajectories. First, MCMC simulation derives an optimal <span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span> discharge stroke chain. Next, multiple stroke realizations are obtained by concatenating sequentially constrained micro-trips. A genetic algorithm then discovers feasible solutions to the related combinatorial optimization problem. Representativity is measured using the earth mover’s distance between signal distributions. Final profiles are selected from a Pareto front, allowing for the prioritization of Markov- or signal-related fitness. We conclude that applying the scale reduction factor with a threshold of <span><math><mrow><mover><mrow><mi>R</mi></mrow><mrow><mo>ˆ</mo></mrow></mover><mo>≤</mo><mn>1</mn><mo>.</mo><mn>01</mn></mrow></math></span> yields suitable length estimations of <span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span> stroke chains. The general introduction of an optimization step enables mean fitness improvement of up to <span><math><mrow><mn>40</mn><mspace></mspace><mstyle><mtext>%</mtext></mstyle></mrow></math></span> compared to sole MCMC sampling. 1D and 2D error function designs yield similar average fitness, while the latter demonstrates to deliver a broader solution variety. Our framework serves as a versatile base for individual battery applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100419"},"PeriodicalIF":15.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855705","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":"Deep learning-based inverse prediction of side pole collision conditions of electric vehicle","authors":"Chenghao Ma,&nbsp;Ziao Zhuang,&nbsp;Bobin Xing,&nbsp;Yong Xia,&nbsp;Qing Zhou","doi":"10.1016/j.etran.2025.100421","DOIUrl":"10.1016/j.etran.2025.100421","url":null,"abstract":"<div><div>To improve safety of electric vehicles under side pole collisions, accident reconstruction and failure risk prediction on battery cell are essential. Accident reconstruction and analysis is complex due to structural nonlinearities and vehicle rotation during collision. Such task becomes more challenging due to the ill-posedness of this inverse problem. This study proposed a deep-learning based method to inversely predict the collision conditions when only the deformation of battery pack exterior structure is available. Battery cell deformation was also predicted to assess the accident severity. To build the dataset, a large number of finite element simulations were run at pack level. Compared to the comprehensive coverage of collision condition domain, the collision response domain inevitably exhibits poor filling, leading to non-uniqueness in inverse prediction. To address this, the model was trained with pre- and post-collision images of the side structure. A convolutional neural network integrated with residual network (ResNet) was applied to improve model performance. The amount of input feature information and the network structure were thoroughly discussed. The model also demonstrated good interpretability and robustness, maintaining stable performance with added noise. This proposed approach would become an effective tool for analyzing collision scenarios where limited information is available.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100421"},"PeriodicalIF":15.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838496","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":"State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives","authors":"Hanqing Yu ,&nbsp;Hongcai Zhang ,&nbsp;Zhengjie Zhang ,&nbsp;Shichun Yang","doi":"10.1016/j.etran.2025.100420","DOIUrl":"10.1016/j.etran.2025.100420","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) have become indispensable in modern energy storage applications. However, accurate and reliable state estimation, such as state of charge (SOC), state of health (SOH), and other critical variables, remain significant challenges, especially as LIBs are being pushed to their performance limits in advanced applications. Traditional methods can be broadly categorized into physics-based (PB) and machine learning (ML) methods. Each approach has its strengths but also inherent limitations. In recent years, integrating PB and ML methods has emerged as a promising solution to address these challenges, combining the physical interpretability of PB models with the adaptability and efficiency of ML techniques. This integration has demonstrated remarkable improvements, reducing estimation errors by approximately half compared to traditional methods. This review systematically categorizes these combined methods into three main strategies—serial, parallel, and hybrid—and further analyzes their applications in LIB state estimation, focusing on key variables such as voltage, SOC, SOH, state of temperature (SOT), and other states. Additionally, this review discusses key challenges in real applications and presents future outlooks. By synthesizing the current state of knowledge, this work provides valuable guidance specifically tailored for electric vehicle engineers and energy storage researchers facing real-world deployment challenges, offering potential benefits in terms of cost reduction and efficiency improvement in battery management systems. Ultimately, the accuracy, efficiency, and reliability of LIB state estimation can be advanced through hybrid methods, bridging the gap between academic research and industrial applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100420"},"PeriodicalIF":15.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852055","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":"Degradation and expansion of lithium-ion batteries with silicon/graphite anodes: Impact of pretension, temperature, C-rate and state-of-charge window","authors":"Zhiwen Wan ,&nbsp;Sravan Pannala ,&nbsp;Charles Solbrig ,&nbsp;Taylor R. Garrick ,&nbsp;Anna G. Stefanopoulou ,&nbsp;Jason B. Siegel","doi":"10.1016/j.etran.2025.100416","DOIUrl":"10.1016/j.etran.2025.100416","url":null,"abstract":"<div><div>Lithium-ion batteries with silicon/graphite (Si/Gr) anodes achieve higher energy densities but face challenges such as rapid capacity fade, resistance growth, and complex expansion behavior under various cycling conditions. This study systematically addresses these challenges through a comprehensive test matrix to investigate the effects of pressure, temperature, state-of-charge (SoC) windows, and charge rates (C-rates) on the evolution of expansion, resistance, and capacity behavior over the lifetime of the battery. Increasing the applied pressure between 34 and 172 kPa reduced both reversible and irreversible expansion per cycle, as well as resistance growth over time, without significantly impacting capacity fade. Electrochemical Impedance Spectroscopy (EIS) confirmed that increased pressure lowered initial solution resistance and mitigated the further growth of the solution and solid electrolyte interphase (SEI) resistance. Elevated temperature (45°C) extended battery cycle life despite an initial increase in resistance. The lifetime impedance increase under 45°C was dominated by SEI resistance. Consistent with prior studies, operating in a narrow SoC window at high SoC minimized capacity loss. Additionally, charge rates up to 2C had a limited effect on the overall degradation trends. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) identified lithium inventory loss (LLI) as the primary cause of pre-knee degradation, whereas post-knee degradation resulted from a combination of LLI and anode-active material loss, particularly silicon. The deeper understanding of degradation mechanisms in batteries with Si/Gr anodes provided by this work enables the optimal packaging design and selection of operating conditions for the battery management system to extend battery cycle life.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100416"},"PeriodicalIF":15.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768522","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 survey of machine learning applications in advanced transportation systems: Trends, techniques, and future directions","authors":"Yuzhong Zhang,&nbsp;Songyang Zhang,&nbsp;Venkata Dinavahi","doi":"10.1016/j.etran.2025.100417","DOIUrl":"10.1016/j.etran.2025.100417","url":null,"abstract":"<div><div>In recent years, artificial intelligence (AI) has revolutionized numerous sectors, including advanced transportation systems (ATS). This paper presents a comprehensive review of the latest machine learning (ML) applications within ATS, encompassing air, marine, and land transport modes. The review systematically categorizes and evaluates ML applications in four key subdomains: more-electric aircraft (MEA), all-electric ships (AES), high-speed rail (HSR), and electric vehicles (EV). A total of 124 articles were analyzed, spanning January 2014 to December 2023, to identify the global focus and results of ML in ATS. Our findings reveal that ML methods significantly improve predictive maintenance, energy management, fault diagnosis, and system optimization in ATS. However, the adoption and integration of ML face challenges related to data quality, model complexity, and real-time implementation. This review serves as a multidisciplinary research roadmap, considering ATS as a whole and taking a broad perspective of ML applications in ATS; highlighting open challenges and future directions, including dealing with data limitations, computational demands, applying transformers for time series forecasting, applying other emerging ML methods in ATS, and combining different ML approaches. The insights provided aim to facilitate further adoption of ML by both academia and industry, ultimately contributing to the evolution of intelligent and efficient transportation systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100417"},"PeriodicalIF":15.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654837","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":"Techno-economic analysis of energy storage systems integrated with ultra-fast charging stations: A dutch case study","authors":"A. Ahmad,&nbsp;J. Meyboom,&nbsp;P. Bauer,&nbsp;Z. Qin","doi":"10.1016/j.etran.2025.100411","DOIUrl":"10.1016/j.etran.2025.100411","url":null,"abstract":"<div><div>A fast and efficient charging infrastructure has become indispensable in the evolving energy landscape and thriving electric vehicle (EV) market. Irrespective of the charging stations’ internal alternating current (AC) or direct current (DC) bus configurations, the main concern is the exponential growth in charging demands, resulting in network congestion issues. In the context of exponential EV growth and the provision of charging facilities from low-voltage distribution networks, the distribution network may require frequent upgrades to meet the rising charging demands. To avoid network congestion problems and minimize operational expenses (OE) by integrating energy storage systems (ESS) into ultra-fast charging stations (UFCS). This paper presents a techno-economic analysis of a UFCS equipped with a battery ESS (BESS). To reduce reliance on the electric grid and minimize OE, a dual-objective optimization problem is formulated and solved via grid search and dual-simplex algorithms. Analytical energy and physical BESS models are employed to evaluate the optimization matrices. The intricacies of BESS aging are examined to ensure an optimal BESS size with a more extensive lifespan than the corresponding payback period. The integrated BESS significantly reduced reliance on the grid to tackle network congestion while fulfilling charging demands. The dynamic pricing (DP) structure has proven more favorable, as the average per unit cost remains lower than the static tariff (ST). Results illustrate that integrating BESS reduces the OE and peak-to-average ratio (PAR) by 5-to-49% and 16-to-73%, respectively. Moreover, the combination of 70% BESS and 30% grid capacities outperforms the other configurations with a 73% reduction in PAR and a 49% reduction in OE before BESS reaches the end-of-life.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100411"},"PeriodicalIF":15.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620238","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":"Optimising fast-charging infrastructure for long-haul electric trucks in remote regions under adverse climate conditions","authors":"Albert Alonso-Villar ,&nbsp;Brynhildur Davíðsdóttir ,&nbsp;Hlynur Stefánsson ,&nbsp;Eyjólfur Ingi Ásgeirsson ,&nbsp;Ragnar Kristjánsson","doi":"10.1016/j.etran.2025.100414","DOIUrl":"10.1016/j.etran.2025.100414","url":null,"abstract":"<div><div>This study proposes a novel methodology for planning fast-charging infrastructure for long-haul battery-electric trucks (BETs) in low-traffic flow regions. The research addresses the challenge of early-stage charging infrastructure development and optimally locating fast-charging stations (FCS) in remote areas, with a focus on minimising routing time and ensuring reliability.</div><div>The proposed approach integrates a vehicle energy consumption, a non-linear charging optimisation framework, and a queueing model to design an efficient fast-charging station network in Iceland's Reykjavík-Westfjords freight routes under harsh climate and freight conditions.</div><div>Findings indicate that larger batteries and higher charging rates significantly reduce additional routing times. Trucks with 540 kWh battery capacity using 500 kW chargers require minimal extra time, averaging 25 min, while trucks with 360 kWh batteries and 350 kW charging rates experience longer delays, averaging 83 min. These results highlight the impact of battery capacity and charging power on route electrification feasibility and suggest potential alignment with freight schedules.</div><div>This study provides valuable insights for policymakers and fleet operators to guide fast-charging infrastructure development and prioritise investments, contributing to the broader goal of freight transport electrification. Future research should investigate the potential impact of the derived charging loads on the power grid.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100414"},"PeriodicalIF":15.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642481","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-scenario data-driven approach for anomaly detection in electric vehicle battery systems","authors":"Zirun Jia ,&nbsp;Zhenpo Wang ,&nbsp;Zhenyu Sun ,&nbsp;Xin Sun ,&nbsp;Peng Liu ,&nbsp;Franco Ruzzenenti","doi":"10.1016/j.etran.2025.100418","DOIUrl":"10.1016/j.etran.2025.100418","url":null,"abstract":"<div><div>The increasing adoption of electric vehicle (EV) emphasizes the need for safer battery systems. However, detecting anomalies during charging and discharging processes remains challenging due to the high variability and complexity of EV operational data. This study proposes a multi-scenario data-driven framework to address these challenges. The Pearson Correlation Coefficient is employed for feature selection in charging scenarios, while a Time Series Shape Feature Extraction Algorithm is developed for discharging scenarios to reduce data dimensionality while preserving critical information. An enhanced Transformer model integrated with a Generative Adversarial Network reconstructs voltage data, capturing complex temporal dependencies. Additionally, an improved Cumulative Sum algorithm with a sliding window mechanism enhances sensitivity to localized anomalies. Validation with real-world EV data demonstrates <em>F</em>_<em>1</em> score of 90.38 % in charging and 86.55 % in discharging, outperforming existing methods. Moreover, the framework can detect anomalies at least two charging and discharging cycles (67 h) before thermal runaway occur. Additionally, a techno-economic analysis reveals that the framework could prevent up to $692.99 million in economic losses for China's EV fleet by reducing fire-related incidents. The presented framework enhance safety, reduce risks, and offer substantial economic benefits, demonstrating its potential for large-scale application in the EV industry.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"24 ","pages":"Article 100418"},"PeriodicalIF":15.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654838","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}