Etransportation最新文献

{"title":"Design of high-energy-density lithium batteries: Liquid to all solid state","authors":"Haozhe Du ,&nbsp;Xu Zhang ,&nbsp;Haijun Yu","doi":"10.1016/j.etran.2024.100382","DOIUrl":"10.1016/j.etran.2024.100382","url":null,"abstract":"<div><div>With the rising demand of lithium batteries from application fields including electric vehicles (EVs) and various electric aircrafts, it is imperative to greatly enhance the energy density of lithium batteries by rational design. However, there is still a lack of design roadmap for high-energy-density lithium batteries, largely owing to the uncertain selections of electrochemically active materials and the complicated relationships of diverse factors. In this article, based on the discussion of effects of key components and prototype design of lithium batteries with different energy density classes, we aim to tentatively present an overall and systematic design principle and roadmap, covering the key factors and reflecting crucial relationships. This article starts from the fundamental principles of battery design, and the effects of cathode, anode, electrolyte, and other components to realize high-energy-density lithium batteries have been discussed. Based on the prototype design of high-energy-density lithium batteries, it is shown that energy densities of different classes up to 1000 Wh/kg can be realized, where lithium-rich layered oxides (LLOs) and solid-state electrolytes play central roles to gain high energy densities above 500 Wh/kg. Lithium batteries are thus categorized according to different energy density classes, with available component options, to meet their most suitable application scenes.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"23 ","pages":"Article 100382"},"PeriodicalIF":15.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163256","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":"Electric machine co-optimization for EV drive technology development: Integrating Bayesian optimization and nonlinear model predictive control","authors":"Christoph Wellmann ,&nbsp;Abdul Rahman Khaleel ,&nbsp;Tobias Brinkmann ,&nbsp;Alexander Wahl ,&nbsp;Christian Monissen ,&nbsp;Markus Eisenbarth ,&nbsp;Jakob Andert","doi":"10.1016/j.etran.2024.100392","DOIUrl":"10.1016/j.etran.2024.100392","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Electric powertrains are becoming increasingly prevalent in various mobile propulsion applications, not only due to legislations for lower CO&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; emissions and local pollution, but also due to growing sustainable consciousness. However, conceptualizing those systems, consisting of component and controller design processes, is a complex task. The complexity itself arises from the amount of requirements for design objectives and use-cases, which can be met inside a multidimensional parameter space. Additionally, system design and evaluation are inherently tied to coupled component and system control strategy optimization. In this context, the paper presents a fully automated active machine learning methodology applied for a combined optimization of electric machine and system controller design, considering system performance, durability, and energy consumption. During this iterative approach a stochastic optimization of a permanent magnet synchronous machine (PMSM) takes place, constrained from a nonlinear model predictive control in a model-in-the-loop system environment. The active learning is covered by a Bayesian optimization algorithm with a Gaussian process regression to determine the most suitable parameter set in terms of exploration and exploitation. To demonstrate the feasibility of this novel methodology, a thermal subsystem from an electrified state-of-the-art powertrain has been used and further optimized regarding PMSM scaling and final gear ratio. Different real-world drive scenarios from highway to city were taken into account to cover typical sport utility vehicle use-cases. It could be shown that the electric machine losses of the optimized system are reduced by up to &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;32&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mstyle&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, which equals a consumption of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;43&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mstyle&gt;&lt;mfrac&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;100&lt;/mn&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; compared to the reference vehicle. Due to slightly worse operating conditions of the inverter the whole system consumption has been minimized by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;35&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mstyle&gt;&lt;mfrac&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;100&lt;/mn&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. Three parameter studies with fixed iteration count have been executed to find the optimal machine diameter to be increased by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;25&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mstyle&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and the length slightly reduced by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;16&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mstyle&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. Moreover, the total gear ratio was adjusted by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;31&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;msty","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"23 ","pages":"Article 100392"},"PeriodicalIF":15.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163250","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":"Advancing lithium battery safety: Introducing a composite phase change material with anti-leakage and fire-resistant properties","authors":"Xinxi Li ,&nbsp;Wensheng Yang ,&nbsp;Likun Yin ,&nbsp;Shuangyi Zhang ,&nbsp;Yuhang Wu ,&nbsp;Ya Mao ,&nbsp;Wei Jia ,&nbsp;Di Wu ,&nbsp;Kai Chen ,&nbsp;Lifan Yuan ,&nbsp;Xiaoyu Zhou ,&nbsp;Canbing Li","doi":"10.1016/j.etran.2024.100387","DOIUrl":"10.1016/j.etran.2024.100387","url":null,"abstract":"<div><div>The thermal safety of batteries has still existed challenge in energy-storage power stations and electric vehicles. Composite phase change material (CPCM) as a passive cooling system has great potential in the application of controlling an uneven temperature distribution, but its high flammability and susceptibility to leakage severely restrict its widespread adoption, especially in battery packs for electric vehicles and energy storage. Herein, an innovative paraffin/expanded graphite/[Ca(polyethylene glycol)₂]Cl₂ coordination polymer/triphenyl phosphate (TPP)/hexaphenoxycyclotriphosphazene (HPCP) flame retardant multifunctional CPCM (PPCTH) has been introduced and utilized in battery module for thermal management and preventing thermal runaway. PPCTH2 has contained TPP/HPCP with the proportion of 1:1 which provides a multifunctional CPCM with excellent antileakage properties, high thermal conductivity, superior flame-retardant ability, the PPCTH2 exhibits excellent shape stability without collapsing at 200 °C. Moreover, the total heat release and smoke production of PPCTH2 are 108.8 MJ/m² and 8.4 m², respectively. Additionally, the prismatic battery module endowed with PPCTH2 can maintain the maximum temperature below 50 °C and balance the temperature difference within 4.2 °C at a 2 C discharge rate. Thus, the battery module with PPCTH2 can not only improve the temperature consistency even during long cycling processes but also lengthen the temperature rising time and decrease heat accumulation, further suppressing thermal runaway. Overall, this research presents a multifunctional CPCM with high fire resistance and shape stability, which may contribute to the research and design of improved thermal safety for battery packs and energy-storage units.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"23 ","pages":"Article 100387"},"PeriodicalIF":15.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163251","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":"Improving fuel cell vehicle efficiency: Exploring dynamic cooling strategies for stack radiators with intermittent spray cooling","authors":"Rajendran Prabakaran,&nbsp;M. Mohamed Souby,&nbsp;Jie Liu,&nbsp;Sung Chul Kim","doi":"10.1016/j.etran.2024.100384","DOIUrl":"10.1016/j.etran.2024.100384","url":null,"abstract":"<div><div>Advancements in stack cooling via air-cooled radiators for fuel cell (FC) electric vehicles have attracted significant attention. In this study, continuous spray cooling (CTSC) and intermittent spray cooling (IMSC) approaches for FC vehicles were developed at a lab-scale level. Additionally, the thermo-evaporation performance of various IMSC strategies, involving different spray intervals (0–120 s), continuous spray periods (10–60 s), and duty cycles (25–100 %), was investigated. Steady-state analysis revealed that, compared to conventional stack radiators, the CTSC approach using Nozzle#2 achieved superior thermal efficiency (η_th) with an improvement of 36.6–83.8 %, and enhanced spray evaporation efficiency (η_ev) by 18.2–23.9 %. In contrast, Nozzle#1 yielded only a 16.2–52.5 % increase in η_th and an 11.4–18.6 % improvement in η_ev. Compared to CTSC, IMSC extended the low-temperature operating range of the radiator even during the spray-off periods, leading to improved spray evaporation performance. However, excessive coolant exit temperature and heat rejection rate fluctuations were observed at higher spray periods with longer intervals (IMSC-60-60I and IMSC-40-40I) and lower duty cycles (&lt;50 %). On the other hand, the IMSC strategy with shorter intervals and spray periods, i.e., IMSC-30-20I, was identified as optimal, offering a 55.7 % improvement in η_ev compared to CTSC, despite a 2.8 % reduction in η_th. Overall, the optimal IMSC configuration exhibited a 69.4 % higher heat rejection capacity compared to conventional air-cooled stack radiators. Furthermore, variations in η_th were validated using existing correlations, and new empirical correlations for both η_th and air-side heat transfer coefficient were developed, with prediction accuracies of approximately 86 % and 85 %, respectively. Additionally, the radiator's heat transfer area could be reduced by up to 76.2 %, despite a 7.5 % increase in vehicle curb weight. In summary, this study highlights the potential of using IMSC strategies for stack radiators in FC vehicles. The findings provide valuable insights for designing and implementing IMSC-enhanced radiators in real-world applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"23 ","pages":"Article 100384"},"PeriodicalIF":15.0,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723607","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":"Resource-efficient artificial intelligence for battery capacity estimation using convolutional FlashAttention fusion networks","authors":"Zhilong Lv ,&nbsp;Jingyuan Zhao","doi":"10.1016/j.etran.2024.100383","DOIUrl":"10.1016/j.etran.2024.100383","url":null,"abstract":"<div><div>Accurate battery capacity estimation is crucial for optimizing lifespan and monitoring health conditions. Deep learning has made notable strides in addressing long-standing issues in the artificial intelligence community. However, large AI models often face challenges such as high computational resource consumption, extended training times, and elevated deployment costs. To address these issues, we developed an efficient end-to-end hybrid fusion neural network model. This model combines FlashAttention-2 with local feature extraction through convolutional neural networks (CNNs), significantly reducing memory usage and computational demands while maintaining precise and efficient health estimation. For practical implementation, the model uses only basic parameters, such as voltage and charge, and employs partial charging data (from 80 % SOC to the upper limit voltage) as features, without requiring complex feature engineering. We evaluated the model using three datasets: 77 lithium iron phosphate (LFP) cells, 16 nickel cobalt aluminum (NCA) cells, and 50 nickel cobalt manganese (NCM) oxide cells. For LFP battery health estimation, the model achieved a root mean square error of 0.109 %, a coefficient of determination of 0.99, and a mean absolute percentage error of 0.096 %. Moreover, the proposed convolutional and flash-attention fusion networks deliver an average inference time of 57 milliseconds for health diagnosis across the full battery life cycle (approximately 1898 cycles per cell). The resource-efficient AI (REAI) model operates at an average of 1.36 billion floating point operations per second (FLOPs), with GPU power consumption of 17W and memory usage of 403 MB. This significantly outperforms the Transformer model with vanilla attention. Furthermore, the multi-fusion model proved to be a powerful tool for evaluating capacity in NCA and NCM cells using transfer learning. The results emphasize its ability to reduce computational complexity, energy consumption, and memory usage, while maintaining high accuracy and robust generalization capabilities.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"23 ","pages":"Article 100383"},"PeriodicalIF":15.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699833","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":"Recent advances and perspectives in enhancing thermal state of lithium-ion batteries with phase change materials: Internal and external heat transfer enhancement factors","authors":"Sagar Vashisht ,&nbsp;Rajat ,&nbsp;Dibakar Rakshit","doi":"10.1016/j.etran.2024.100381","DOIUrl":"10.1016/j.etran.2024.100381","url":null,"abstract":"<div><div>Electric vehicles (EVs) play a crucial role in reducing fuel consumption and emissions, underscoring the importance of lithium-ion batteries (Li-ion) in powering these vehicles. However, Li-ion batteries are susceptible to degradation, capacity loss, and catastrophic failure due to temperature fluctuations, necessitating efficient thermal management. This review explores advancements and challenges in PCM-based battery thermal management systems (BTMS), focusing on internal and external factors influencing performance. It discusses internal factors such as material-level improvements in PCM-based BTMS, including solutions like SiC and EG-based PCM, flexible composite PCM, and serpentine-shaped PCM. External factors, such as fluid flow dynamics, cell spacing, and shape, significantly influence BTMS performance. Critical considerations include evaluating air- and liquid-based approaches and integrating heat pipes with PCM for passive BTMS. Furthermore, understanding the influence of these factors on temperature uniformity and heat dissipation is essential. The paper concludes by outlining future trends in PCM-based battery thermal management, emphasizing the utilization of flexible PCM and copper foam-enhanced PCM alongside hybrid BTMS configurations to optimize performance. By comprehensively addressing internal and external factors, BTMS can enhance Li-ion battery efficiency and lifespan in EVs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100381"},"PeriodicalIF":15.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707019","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":"Comprehensive energy footprint of electrified fleets: School bus fleet case study","authors":"Joon Moon ,&nbsp;Athar Hanif ,&nbsp;Qadeer Ahmed","doi":"10.1016/j.etran.2024.100379","DOIUrl":"10.1016/j.etran.2024.100379","url":null,"abstract":"<div><div>This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel, depending on the specific price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100379"},"PeriodicalIF":15.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664329","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":"Simulation of single-layer internal short circuit in anode-free batteries","authors":"Nitesh Gupta ,&nbsp;Shanhai Ge ,&nbsp;Tatsuro Sasaki ,&nbsp;Kaiqiang Qin ,&nbsp;Ryan S. Longchamps ,&nbsp;Koichiro Aotani ,&nbsp;Chao-Yang Wang","doi":"10.1016/j.etran.2024.100380","DOIUrl":"10.1016/j.etran.2024.100380","url":null,"abstract":"<div><div>The lithium metal battery technologies that can fulfil the high energy density goal have grave safety concerns and lead to fire/smoke, leading to battery failure. Out of all the causes of fire, internal short circuits (ISC) are the most common. The ISC safety test is considered a crucial checkpoint for battery design, but the present tests, like nail penetration and ball indentation, lack certainty and reproducibility in declaring battery safety. In light of these experimental limitations, we present an experimentally validated ISC simulation method that can elucidate fundamental mechanisms underlying ISC. The experimental/simulation method isolates the shorted single-layer from the unshorted layers, which helps in scrutinizing ISC and thermal runaway (TR) phenomenon. The present ISC model is flexible and computationally inexpensive compared to other 3D electrochemical thermal coupled (ECT) ISC simulations for a whole battery pack. We show the experimental validation of terminal voltage, short-circuit current, shorting resistance, internal temperature and other derived parameters of an ISC simulation of anode-free cell. Finally, the simulation model was used to do a parametric study for an anode-free battery (AFB) and the effect of cell design, and shorting parameters on ISC was scrutinized.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100380"},"PeriodicalIF":15.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664328","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":"An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas","authors":"Akram Elomiya ,&nbsp;Jiří Křupka ,&nbsp;Vladimir Simic ,&nbsp;Libor Švadlenka ,&nbsp;Petr Průša ,&nbsp;Stefan Jovčić","doi":"10.1016/j.etran.2024.100375","DOIUrl":"10.1016/j.etran.2024.100375","url":null,"abstract":"<div><div>The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100375"},"PeriodicalIF":15.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663744","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 driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage","authors":"Aihua Tang ,&nbsp;Yuchen Xu ,&nbsp;Pan Liu ,&nbsp;Jinpeng Tian ,&nbsp;Zikang Wu ,&nbsp;Yuanzhi Hu ,&nbsp;Quanqing Yu","doi":"10.1016/j.etran.2024.100378","DOIUrl":"10.1016/j.etran.2024.100378","url":null,"abstract":"<div><div>Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100378"},"PeriodicalIF":15.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578261","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}