Etransportation最新文献

{"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}

{"title":"Explosion characteristics of two-phase ejecta from large-capacity lithium iron phosphate batteries","authors":"Shilin Wang ,&nbsp;Chenyu Zhang ,&nbsp;Dapeng Chen ,&nbsp;Yiming Qin ,&nbsp;Lejun Xu ,&nbsp;Yitong Li ,&nbsp;Qinzheng Wang ,&nbsp;Xuning Feng ,&nbsp;Huaibin Wang","doi":"10.1016/j.etran.2024.100377","DOIUrl":"10.1016/j.etran.2024.100377","url":null,"abstract":"<div><div>When a thermal runaway accident occurs in a lithium-ion battery energy storage station, the battery emits a large amount of flammable electrolyte vapor and thermal runaway gas, which may cause serious combustion and explosion accidents when they are ignited in a confined space. With the gradual development of large-scale energy storage batteries, the composition and explosive characteristics of thermal runaway products in large-scale lithium iron phosphate batteries for energy storage remain unclear. In this paper, the content and components of the two-phase eruption substances of 340Ah lithium iron phosphate battery were determined through experiments, and the explosion parameters of the two-phase battery eruptions were studied by using the improved and optimized 20L spherical explosion parameter test system, which reveals the explosion law and hazards of the two-phase battery eruptions. Studies have shown that in a two-phase system explosion, EMC can make the two-phase system more explosive and more powerful, and the thermal runaway gas expands its explosion concentration range. The coupling explosion of the two enhanced the sensitivity and explosive power of the two-phase ejecta. Increasing the concentration of any combustible in a two-phase system will cause the explosion intensity parameters of the system to increase. However, when the combustible concentration exceeds the optimal explosion concentration, the explosion intensity parameters will decrease or even no explosion will occur. Both explosion intensity parameters and sensitivity parameters are more sensitive to EMC concentration, and the higher the EMC concentration, the stronger its dominant role in the explosion of the two-phase system. This work can lay the foundation for revealing the disaster-causing mechanism of explosion accidents in lithium-ion battery energy storage power stations, guide the safe design of energy storage systems and the prevention and control of explosion accidents, and provide theoretical and data support for the investigation of explosion accidents in energy storage power stations.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100377"},"PeriodicalIF":15.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572498","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":"Experimental analysis and optimal control of temperature with adaptive control objective for fuel cells","authors":"Pei Peng,&nbsp;Zhendong Sun,&nbsp;Yujie Wang,&nbsp;Zonghai Chen","doi":"10.1016/j.etran.2024.100373","DOIUrl":"10.1016/j.etran.2024.100373","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) vehicles are regarded as the most promising green transportation option, but their widespread adoption is hindered by cost and longevity, and temperature of PEMFCs stack is a crucial factor affecting both efficiency and longevity. Current researches on temperature control mainly focus on the iterative updates of control methods and performance optimization, while there is relatively little research on the detailed analysis of control objectives. Therefore this paper developed an active optimal control strategy for stack temperature with adaptive control objective to enhance the output performance of PEMFCs in hybrid systems. To this end, firstly, a quantitative mapping relationship between operating temperature and current was established through experimental calibration, identifying the optimal temperature path for maximizing output voltage at different current levels. Secondly, a control-oriented voltage model was developed to describe the phenomenon observed experimentally, where the output voltage initially increased and then decreased with the monotonically increasing stack temperature, provided that other parameters remain constant. Finally, an active optimal control strategy is proposed, which actively adjusts the temperature control objective in real-time according to the prevailing operating current and the predetermined optimal temperature path. The comparative validations under both static and dynamic conditions, utilizing three different control methods, demonstrated that the proposed active optimal control strategy clearly outperforms normal control strategy. The maximum performance enhancements achieved were 1.15%, 1.21%, and 1.30%, respectively.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100373"},"PeriodicalIF":15.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572499","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":"Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives","authors":"Maher G.M. Abdolrasol ,&nbsp;Afida Ayob ,&nbsp;M.S. Hossain Lipu ,&nbsp;Shaheer Ansari ,&nbsp;Tiong Sieh Kiong ,&nbsp;Mohamad Hanif Md Saad ,&nbsp;Taha Selim Ustun ,&nbsp;Akhtar Kalam","doi":"10.1016/j.etran.2024.100374","DOIUrl":"10.1016/j.etran.2024.100374","url":null,"abstract":"<div><div>Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100374"},"PeriodicalIF":15.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553117","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":"Trustworthy V2G scheduling and energy trading: A blockchain-based framework","authors":"Yunwang Chen ,&nbsp;Xiang Lei ,&nbsp;Songyan Niu ,&nbsp;Linni Jian","doi":"10.1016/j.etran.2024.100376","DOIUrl":"10.1016/j.etran.2024.100376","url":null,"abstract":"<div><div>The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G's potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework's potential to support auxiliary grid services.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100376"},"PeriodicalIF":15.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592921","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":"Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles","authors":"Wenbo Lu ,&nbsp;Zheng Yuan ,&nbsp;Ting Wang ,&nbsp;Peikun Li ,&nbsp;Yong Zhang","doi":"10.1016/j.etran.2024.100372","DOIUrl":"10.1016/j.etran.2024.100372","url":null,"abstract":"<div><div>To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100372"},"PeriodicalIF":15.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553120","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":"Industrialization challenges for sulfide-based all solid state battery","authors":"Yujing Wu ,&nbsp;Ziqi Zhang ,&nbsp;Qinggang Zhang ,&nbsp;Zhaoshuai Zhang ,&nbsp;Jiawei Li ,&nbsp;Ming Liu ,&nbsp;Hong Li ,&nbsp;Liquan Chen ,&nbsp;Fan Wu","doi":"10.1016/j.etran.2024.100371","DOIUrl":"10.1016/j.etran.2024.100371","url":null,"abstract":"<div><div>All-solid-state battery(ASSB) is the most promising solution for next-generation energy-storage device due to its high energy density, fast charging capability, enhanced safety, wide operating temperature range and long cycle life. Although great efforts and breakthroughs have been made in recent years, many challenges still exist for its industrialization. This perspective aims to summarize the most critical challenges in mass production of ASSB to fully release its potential and facilitate the arrival of a more sustainable future.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100371"},"PeriodicalIF":15.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538143","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":"The role of EV fast charging in the urban context: An agent-based model approach","authors":"F. Hipolito ,&nbsp;J. Rich ,&nbsp;Peter Bach Andersen","doi":"10.1016/j.etran.2024.100369","DOIUrl":"10.1016/j.etran.2024.100369","url":null,"abstract":"<div><div>Using an agent-based simulation approach, this paper investigates the role of fast-charging infrastructure in urban environments. The simulation model tracks the spatial and temporal behaviours of electric vehicles (EVs), facilitating a comprehensive analysis of the deployment of charging infrastructure. Notably, the model incorporates non-parametric queuing dynamics, information-sharing regarding waiting times, and diverse agent characteristics, deepening insights into the subject matter. Drawing on a large-scale implementation in the municipalities of Frederiksberg and Copenhagen, the study advocates for adopting fast chargers by demonstrating several key points. Firstly, information-sharing significantly reduces waiting times, particularly within the fast-charging network, with potential reductions of up to 30% during peak demand periods. Secondly, larger fast-charging clusters comprising 10–14 outlets outperform smaller clusters, primarily due to reduced waiting times and enhanced prediction accuracy of waiting times, which is a consequence of the information-sharing. Thirdly, placement strategies based on unserved demand metrics yield superior outcomes than those solely driven by observed demand patterns. By effectively monitoring both observed and unmet demand, these strategies tend to better optimize charging infrastructure placement. These insights, which emerge from the sophisticated and heterogeneous nature of the simulation framework, highlight the value of information and unserved demand in this field.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100369"},"PeriodicalIF":15.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527424","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":"Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub","authors":"Yinyu Chen ,&nbsp;Minwu Chen ,&nbsp;Wenjie Lu ,&nbsp;Agustí Egea-Àlvarez ,&nbsp;Lie Xu","doi":"10.1016/j.etran.2024.100366","DOIUrl":"10.1016/j.etran.2024.100366","url":null,"abstract":"<div><div>As the scale of suburban rail and electric vehicles (EVs) continues to expand with the revolution of electrification of transportation, park and ride (P&amp;R) facilities are increasingly recognized as critical energy coupling points between suburban rail traction transformers and EV charging stations. However, flexible coordination of the energy distribution among the bidirectional power flow of multiple trains and EVs’ charging demand becomes an urgent issue. In this paper, we establish a rail-EV Smart Energy Hub (SEH) framework integrating trains, ultra-capacitors (UC), and battery-based EVs. An emendable two-stage optimization model is proposed, enabling railways to provide R2X (railway-to-anything) services. The first stage determines the optimal train trajectory and adjusts timetables to minimize the energy consumption of multiple trains. In the second stage, the charging strategy of the EV is coordinated with the charging/discharging scheme of the UC, which takes the train power flow determined in the first stage as input. Meanwhile, the voltage unbalance caused by the railway is constrained to comply with the limits set by IEC/TR 61000-3-13. Case studies based on actual suburban railway lines in China demonstrate that the proposed scheduling optimization approach can significantly reduce the energy consumption of both railways and EVs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100366"},"PeriodicalIF":15.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538142","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":"On safety of swelled commercial lithium-ion batteries: A study on aging, swelling, and abuse tests","authors":"Yiding Li ,&nbsp;Shicong Ding ,&nbsp;Li Wang ,&nbsp;Wenwei Wang ,&nbsp;Cheng Lin ,&nbsp;Xiangming He","doi":"10.1016/j.etran.2024.100368","DOIUrl":"10.1016/j.etran.2024.100368","url":null,"abstract":"<div><div>Lithium-ion battery technology has advanced significantly, making these power sources essential for portable electronic devices such as smartphones. In 2023, global smartphone shipments reached nearly 1.2 billion units, underscoring the widespread reliance on these batteries. However, as batteries age, they may swell and potentially pose explosion risks. To investigate the safety of swollen batteries, this study conducts accelerated aging and swelling tests on lithium-ion batteries from five leading brands, which together represent over half of the global smartphone market share. The research involves a series of comprehensive tests, including Accelerated Rate Calorimeters (ARC) test, mechanical, electrical, and thermal abuse tests in accordance with Chinese national standards, as well as gas composition and theoretical flammability analyses on both new and swollen batteries. The findings indicate that swollen batteries generally exhibit safer behavior under floating charging conditions, and both new and swollen batteries pass the abuse tests within the standard framework. This study suggests that the safety of swollen lithium-ion batteries cannot be categorically labeled as dangerous or safe and should be assessed within the context of specific environments.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100368"},"PeriodicalIF":15.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527423","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}