首页 > 最新文献

Etransportation最新文献

英文 中文
Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage 利用短期弛豫电压进行深度学习驱动的电池电压-容量曲线预测
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-30 DOI: 10.1016/j.etran.2024.100378
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.
准确监控电池容量衰减对电池的稳定运行至关重要。然而,仅凭有限的循环信息来评估最大容量并不足以充分显示电池退化的程度。在此,本研究提出了一种使用松弛电压结合编码器-解码器的电池劣化监测方法,将传统的最大容量估算扩展到整个电压-容量(V-Q)曲线估算。编码器-解码器采用无监督预训练和迁移学习的两阶段训练策略。首先,将短时弛豫电压序列输入自动编码器进行无监督预训练。通过这一自动编码过程,编码器获得了在相同测试条件下对未标记的弛豫电压进行特征学习的能力。随后,通过冻结编码器权重和在解码器上执行迁移学习,将弛豫电压序列映射到相应的 V-Q 曲线,从而完成两阶段训练过程。在相同的历时下,与直接训练相比,所提出的方法实现了更先进的预测性能。这意味着使用 V-Q 曲线和推导出的增量容量曲线进行全面电池劣化监测的准确性更高。经过对来自不同实验室的 130 个电池样本的验证,所提出的方法能预测出高保真的 V-Q 曲线,均方根误差小于 0.03 Ah。这项研究强调了采用弛豫电压进行电池退化监测的能力,由于所需的弛豫时间较短,而且无需额外的循环信息,因此有望在非恒定电流充电情况下实现快速、全面的老化诊断。
{"title":"Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage","authors":"","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":null,"pages":null},"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}
引用次数: 0
Explosion characteristics of two-phase ejecta from large-capacity lithium iron phosphate batteries 大容量磷酸铁锂电池两相喷出物的爆炸特性
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-30 DOI: 10.1016/j.etran.2024.100377
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.
锂离子电池储能电站发生热失控事故时,电池会释放出大量可燃电解液蒸气和热失控气体,在密闭空间内点燃后可能引发严重的燃烧爆炸事故。随着大型储能电池的逐步发展,大型储能磷酸铁锂电池中热失控产物的成分和爆炸特性仍不明确。本文通过实验确定了340Ah磷酸铁锂电池两相爆发物质的含量和成分,并利用改进优化的20L球形爆炸参数测试系统研究了两相电池爆发的爆炸参数,揭示了两相电池爆发的爆炸规律和危害。研究表明,在两相体系爆炸中,EMC 能使两相体系爆炸性更强、威力更大,热失控气体扩大了其爆炸浓度范围。二者的耦合爆炸增强了两相喷出物的敏感性和爆炸威力。增加两相系统中任何可燃物的浓度都会导致系统的爆炸强度参数增加。然而,当可燃物浓度超过最佳爆炸浓度时,爆炸强度参数会降低,甚至不发生爆炸。爆炸强度参数和灵敏度参数对 EMC 浓度都比较敏感,EMC 浓度越高,对两相体系爆炸的主导作用越强。该工作可为揭示锂离子电池储能电站爆炸事故的致灾机理奠定基础,指导储能系统的安全设计和爆炸事故的防控,为储能电站爆炸事故的调查提供理论和数据支持。
{"title":"Explosion characteristics of two-phase ejecta from large-capacity lithium iron phosphate batteries","authors":"","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":null,"pages":null},"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}
引用次数: 0
Experimental analysis and optimal control of temperature with adaptive control objective for fuel cells 燃料电池温度自适应控制目标的实验分析和优化控制
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-29 DOI: 10.1016/j.etran.2024.100373
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.
质子交换膜燃料电池(PEMFCs)汽车被认为是最有前途的绿色交通工具,但其广泛应用受到成本和寿命的阻碍,而 PEMFCs 堆的温度是影响效率和寿命的关键因素。目前有关温度控制的研究主要集中在控制方法的迭代更新和性能优化上,而对控制目标进行详细分析的研究相对较少。因此,本文开发了一种具有自适应控制目标的堆栈温度主动优化控制策略,以提高混合动力系统中 PEMFC 的输出性能。为此,首先通过实验校准建立了工作温度与电流之间的定量映射关系,确定了在不同电流水平下输出电压最大化的最佳温度路径。其次,建立了一个以控制为导向的电压模型,以描述实验观察到的现象,即在其他参数保持不变的情况下,随着堆栈温度的单调升高,输出电压最初升高,然后降低。最后,还提出了一种主动优化控制策略,即根据当时的工作电流和预定的最佳温度路径,实时主动调整温度控制目标。利用三种不同的控制方法,在静态和动态条件下进行的对比验证表明,所提出的主动优化控制策略明显优于普通控制策略。所实现的最大性能提升分别为 1.15%、1.21% 和 1.30%。
{"title":"Experimental analysis and optimal control of temperature with adaptive control objective for fuel cells","authors":"","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":null,"pages":null},"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}
引用次数: 0
Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives 电动汽车锂离子电池管理系统中的高级数据驱动故障诊断:进展、挑战和未来展望
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-28 DOI: 10.1016/j.etran.2024.100374
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.
电动汽车(EV)在运行、老化或充电过程中的危险往往来自锂离子电池组(LIB)。强大的早期故障诊断算法对于提高安全性、效率和可靠性至关重要。锂离子电池故障类型涉及内部电池、传感器、执行器和系统故障,由电池管理系统(BMS)管理,该系统负责处理状态估计、电池平衡、热管理和故障诊断。及时识别和隔离故障电池,并采取早期预警措施,对安全性至关重要。本综述探讨了用于 LIB 管理系统故障诊断的数据驱动方法,内容包括实施、分类、故障类型和特征提取。它还讨论了 BMS 的作用、传感器类型、挑战和未来趋势。研究结果旨在指导研究人员和汽车行业推进故障诊断方法,以支持可持续的电动汽车交通。
{"title":"Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives","authors":"","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":null,"pages":null},"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}
引用次数: 0
Trustworthy V2G scheduling and energy trading: A blockchain-based framework 可信的 V2G 调度和能源交易:基于区块链的框架
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-28 DOI: 10.1016/j.etran.2024.100376
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.
电动汽车(EV)的快速增长和车联网(V2G)技术的部署给分布式电网带来了巨大挑战,尤其是在促进信任和确保利益相关者之间的有效协调方面。建立可信的 V2G 运行环境对于电动汽车用户的大规模参与和实现 V2G 在实际应用中的潜力至关重要。本文开发了一个综合调度和交易框架,以在 V2G 运营中进行透明、高效的协调。在区块链实施方面,本文提出了一种网络物理区块链架构,利用智能充电站(SCP),通过快速路径冗余容错(fast-path practical byzantine fault tolerance,Fast-path PBFT)共识机制进行快速交易验证,从而提高交易效率和可扩展性。从能源调度的角度来看,采用了博弈论定价策略,并利用智能合约在电动汽车和运营商之间进行自主决策,旨在优化交易过程,实现经济效益最大化。对区块链共识的数值评估表明,快速路径 PBFT 共识在提高系统可扩展性的同时,还兼顾了稳健性。利用南方科技大学(SUSTech)的实际数据进行的案例研究表明,电动汽车充电成本显著降低,该框架具有支持辅助电网服务的潜力。
{"title":"Trustworthy V2G scheduling and energy trading: A blockchain-based framework","authors":"","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":null,"pages":null},"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}
引用次数: 0
Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles 它能到达目的地吗?用于预测城市绿色货运中电动汽车下一趟充电状态的深度学习模型
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-24 DOI: 10.1016/j.etran.2024.100372
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.
为提高城市货运效率,促进绿色发展,中国实施了城市绿色货运(UGFD)项目,其中包括优化车辆交通管制政策和增加新能源汽车(NEV)数量。然而,对于使用电动汽车(新能源汽车的主要组成部分)执行配送任务的货运司机来说,续航焦虑是一个重大挑战。我们构建了一个预测模型,用于预测 UGFD 车辆到达下一个行程点时的充电状态(SOC)或电池剩余能量百分比,旨在缓解这一问题。该模型由三个模块组成:(1) 车辆 SOC 情境预测模块;(2) 车辆能耗预测模块;(3) 多视角 SOC 预测值融合模块。具体来说,在 SOC 情境预测模块中,历史 SOC 序列、车辆状态(装载/卸载、充电)和 SOC 点之间的时间间隔被用来准确描述情境变化趋势,并直接预测下一个行程点的车辆 SOC。能耗预测模块结合社区级和网格级的车辆停靠地理位置信息,利用天气、车辆参数等建立能耗空间动态关联模型。下一个行程点的车辆 SOC 是当前车辆 SOC 与预测能耗之间的差值。多视角 SOC 预测值融合模块是将情境和能耗视角的预测值进行组合,得出最终的车辆 SOC 预测值。以中国苏州为例,研究结果表明,所构建模型的平均绝对误差、均方根误差和对称平均绝对百分比误差分别比以 SOC 短期时间序列预测为主的基线模型小 23.67%、10.39% 和 20.03%。研究成果可为制定精细化能源管理、充电站布局和货运优化方法提供科学依据。
{"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":"","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":null,"pages":null},"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}
引用次数: 0
Industrialization challenges for sulfide-based all solid state battery 硫化物全固态电池的产业化挑战
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-23 DOI: 10.1016/j.etran.2024.100371
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.
全固态电池(ASSB)具有能量密度高、充电速度快、安全性高、工作温度范围宽、循环寿命长等优点,是下一代储能设备最有前途的解决方案。虽然近年来已取得了巨大的努力和突破,但其产业化仍面临许多挑战。本视角旨在总结 ASSB 大规模生产过程中面临的最关键挑战,以充分释放其潜力,推动实现更可持续的未来。
{"title":"Industrialization challenges for sulfide-based all solid state battery","authors":"","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":null,"pages":null},"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}
引用次数: 0
The role of EV fast charging in the urban context: An agent-based model approach 电动汽车快速充电在城市环境中的作用:基于代理模型的方法
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-22 DOI: 10.1016/j.etran.2024.100369
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.
本文采用基于代理的模拟方法,研究了快速充电基础设施在城市环境中的作用。仿真模型跟踪电动汽车(EV)的空间和时间行为,有助于对充电基础设施的部署进行全面分析。值得注意的是,该模型纳入了非参数排队动态、等待时间信息共享和不同的代理特征,从而加深了对主题的理解。该研究以腓特烈斯贝格市和哥本哈根市的大规模实施为基础,通过证明几个关键点来倡导采用快速充电器。首先,信息共享可显著减少等待时间,尤其是在快速充电网络内,在需求高峰期可减少多达 30% 的等待时间。其次,由 10-14 个网点组成的大型快充集群优于小型集群,这主要是由于信息共享缩短了等待时间并提高了等待时间预测的准确性。第三,基于未满足需求指标的投放策略比仅由观察到的需求模式驱动的投放策略效果更好。通过有效监控观察到的需求和未满足的需求,这些策略往往能更好地优化充电基础设施的布局。这些见解来自于模拟框架的复杂性和异质性,凸显了信息和未满足需求在这一领域的价值。
{"title":"The role of EV fast charging in the urban context: An agent-based model approach","authors":"","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":null,"pages":null},"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}
引用次数: 0
Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub 提高市郊铁路的能源效率:铁路-电动汽车智能枢纽的两阶段调度优化
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-20 DOI: 10.1016/j.etran.2024.100366
As the scale of suburban rail and electric vehicles (EVs) continues to expand with the revolution of electrification of transportation, park and ride (P&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.
随着交通电气化革命的推进,市郊铁路和电动汽车(EV)的规模不断扩大,停车和乘车(P&R)设施作为市郊铁路牵引变压器和电动汽车充电站之间的关键能源耦合点,越来越受到重视。然而,如何灵活协调多列列车双向电力流与电动汽车充电需求之间的能量分配成为一个亟待解决的问题。本文建立了一个铁路-电动汽车智能能源枢纽(SEH)框架,将列车、超级电容器(UC)和基于电池的电动汽车整合在一起。本文提出了一个可修正的两阶段优化模型,使铁路能够提供 R2X(铁路到任何地方)服务。第一阶段确定最佳列车轨迹并调整时刻表,使多列列车的能耗最小化。在第二阶段,以第一阶段确定的列车功率流为输入,协调电动汽车的充电策略和 UC 的充放电方案。同时,铁路造成的电压不平衡将受到限制,以符合 IEC/TR 61000-3-13 规定的限制。基于中国实际市郊铁路线路的案例研究表明,所提出的调度优化方法可以显著降低铁路和电动汽车的能耗。
{"title":"Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub","authors":"","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":null,"pages":null},"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}
引用次数: 0
On safety of swelled commercial lithium-ion batteries: A study on aging, swelling, and abuse tests 关于膨胀商用锂离子电池的安全性:老化、膨胀和滥用测试研究
IF 15 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-10-16 DOI: 10.1016/j.etran.2024.100368
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.
锂离子电池技术取得了长足的进步,使其成为智能手机等便携式电子设备的重要电源。2023 年,全球智能手机出货量将近 12 亿部,凸显了对这些电池的广泛依赖。然而,随着电池老化,它们可能会膨胀,并可能带来爆炸风险。为了研究膨胀电池的安全性,本研究对五大领先品牌的锂离子电池进行了加速老化和膨胀测试,这五大品牌共占全球智能手机市场份额的一半以上。研究涉及一系列综合测试,包括加速速率量热仪(ARC)测试,符合中国国家标准的机械、电气和热滥用测试,以及新电池和膨胀电池的气体成分和理论可燃性分析。研究结果表明,膨胀电池在浮充条件下通常表现得更安全,新电池和膨胀电池都能通过标准框架内的滥用测试。这项研究表明,膨胀锂离子电池的安全性不能一概而论地归结为危险或安全,而应根据具体环境进行评估。
{"title":"On safety of swelled commercial lithium-ion batteries: A study on aging, swelling, and abuse tests","authors":"","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":null,"pages":null},"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}
引用次数: 0
期刊
Etransportation
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1