Etransportation最新文献_第10页

Enhancing mechanical reliability and safety performance of a battery pack system for electric vehicles: A review 提高电动汽车电池组系统的机械可靠性和安全性能：综述

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-08 DOI: 10.1016/j.etran.2025.100469

Ibna Kawsar, Honggang Li, Binghe Liu, Yongzhi Zhang, Yongjun Pan

In electric vehicles (EVs), battery packs (BPs) are susceptible to mechanical and functional failures, where various environmental factors are influenced. Although standard testing procedures contribute to improved safety and overall performance, current research primarily examines individual factors, neglecting a comprehensive assessment of battery pack (BP) design solutions. This review comprehensively analyzes safety standards, empirical research, and advances in patent design to provide a broad perspective on the safety of battery pack systems (BPS). Specifically, it examines the responses of BPs to severe environmental conditions, including vibrations, mechanical shock, and collisions. The paper presents comprehensive design solutions, providing valuable knowledge on reducing the likelihood of failure and addressing safety concerns. The review emphasizes the importance of a complete optimization strategy for BPS, explicitly focusing on analyzing mechanical reactions, particularly concerning the reliability and efficacy of safety alerts. The conclusion highlights the imperative to meet operational requirements and safety standards in the design of BP, emphasizing the importance of adopting a robust structural design approach. The study suggested adopting harmonized standards for testing in realistic scenarios. Furthermore, this study makes an innovative contribution by exploring advanced technologies, such as FEA-DNN, reinforcement learning, and various intelligent optimization algorithms, to mitigate mechanical stresses, vibrations, shock impacts, and collision-induced damage in different work environments, providing engineering guidance to enhance the safety performance of BPS.

在电动汽车（ev）中，电池组（bp）容易受到机械和功能故障的影响，其中各种环境因素都受到影响。虽然标准测试程序有助于提高安全性和整体性能，但目前的研究主要是检查单个因素，而忽略了对电池组（BP）设计解决方案的全面评估。本文综合分析了安全标准、实证研究和专利设计的进展，为电池组系统（BPS）的安全性提供了一个广阔的视角。具体来说，它检查了bp对恶劣环境条件的响应，包括振动、机械冲击和碰撞。本文提出了全面的设计解决方案，提供了减少故障可能性和解决安全问题的宝贵知识。该综述强调了BPS完整优化策略的重要性，明确侧重于分析机械反应，特别是安全警报的可靠性和有效性。结论强调了在BP设计中满足运行要求和安全标准的必要性，强调了采用稳健的结构设计方法的重要性。该研究建议在现实情况下采用统一的测试标准。此外，本研究还探索了先进的技术，如有限元深度神经网络、强化学习和各种智能优化算法，以减轻不同工作环境下的机械应力、振动、冲击冲击和碰撞损伤，为提高BPS的安全性能提供了工程指导。

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引用次数: 0

Atmosphere-regulated thermal runaway characteristics and multidimensional safety assessment of sodium-ion and lithium-ion batteries 钠离子和锂离子电池大气调节热失控特性及多维安全评价

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-06 DOI: 10.1016/j.etran.2025.100475

Zhixiang Cheng, Zhiyuan Li, Yuxuan Li, Yin Yu, Chaoshi Liu, Zhenwei Wu, Peiyu Duan, Huang Li, Wenxin Mei, Qingsong Wang

Understanding and quantifying the thermal runaway behavior of emerging battery chemistries is essential for ensuring safety in real-world applications. This study systematically investigates the thermal runaway characteristics of sodium-ion (SIB) and lithium-ion (LIB) batteries of comparable volumes under both air and inert gas environments. Experimental results show that under low-oxygen conditions, SIB and nickel–cobalt–manganese (NCM) cells exhibit substantial mitigation of thermal runaway severity, including over 35 % decrease in gas generation metrics, while lithium iron phosphate (LFP) cells remain largely unaffected. In gas composition analysis, NCM cells show significant decreases in CO₂/CO and O₂/N₂ ratios, whereas SIB and LFP display no notable compositional changes. Based on experimental data and literature, a multidimensional database of thermal runaway parameters is developed, incorporating metrics such as gas explosiveness, toxicity, and heat of combustion. Three classical multi-criteria evaluation methods—Technique for Order Preference by Similarity to Ideal Solution, Principal Component Analysis, and a median-based approach—are applied and compared. To address limitations arising from dimensional and variance scale differences among parameters, an expected contribution method is proposed to enable balanced and consistent scoring. Results demonstrate that this method enhances fairness and interpretability, particularly in scenarios with substantial scale disparities among variables arising from cross-battery systems. This work establishes a quantitative safety assessment framework that enables cross-platform comparisons and provides guidance for battery system design, risk zoning, and thermal mitigation strategies. The framework is broadly applicable to emerging battery chemistries and advances battery safety evaluation across diverse application environments.

理解和量化新兴电池化学物质的热失控行为对于确保实际应用中的安全性至关重要。本研究系统地研究了同等体积的钠离子（SIB）和锂离子（LIB）电池在空气和惰性气体环境下的热失控特性。实验结果表明，在低氧条件下，SIB和镍钴锰（NCM）电池的热失控严重程度得到了显著缓解，其中产气指标降低了35%以上，而磷酸铁锂（LFP）电池在很大程度上不受影响。在气体成分分析中，NCM细胞的CO2/CO和O2/N2比例显著降低，而SIB和LFP细胞的成分没有显著变化。基于实验数据和文献，开发了一个多维热失控参数数据库，包括气体爆炸性、毒性和燃烧热等指标。应用并比较了三种经典的多准则评价方法——理想解相似性排序偏好法、主成分分析法和基于中值法。为了解决参数之间的维度和方差尺度差异所带来的限制，提出了一种期望贡献方法，以实现平衡和一致的评分。结果表明，该方法增强了公平性和可解释性，特别是在跨电池系统产生的变量之间存在巨大规模差异的情况下。这项工作建立了一个定量的安全评估框架，可以进行跨平台比较，并为电池系统设计、风险分区和热缓解策略提供指导。该框架广泛适用于新兴的电池化学物质，并在不同的应用环境中推进电池安全评估。

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引用次数: 0

3D-printed honeycomb lithium-silicon alloy anodes for stabilized interface in sulfide all-solid-state batteries 用于硫化物全固态电池稳定界面的3d打印蜂窝锂硅合金阳极

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-05 DOI: 10.1016/j.etran.2025.100476

Lutong Wang , Ziqi Zhang , Fuqiang Xu , Jixian Luo , Chuang Yi , Hong Li , Liquan Chen , Fan Wu

Solid-state batteries have emerged as a crucial development direction for next-generation energy storage technologies, owing to their high energy density, long cycle life, and excellent safety. However, the most challenging issue of interfacial contact/degradation in solid-state batteries remains unsolved. Herein, a novel Si-C interlocking honeycomb electrode is designed/realized via 3D printing technology. Achieves 98.9 % capacity retention over 2100 cycles at 1C. The honeycomb pore walls form a mortise-tenon structure with the electrolyte to maintain good interfacial contact, while the hard carbon layer isolates the electrolyte from the lithium-silicon interface, thereby stabilizing the growth of the solid electrolyte interphase (SEI) and achieving stress-electrochemical coupling regulation. Moreover, as the honeycomb channels form an interpenetrating structure with the solid electrolyte, a three-dimensional ion transport network is established, shortening the lithium-ion diffusion path, enhancing the interfacial contact between the electrode and solid electrolyte, reducing the risk of lithium dendrite formation, and improving the rate performance of all-solid-state batteries. This approach leverages structural design to enhance material performance, for the first time enabling the compatibility of 3D-printed structured silicon-based anodes with sulfide-based all-solid-state systems, thus providing a scalable solution for next-generation high-energy-density batteries.

固态电池具有能量密度高、循环寿命长、安全性好等优点，已成为下一代储能技术的重要发展方向。然而，固态电池中最具挑战性的界面接触/降解问题仍未得到解决。本文采用3D打印技术设计/实现了一种新型硅碳联锁蜂窝电极。达到98.9%的容量保持超过2100循环在1C。蜂窝孔壁与电解质形成榫卯结构，保持良好的界面接触，而硬碳层将电解质与锂硅界面隔离，从而稳定固体电解质界面相（SEI）的生长，实现应力-电化学耦合调节。此外，由于蜂窝通道与固体电解质形成互穿结构，建立了三维离子输运网络，缩短了锂离子扩散路径，增强了电极与固体电解质的界面接触，降低了锂枝晶形成的风险，提高了全固态电池的速率性能。这种方法利用结构设计来提高材料性能，首次实现了3d打印结构硅基阳极与硫化物基全固态系统的兼容性，从而为下一代高能量密度电池提供了可扩展的解决方案。

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引用次数: 0

State-of-charge estimation over full battery lifespan under diverse fast-charging protocols: A lightweight base-error joint modeling framework 多种快速充电协议下全电池寿命的充电状态估计：轻量级基础误差联合建模框架

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-05 DOI: 10.1016/j.etran.2025.100474

Ganglin Cao , Shouxuan Chen , Yuanfei Geng , Shuzhi Zhang , Yao Jia , Rong Feng , Yongjun Liu

Accurate state-of-charge (SOC) online estimation during various multi-stage constant current (MCC) fast-charging protocols over battery entire lifespan holds significant importance. In this work, we develop a lightweight-training oriented data-driven base-error joint modeling framework to fill this research gap. Through deep learning-based initial-cycle data training and lightweight machine learning-based typical-cycle data training, we only extract approximately 1 % of whole battery data for data-driven base-error joint modeling. With consideration of SOC time-dependency, short-term Ampere-hour is further combined via a simple filter structure to guarantee final SOC estimation accuracy. The validation, derived from a public battery degradation dataset comprising 8 different MCC fast-charging protocols from 46 cells, demonstrates that our framework allows rapid data-driven base-error joint modeling with training time only about l min, where both average mean absolute error and average root mean square error of SOC estimation during various MCC fast-charging protocols over battery entire lifespan are roughly below 0.3 %. Our work, for the first time, reveals the possibility of joint data-driven model trained via extremely few data on accurate SOC online estimation with consideration of various MCC fast-charging protocols and battery degradation status, and also offers a pretty concise but efficient solution for multi-scenario battery aging diagnosis and voltage dynamics forecast. The code accompanying this work is available at https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation.

在不同的多级恒流（MCC）快速充电协议中，准确的在线估计电池的充电状态（SOC）具有重要的意义。在这项工作中，我们开发了一个面向轻量级训练的数据驱动基础误差联合建模框架来填补这一研究空白。通过基于深度学习的初始周期数据训练和基于轻量级机器学习的典型周期数据训练，我们只提取了大约1%的整个电池数据，用于数据驱动的基础误差联合建模。考虑到SOC的时间依赖性，通过简单的滤波器结构进一步组合短期安培小时，以保证最终的SOC估计精度。来自46个电池的8种不同MCC快速充电协议的公共电池退化数据集的验证表明，我们的框架允许快速数据驱动的基本误差联合建模，训练时间仅为1分钟，其中各种MCC快速充电协议下SOC估计的平均绝对误差和平均均方根误差在电池整个使用寿命期间大致低于0.3%。我们的工作首次揭示了通过极少量数据训练的联合数据驱动模型在考虑各种MCC快速充电协议和电池退化状态的情况下进行准确的SOC在线估计的可能性，并为多场景电池老化诊断和电压动态预测提供了一种非常简洁而高效的解决方案。本文附带的代码可从https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation获得。

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引用次数: 0

Multi-level passive-active thermal control for battery thermal runaway prevention and suppression in electric vehicles 电动汽车电池热失控预防与抑制多级被动-主动热控制

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-04 DOI: 10.1016/j.etran.2025.100467

Jiekai Xie , Junlin Li , Canbing Li , Xinyan Huang , Guoqing Zhang , Xiaoqing Yang

Resolving the contradiction between heat-dissipation during normal operation and thermal-insulation after thermal runaway (TR) is highly desirable for battery thermal safety system but remains challenges. Herein, a multi-leveled thermal control strategy, i.e., passive cooling - active cooling - passive suppression - active suppression, has been proposed for TR prevention-suppression of the battery packs. The system is primarily designed by modular composite phase change material (CPCM), liquid cooling (LC) plates and aerogel plates (APs). Firstly, the passive cooling CPCM coordinated with active LC enables a suitable working temperature, low temperature gradient and low energy consumption of the battery pack under variable environments. Secondly, the modular design of the battery pack couples with the passive thermal-insulation effect of APs, successfully preventing TR from propagating to other modules. Thirdly, APs work synergistically with dynamic LC, greatly enhancing the directional heat-dissipation, and consequently, the TR propagation can be suppressed to the lowest level. By the flexible dynamic flow rate adjustment, the TR of large-scaled battery packs with different configurations of 4S12P, 6S8P, 8S6P and 12S4P can be successfully suppressed in the initially-triggered cell.

解决电池正常工作时的散热与热失控后的隔热之间的矛盾是电池热安全系统迫切需要解决的问题，但仍是一个挑战。本文提出了一种多层热控制策略，即被动冷却-主动冷却-被动抑制-主动抑制，以防止电池组的TR抑制。该系统主要由模块化复合相变材料（CPCM）、液体冷却（LC）板和气凝胶板（APs）设计而成。首先，被动冷却CPCM与主动LC相协调，使电池组在可变环境下具有合适的工作温度、低温度梯度和低能耗。其次，电池组的模块化设计与ap的被动隔热效应耦合，成功地阻止了TR传播到其他模块。第三，ap与动态LC协同工作，大大增强了定向散热，从而将TR传播抑制到最低水平。通过灵活的动态流量调节，4S12P、6S8P、8S6P和12S4P不同配置的大型电池组的TR可以在初始触发电池中成功抑制。

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引用次数: 0

Full-scene battery self-heating method based on powertrain system for electric vehicles at extremely low temperatures 基于动力总成系统的电动汽车极低温全场景电池自热方法

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-04 DOI: 10.1016/j.etran.2025.100465

Heping Ling, Lei Yan, Hua Pan, Siliang Chen, Fang Li, Shiyun Zhang

The popularity of electric vehicles (EVs) in the cold regions is seriously hindered by the degradation of lithium-ion batteries (LIBs) at low temperatures. To settle such issue, it is necessary to preheat the LIBs to moderate temperature for normal operation. As one of attractive internal preheating methods, pulse self-heating possesses high heating rate and efficiency. However, the application of pulse self-heating still faces the challenges of the pulse current power source unavailable in EVs. Herein we proposed a novel battery self-heating method which reuses the powertrain system of EVs to generate pulse excitation onboard, eliminating additional hardware. Moreover, the decoupled control of battery self-heating and motor torque was further developed to achieve the full-scene application, including charging, parking and driving. When applied in EVs, the proposed self-heating method could realize fast temperature rising of battery pack, shortening 30.7 % charging time at −20 °C compared with the conventional heat pump method. It also achieves rapid startup of EVs even at low temperature of −38 °C with high heating rate (0.73 °C min⁻¹) and low energy consumption (4.2 % SOC), as well as maintains the dynamic performance during driving at −30 °C. The proposed method provides a promising solution to preheat the battery pack for EVs application at extremely low temperatures.

锂离子电池的低温降解严重阻碍了电动汽车在寒冷地区的普及。为了解决这个问题，有必要将lib预热到正常工作的温度。脉冲自加热具有较高的加热速率和效率，是一种很有吸引力的内部预热方法。然而，脉冲自加热的应用仍然面临着电动汽车无法获得脉冲电流电源的挑战。在此，我们提出了一种新的电池自加热方法，该方法利用电动汽车的动力总成系统产生车载脉冲激励，消除了额外的硬件。进一步开发电池自热和电机转矩的解耦控制，实现充电、停车、行驶全场景应用。应用于电动汽车时，所提出的自加热方法可以实现电池组的快速升温，与传统热泵方法相比，在−20℃的充电时间缩短了30.7%。在−38℃的低温条件下，也能实现电动汽车的快速启动，加热速率高（0.73℃min−1），能耗低（4.2 % SOC），且在−30℃下仍能保持行驶过程中的动态性能。该方法为在极低温度下对电动汽车电池组进行预热提供了一种很有前景的解决方案。

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引用次数: 0

Physics-enhanced U-net and deep reinforcement learning for automated optimization of pin-fin heat sinks in electric vehicle power modules 基于物理增强U-net和深度强化学习的电动汽车电源模块插片散热器自动优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-03 DOI: 10.1016/j.etran.2025.100463

Yubo Lian, Heping Ling, Gan Song, Jiapei Yang, Hanzhi Wang, Zhe Zhang, Shaokuan Mao, Bin He

The use of pin-fin structures in compact energy devices, such as electric vehicle power modules, is a widely adopted thermal management strategy to enhance heat transfer efficiency. In this study, we present an innovative deep learning framework that integrates a physics-enhanced U-net architecture with a deep reinforcement learning agent to achieve autonomous optimal design of pin-fin arrays. The physics-enhanced U-net is trained to predict thermal-flow fields, while the integrated deep reinforcement learning agent autonomously optimizes pin-fin configurations to minimize both pressure drop and junction temperature. First, we generate a high-fidelity training dataset through an automated computational pipeline that integrates COMSOL Multiphysics for thermal-flow field simulations with a custom Matlab script for parametric generation of 1080 training samples. Subsequently, we train our physics-enhanced U-net architecture to predict the velocity, pressure and temperature fields from various pin-fin structure inputs. The proposed model demonstrates both high prediction accuracy and robustness, achieving mean-squared-errors on the order of 10⁻⁴ for all output fields. As a result, the trained U-net model achieves exceptional prediction accuracy, demonstrating 93.9 % precision for pressure drop and 99.5 % for junction temperature. Finally, we integrate the deep reinforcement learning agent with the trained U-net model to establish an automated optimization framework for pin-fin design, enabling intelligent exploration of design space. The proposed deep learning framework successfully automates the optimization of pin-fin heat sinks for a high power density module. The model demonstrates exceptional capability in generating optimal designs, with the optimized configuration achieving an 8.8 K reduction in junction temperature and 11.3 % decrease in pressure drop comparing to a baseline design. These improvements can be translated into approximately 10 % augmentation in power output, which validates both the effectiveness and robustness of our deep learning driven design approach.

在电动汽车电源模块等紧凑型能源器件中，采用针翅结构是一种广泛采用的热管理策略，以提高传热效率。在本研究中，我们提出了一个创新的深度学习框架，该框架将物理增强的U-net架构与深度强化学习代理集成在一起，以实现引脚鳍阵列的自主优化设计。物理增强的U-net经过训练，可以预测热流场，而集成的深度强化学习代理可以自主优化引脚鳍配置，以最小化压降和结温。首先，我们通过自动化计算管道生成高保真度的训练数据集，该管道集成了COMSOL Multiphysics用于热流场模拟，以及用于参数化生成1080个训练样本的自定义Matlab脚本。随后，我们训练了物理增强的U-net架构，以预测来自不同鳍片结构输入的速度、压力和温度场。该模型具有较高的预测精度和鲁棒性，所有输出字段的均方误差均在10−4量级。结果，训练后的U-net模型达到了优异的预测精度，对压降的预测精度为93.9%，对结温的预测精度为99.5%。最后，我们将深度强化学习智能体与训练好的U-net模型相结合，建立了鳍片设计的自动化优化框架，实现了设计空间的智能探索。提出的深度学习框架成功地自动优化了高功率密度模块的鳍片散热器。该模型在生成优化设计方面表现出卓越的能力，与基线设计相比，优化后的配置实现了结温降低8.8 K，压降降低11.3%。这些改进可以转化为大约10%的功率输出增加，这验证了我们深度学习驱动设计方法的有效性和鲁棒性。

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引用次数: 0

Cross-Domain Feature-Based Battery State-of-Health Estimation from Rest Period for Real-World Electric Vehicles 基于跨域特征的电动汽车静息期电池健康状态估计

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-02 DOI: 10.1016/j.etran.2025.100471

Siyi Tao , Jiangong Zhu , Yuan Li , Bo Jiang , Wei Chang , Haifeng Dai , Xuezhe Wei

Accurate power battery state-of-health (SOH) estimation is essential for ensuring the stable and reliable operation of electric vehicles (EVs). However, the diversity of charging methods and battery materials (nickel-cobalt-manganese (NCM) and lithium iron phosphate (LFP)) poses challenges for generalizing SOH estimation on field data. In this study, we propose a general cross-domain feature extraction method that integrates time-domain (TD) and frequency-domain (FD) features, along with inter-cell inconsistency features, from a two-minute post-charging rest period. Leveraging datasets from 106 real EVs encompassing 17,729 charging cycles and 28 laboratory cells with 10,912 charging cycles, we employ lightweight tree-based models for reliable and rapid SOH estimation. For EVs equipped with five different capacities of NCM and LFP batteries under various charging conditions, a single unified model is employed across all cases, yielding a mean absolute percentage error (MAPE) of less than 1.94% and a maximum error (MAXE) below 6.28%. This study highlights the potential of features from post-charging rest period to enable high-accuracy SOH estimation in real-world conditions, contributing to reduced costs and improved efficiency for future TWh-scale power battery market.

准确的动力电池健康状态（SOH）估计是保证电动汽车稳定可靠运行的关键。然而，充电方法和电池材料（镍钴锰（NCM）和磷酸铁锂（LFP））的多样性给现场数据的SOH估计带来了挑战。在这项研究中，我们提出了一种通用的跨域特征提取方法，该方法结合了充电后两分钟休息时间的时域（TD）和频域（FD）特征以及细胞间不一致特征。利用106辆真实电动汽车的17,729个充电周期和28个实验室电池的10,912个充电周期的数据集，我们采用轻量级的基于树的模型进行可靠和快速的SOH估计。对于搭载5种不同容量NCM和LFP电池的电动汽车，在不同充电条件下均采用统一模型，平均绝对百分比误差（MAPE）小于1.94%，最大误差（MAXE）小于6.28%。这项研究强调了充电后休息期的特征在现实条件下实现高精度SOH估计的潜力，有助于降低成本，提高未来太瓦时规模的动力电池市场的效率。

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引用次数: 0

Maritime electrification pathways for sustainable shipping: Technological advances, environmental drivers, challenges, and prospects 可持续航运的海上电气化途径：技术进步、环境驱动因素、挑战和前景

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-01 DOI: 10.1016/j.etran.2025.100462

Zhe Wang , Pengzhi Liao , Fei Long , Zhengquan Wang , Yulong Ji , Fenghui Han

Maritime electrification has gained unprecedented momentum as the shipping industry faces stringent global decarbonization targets and increasingly rigorous International Maritime Organization (IMO) regulations. This review provides a systematic assessment of the technological advances, environmental drivers, challenges, and future prospects of ship electrification, with a focus on three primary pathways: Battery-Electric Ships (BES), Hybrid-Electric Ships (HES), and Fuel Cell Electric Ships (FCES). The analysis encompasses technological maturity levels (TRL), economic competitiveness, lifecycle environmental performance, and regional deployment feasibility. Findings indicate that HES currently dominate commercial applications owing to their operational flexibility and compliance readiness, while BES demonstrate strong potential for short-sea and inland shipping routes, and FCES represent a long-term solution for deep decarbonization provided that green hydrogen and ammonia infrastructure becomes available. The review highlights persistent barriers, including limited energy density for large vessels, insufficient megawatt-scale charging and refueling infrastructure, durability and reliability concerns under harsh marine conditions, and misaligned global policy frameworks. Notable contributions include the provision of quantitative TRL evaluations for BES, HES, and FCES, a comparative analysis of regional deployment strategies targeting emission-intensive maritime zones, and the identification of AI-enabled digital twin technologies as a promising approach to optimize energy management and fleet operations. To accelerate maritime electrification, future research is directed toward breakthroughs in solid-state batteries, advanced corrosion-resistant materials, safe and efficient hydrogen/ammonia storage, port-level renewable microgrids, and standardized international safety regulations. Overall, this review establishes a comprehensive roadmap for academia, industry stakeholders, and policymakers to advance the transition toward sustainable, zero-emission shipping.

随着航运业面临严格的全球脱碳目标和国际海事组织（IMO）日益严格的规定，海上电气化获得了前所未有的动力。本文对船舶电气化的技术进步、环境驱动因素、挑战和未来前景进行了系统评估，重点介绍了三种主要途径：电池电动船舶（BES）、混合动力电动船舶（HES）和燃料电池电动船舶（FCES）。分析包括技术成熟度水平（TRL）、经济竞争力、生命周期环境绩效和区域部署可行性。研究结果表明，由于其操作灵活性和合规性，HES目前在商业应用中占主导地位，而BES在短途和内陆航线上显示出强大的潜力，而FCES则代表了深度脱碳的长期解决方案，前提是绿色氢和氨基础设施可用。该评估强调了持续存在的障碍，包括大型船舶的能量密度有限，兆瓦级充电和加油基础设施不足，恶劣海洋条件下的耐久性和可靠性问题，以及不一致的全球政策框架。值得注意的贡献包括为BES、HES和FCES提供定量TRL评估，对针对排放密集型海域的区域部署策略进行比较分析，以及将人工智能支持的数字孪生技术确定为优化能源管理和船队运营的有前途的方法。为了加速海上电气化，未来的研究方向是在固态电池、先进的耐腐蚀材料、安全高效的氢/氨储存、港口级可再生微电网以及标准化的国际安全法规方面取得突破。总体而言，本综述为学术界、行业利益相关者和政策制定者制定了全面的路线图，以推进向可持续、零排放航运的过渡。

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引用次数: 0

Optimizing the charging behaviors of private BEVs to enhance coordinated charging and V2G in Beijing 优化北京市私人纯电动汽车充电行为，增强充电与V2G的协同

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-01 DOI: 10.1016/j.etran.2025.100470

Bowen Tian , Wei Shen , Chongyu Zhang , James E. Anderson , Michael W. Degner , Xi Lu , Sheng Zhao , Ye Wu , Shaojun Zhang

Deep electrification of China's transport sector offers CO₂ emission reduction potential but poses reliability challenges to the urban power system. Smart charging strategies for battery electric vehicles (BEVs), including coordinated charging and vehicle-to-grid (V2G), are one of the most promising approaches to realizing the trade-off between decarbonization and stability of the electricity grid. In this study, an integrated model coupling load prediction and unit dispatching was developed to evaluate the multiple impacts of smart charging strategies considering the heterogeneity of individual driving and charging behaviors expected in Beijing in 2030. Compared with previous work, we have further revealed the different impacts of changing drivers' charging preference under uncoordinated charging, coordinated charging and V2G. The lowest operating cost and CO₂ emissions occur in the workplace charging preference (WCP) scenario with uncoordinated charging, but occur in the daily charging (DC) scenario when V2G is applied. It is indicated that smart charging strategies could simultaneously reduce grid operating costs and CO₂ emissions by decreasing the net load (thermal unit power outputs) on the electricity grid. Compared with the uncoordinated charging, using coordinated charging could reduce daily operating cost by 2.67 million RMB and daily CO₂ emissions by 10.23 kt on average, and the adoption of V2G could further increase the reductions to 8.74 million RMB and 24.25 kt CO₂. Annual CO₂ emission reductions enabled by coordinated charging and V2G are estimated to be 3700 kt and 8850 kt, respectively, which are equivalent to 1.2 × and 2.9 × the projected total emissions of the Beijing private BEV fleet. Increases in V2G participation can also smooth the net load profile and improve grid stability. In the DC scenario, the application of V2G reduced the peak net load by almost 30 % compared to the uncoordinated charging. Furthermore, there is a synergy between V2G participation and renewable energy (RE) development. Improving the electricity system and charging technology during future fleet electrification may be facilitated by coordinated charging and V2G opportunities.

中国交通运输行业的深度电气化提供了二氧化碳减排的潜力，但对城市电力系统的可靠性提出了挑战。纯电动汽车（bev）的智能充电策略，包括协调充电和车辆到电网（V2G），是实现脱碳和电网稳定之间权衡的最有前途的方法之一。考虑到2030年北京市居民个人驾驶和充电行为的异质性，建立了负荷预测和单元调度耦合的综合模型，以评估智能充电策略的多重影响。在此基础上，进一步揭示了非协调充电、协调充电和V2G下驾驶员充电偏好变化的不同影响。在不协调充电的工作场所充电偏好（WCP）场景下，运行成本和二氧化碳排放量最低，而在应用V2G的日常充电（DC）场景下，运行成本和二氧化碳排放量最低。研究表明，智能充电策略可以通过降低电网净负荷（热电机组输出功率）来同时降低电网运行成本和二氧化碳排放。与不协调充电方式相比，采用协调充电方式平均可降低运营成本267万元/日，减少二氧化碳排放10.23 kt；采用V2G方式可进一步降低运营成本874万元/日，减少二氧化碳排放24.25 kt。通过协调充电和V2G，预计每年的二氧化碳减排量分别为3700千吨和8850千吨，相当于北京私人纯电动汽车预计总排放量的1.2倍和2.9倍。V2G参与的增加还可以平滑净负载分布并提高电网稳定性。在直流场景中，与不协调充电相比，V2G的应用将峰值净负载降低了近30%。此外，V2G参与与可再生能源发展之间也有协同作用。通过协调充电和V2G机会，可以促进未来车队电气化过程中电力系统和充电技术的改进。

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引用次数: 0