Etransportation最新文献

Improving fuel cell vehicle efficiency: Exploring dynamic cooling strategies for stack radiators with intermittent spray cooling 提高燃料电池汽车的效率：探索采用间歇喷雾冷却的堆栈式散热器的动态冷却策略

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-24 DOI: 10.1016/j.etran.2024.100384

Rajendran Prabakaran, M. Mohamed Souby, Jie Liu, Sung Chul Kim

Advancements in stack cooling via air-cooled radiators for fuel cell (FC) electric vehicles have attracted significant attention. In this study, continuous spray cooling (CTSC) and intermittent spray cooling (IMSC) approaches for FC vehicles were developed at a lab-scale level. Additionally, the thermo-evaporation performance of various IMSC strategies, involving different spray intervals (0–120 s), continuous spray periods (10–60 s), and duty cycles (25–100 %), was investigated. Steady-state analysis revealed that, compared to conventional stack radiators, the CTSC approach using Nozzle#2 achieved superior thermal efficiency (η_th) with an improvement of 36.6–83.8 %, and enhanced spray evaporation efficiency (η_ev) by 18.2–23.9 %. In contrast, Nozzle#1 yielded only a 16.2–52.5 % increase in η_th and an 11.4–18.6 % improvement in η_ev. Compared to CTSC, IMSC extended the low-temperature operating range of the radiator even during the spray-off periods, leading to improved spray evaporation performance. However, excessive coolant exit temperature and heat rejection rate fluctuations were observed at higher spray periods with longer intervals (IMSC-60-60I and IMSC-40-40I) and lower duty cycles (<50 %). On the other hand, the IMSC strategy with shorter intervals and spray periods, i.e., IMSC-30-20I, was identified as optimal, offering a 55.7 % improvement in η_ev compared to CTSC, despite a 2.8 % reduction in η_th. Overall, the optimal IMSC configuration exhibited a 69.4 % higher heat rejection capacity compared to conventional air-cooled stack radiators. Furthermore, variations in η_th were validated using existing correlations, and new empirical correlations for both η_th and air-side heat transfer coefficient were developed, with prediction accuracies of approximately 86 % and 85 %, respectively. Additionally, the radiator's heat transfer area could be reduced by up to 76.2 %, despite a 7.5 % increase in vehicle curb weight. In summary, this study highlights the potential of using IMSC strategies for stack radiators in FC vehicles. The findings provide valuable insights for designing and implementing IMSC-enhanced radiators in real-world applications.

燃料电池（FC）电动汽车通过风冷散热器进行堆栈冷却的技术进步引起了广泛关注。在这项研究中，在实验室规模的水平上开发了用于燃料电池汽车的连续喷雾冷却（CTSC）和间歇喷雾冷却（IMSC）方法。此外，还研究了各种 IMSC 策略的热蒸发性能，包括不同的喷淋间隔（0-120 秒）、连续喷淋时间（10-60 秒）和占空比（25%-100%）。稳态分析表明，与传统的堆栈式散热器相比，使用喷嘴 #2 的 CTSC 方法实现了更高的热效率（ηth），提高了 36.6-83.8 %，喷雾蒸发效率（ηev）提高了 18.2-23.9 %。相比之下，喷嘴 #1 的 ηth 仅提高了 16.2-52.5%，ηev 提高了 11.4-18.6%。与 CTSC 相比，IMSC 甚至在喷雾关闭期间也能延长散热器的低温工作范围，从而改善了喷雾蒸发性能。然而，在较长的喷淋周期（IMSC-60-60I 和 IMSC-40-40I）和较低的占空比（<50 %）下，冷却剂出口温度和排热速率波动过大。另一方面，间隔和喷淋时间较短的 IMSC 策略（即 IMSC-30-20I）被确定为最佳策略，与 CTSC 相比，ηev 提高了 55.7%，尽管 ηth 降低了 2.8%。总体而言，最佳 IMSC 配置的排热能力比传统的风冷叠片散热器高出 69.4%。此外，ηth 的变化已通过现有的相关系数进行了验证，并针对 ηth 和空气侧传热系数开发了新的经验相关系数，预测精度分别达到约 86% 和 85%。此外，尽管车辆整备质量增加了 7.5%，但散热器的传热面积最多可减少 76.2%。总之，本研究强调了将 IMSC 策略用于 FC 汽车叠层散热器的潜力。研究结果为在实际应用中设计和实施 IMSC 增强型散热器提供了宝贵的见解。

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引用次数: 0

Resource-efficient artificial intelligence for battery capacity estimation using convolutional FlashAttention fusion networks 利用卷积闪存融合网络进行电池容量估算的资源节约型人工智能

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-22 DOI: 10.1016/j.etran.2024.100383

Zhilong Lv , Jingyuan Zhao

Accurate battery capacity estimation is crucial for optimizing lifespan and monitoring health conditions. Deep learning has made notable strides in addressing long-standing issues in the artificial intelligence community. However, large AI models often face challenges such as high computational resource consumption, extended training times, and elevated deployment costs. To address these issues, we developed an efficient end-to-end hybrid fusion neural network model. This model combines FlashAttention-2 with local feature extraction through convolutional neural networks (CNNs), significantly reducing memory usage and computational demands while maintaining precise and efficient health estimation. For practical implementation, the model uses only basic parameters, such as voltage and charge, and employs partial charging data (from 80 % SOC to the upper limit voltage) as features, without requiring complex feature engineering. We evaluated the model using three datasets: 77 lithium iron phosphate (LFP) cells, 16 nickel cobalt aluminum (NCA) cells, and 50 nickel cobalt manganese (NCM) oxide cells. For LFP battery health estimation, the model achieved a root mean square error of 0.109 %, a coefficient of determination of 0.99, and a mean absolute percentage error of 0.096 %. Moreover, the proposed convolutional and flash-attention fusion networks deliver an average inference time of 57 milliseconds for health diagnosis across the full battery life cycle (approximately 1898 cycles per cell). The resource-efficient AI (REAI) model operates at an average of 1.36 billion floating point operations per second (FLOPs), with GPU power consumption of 17W and memory usage of 403 MB. This significantly outperforms the Transformer model with vanilla attention. Furthermore, the multi-fusion model proved to be a powerful tool for evaluating capacity in NCA and NCM cells using transfer learning. The results emphasize its ability to reduce computational complexity, energy consumption, and memory usage, while maintaining high accuracy and robust generalization capabilities.

准确估算电池容量对于优化电池寿命和监控电池健康状况至关重要。深度学习在解决人工智能界长期存在的问题方面取得了显著进展。然而，大型人工智能模型往往面临计算资源消耗大、训练时间长、部署成本高等挑战。为了解决这些问题，我们开发了一种高效的端到端混合融合神经网络模型。该模型将 FlashAttention-2 与卷积神经网络（CNN）的局部特征提取相结合，大大降低了内存使用量和计算需求，同时保持了精确高效的健康估计。在实际应用中，该模型仅使用电压和电量等基本参数，并采用部分充电数据（从 80% SOC 到上限电压）作为特征，无需复杂的特征工程。我们使用三个数据集对该模型进行了评估：77 个磷酸铁锂（LFP）电池、16 个镍钴铝（NCA）电池和 50 个镍钴锰（NCM）氧化物电池。在锂铁磷酸盐电池健康评估方面，该模型的均方根误差为 0.109%，决定系数为 0.99，平均绝对百分比误差为 0.096%。此外，所提出的卷积和闪存融合网络在整个电池生命周期（每个电池约 1898 个周期）的健康诊断中，平均推理时间为 57 毫秒。资源节约型人工智能（REAI）模型的平均运行速度为每秒 13.6 亿次浮点运算（FLOPs），GPU 功耗为 17W，内存使用量为 403 MB。这明显优于使用 vanilla 注意力的 Transformer 模型。此外，事实证明多融合模型是利用迁移学习评估 NCA 和 NCM 单元容量的强大工具。结果表明，该模型能够降低计算复杂度、能耗和内存使用量，同时保持高精度和强大的泛化能力。

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引用次数: 0

Recent advances and perspectives in enhancing thermal state of lithium-ion batteries with phase change materials: Internal and external heat transfer enhancement factors 利用相变材料改善锂离子电池热状态的最新进展和前景：内部和外部传热增强因素

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-15 DOI: 10.1016/j.etran.2024.100381

Sagar Vashisht , Rajat , Dibakar Rakshit

Electric vehicles (EVs) play a crucial role in reducing fuel consumption and emissions, underscoring the importance of lithium-ion batteries (Li-ion) in powering these vehicles. However, Li-ion batteries are susceptible to degradation, capacity loss, and catastrophic failure due to temperature fluctuations, necessitating efficient thermal management. This review explores advancements and challenges in PCM-based battery thermal management systems (BTMS), focusing on internal and external factors influencing performance. It discusses internal factors such as material-level improvements in PCM-based BTMS, including solutions like SiC and EG-based PCM, flexible composite PCM, and serpentine-shaped PCM. External factors, such as fluid flow dynamics, cell spacing, and shape, significantly influence BTMS performance. Critical considerations include evaluating air- and liquid-based approaches and integrating heat pipes with PCM for passive BTMS. Furthermore, understanding the influence of these factors on temperature uniformity and heat dissipation is essential. The paper concludes by outlining future trends in PCM-based battery thermal management, emphasizing the utilization of flexible PCM and copper foam-enhanced PCM alongside hybrid BTMS configurations to optimize performance. By comprehensively addressing internal and external factors, BTMS can enhance Li-ion battery efficiency and lifespan in EVs.

电动汽车（EV）在减少燃料消耗和排放方面发挥着至关重要的作用，这凸显了锂离子电池（Li-ion）在为这些车辆提供动力方面的重要性。然而，锂离子电池易受温度波动的影响而出现性能下降、容量损失和灾难性故障，因此必须进行有效的热管理。本综述探讨了基于 PCM 的电池热管理系统 (BTMS) 的进展和挑战，重点关注影响性能的内部和外部因素。它讨论了内部因素，如基于 PCM 的 BTMS 在材料层面的改进，包括基于 SiC 和 EG 的 PCM、柔性复合 PCM 和蛇形 PCM 等解决方案。流体流动动力学、电池间距和形状等外部因素对 BTMS 性能有重大影响。关键的考虑因素包括评估基于空气和液体的方法，以及将热管与 PCM 集成用于被动式 BTMS。此外，了解这些因素对温度均匀性和散热的影响也至关重要。论文最后概述了基于 PCM 的电池热管理的未来趋势，强调在混合 BTMS 配置中使用柔性 PCM 和铜泡沫增强 PCM，以优化性能。通过全面解决内部和外部因素，BTMS 可以提高电动汽车中锂离子电池的效率和寿命。

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引用次数: 0

Comprehensive energy footprint of electrified fleets: School bus fleet case study 电气化车队的综合能源足迹：校车车队案例研究

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-12 DOI: 10.1016/j.etran.2024.100379

Joon Moon , Athar Hanif , Qadeer Ahmed

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel, depending on the specific price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

本文提出了一个综合框架，用于在部署电气化车队之前估算其能源足迹和效益。为了支持这一分析，本文引入了一个以控制为导向的电动公交车模拟器模型，该模型不仅能捕捉到驾驶功率要求，还结合了车厢行为热模型和用于加热和冷却的暖通空调（HVAC）系统。通过分析当前的校车路线和道路地形数据，考虑到校车当前的运行特点，对能源需求和经济效应进行了估算。作为一项案例研究，它探讨了美国俄亥俄州中央学区校车电动化的潜在优势，重点是节约能源和减少对环境的影响。我们的研究结果表明，与汽油校车相比，过渡到电动校车可实现高达 76% 的节能，与柴油校车相比，可实现 67% 的节能。在经济上，根据具体的价格比率，如果换算成运营成本，与汽油相比可节约 52%-65% 的能源，与柴油相比可节约 27%-47% 的能源。我们使用校车车队的实际运营数据对模型的准确性进行了校准。此外，本研究还通过能源足迹分析为充电要求提供了基础性见解。本研究通过一个具体案例研究，全面展示了车辆电气化的初步分析结果，为推动可持续交通做出了贡献。它强调了电动校车的实际应用和车辆效率的优化，与交通领域更广泛的生态友好型倡议相一致。

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引用次数: 0

Simulation of single-layer internal short circuit in anode-free batteries 无阳极电池单层内部短路模拟

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-12 DOI: 10.1016/j.etran.2024.100380

Nitesh Gupta , Shanhai Ge , Tatsuro Sasaki , Kaiqiang Qin , Ryan S. Longchamps , Koichiro Aotani , Chao-Yang Wang

The lithium metal battery technologies that can fulfil the high energy density goal have grave safety concerns and lead to fire/smoke, leading to battery failure. Out of all the causes of fire, internal short circuits (ISC) are the most common. The ISC safety test is considered a crucial checkpoint for battery design, but the present tests, like nail penetration and ball indentation, lack certainty and reproducibility in declaring battery safety. In light of these experimental limitations, we present an experimentally validated ISC simulation method that can elucidate fundamental mechanisms underlying ISC. The experimental/simulation method isolates the shorted single-layer from the unshorted layers, which helps in scrutinizing ISC and thermal runaway (TR) phenomenon. The present ISC model is flexible and computationally inexpensive compared to other 3D electrochemical thermal coupled (ECT) ISC simulations for a whole battery pack. We show the experimental validation of terminal voltage, short-circuit current, shorting resistance, internal temperature and other derived parameters of an ISC simulation of anode-free cell. Finally, the simulation model was used to do a parametric study for an anode-free battery (AFB) and the effect of cell design, and shorting parameters on ISC was scrutinized.

能够实现高能量密度目标的锂金属电池技术存在严重的安全隐患，容易起火/冒烟，导致电池故障。在所有起火原因中，内部短路（ISC）是最常见的。内部短路安全测试被认为是电池设计的关键检查点，但目前的测试，如钉子穿透和球压痕，在宣布电池安全方面缺乏确定性和可重复性。鉴于这些实验局限性，我们提出了一种经过实验验证的 ISC 模拟方法，该方法可以阐明 ISC 的基本机制。该实验/模拟方法将短路单层与未短路层隔离开来，有助于仔细研究 ISC 和热失控 (TR) 现象。与其他针对整个电池组的三维电化学热耦合（ECT）ISC 仿真相比，本 ISC 模型灵活且计算成本低廉。我们展示了无阳极电池 ISC 模拟的端电压、短路电流、短路电阻、内部温度和其他衍生参数的实验验证。最后，我们利用仿真模型对无阳极电池（AFB）进行了参数研究，并仔细研究了电池设计和短路参数对 ISC 的影响。

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引用次数: 0

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas 用于在城市地区战略布局异地加氢站的先进空间决策模型

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2024-11-02 DOI: 10.1016/j.etran.2024.100375

Akram Elomiya , Jiří Křupka , Vladimir Simic , Libor Švadlenka , Petr Průša , Stefan Jovčić

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

加氢站（HRS）的战略布局对于成功采用氢燃料电池汽车（HFCV）和促进可持续城市交通至关重要。然而，现有的使用地理信息系统（GIS）和多标准决策（MCDM）的空间决策模型往往止步于生成适宜性地图，并依赖于简单或任意的站点布置方法，如固定服务半径，而没有优化忽略固有不确定性的空间分布，从而限制了决策过程的有效性。本研究开发了一种先进的空间决策模型，用于处理不确定性并优化捷克布拉格的 HRS 布点。该模型整合了多种方法：(i) 利用 21 项标准，涵盖可达性、环境、基础设施和社会经济等维度，并使用模糊分析层次过程（FAHP）对标准权重进行优先排序，以管理专家判断中的不确定性。地理信息系统适宜性分析确定了最佳区域，18.13%的布拉格被归类为非常适合部署 HRS 的区域。(ii) 采用模糊 C-Means（FCM）聚类法优化站点分布，解决 HRS 布点的不确定性，提出了 10 个最佳地点，并通过 0.68 的 Silhouette 分数验证。(iii) 通过敏感性分析评估模型性能，揭示对标准变化的响应。为了对建议的 HRS 位置进行评估和排序，我们将遗传算法（GA）与理想解决方案相似性排序偏好技术（TOPSIS）相结合，通过探索更广阔的解决方案空间来优化选择过程。此外，可达性分析评估了应急响应覆盖范围，确保了高效的响应时间。这一多方法框架确保了以数据为导向的稳健选址方法，优化了可达性，最大限度地减少了对环境的影响，并促进了可持续的城市交通。它推进了战略性基础设施规划，开创了在空间决策中整合先进分析技术以处理不确定性和自动选址的先例，并适用于不同的城市环境。

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引用次数: 0

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

Etransportation

Pub Date : 2024-10-30 DOI: 10.1016/j.etran.2024.100378

Aihua Tang , Yuchen Xu , Pan Liu , Jinpeng Tian , Zikang Wu , Yuanzhi Hu , Quanqing Yu

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。

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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

Etransportation

Pub Date : 2024-10-30 DOI: 10.1016/j.etran.2024.100377

Shilin Wang , Chenyu Zhang , Dapeng Chen , Yiming Qin , Lejun Xu , Yitong Li , Qinzheng Wang , Xuning Feng , Huaibin Wang

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。

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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

Etransportation

Pub Date : 2024-10-29 DOI: 10.1016/j.etran.2024.100373

Pei Peng, Zhendong Sun, Yujie Wang, Zonghai Chen

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%。

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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

Etransportation

Pub Date : 2024-10-28 DOI: 10.1016/j.etran.2024.100374

Maher G.M. Abdolrasol , Afida Ayob , M.S. Hossain Lipu , Shaheer Ansari , Tiong Sieh Kiong , Mohamad Hanif Md Saad , Taha Selim Ustun , Akhtar Kalam

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。

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引用次数: 0