Etransportation最新文献_第10页

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 : 2025-12-01 Epub 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

Advancing SOC estimation in LiFePO4 batteries: Enhanced dQ/dV curve and short-pulse methods LiFePO4电池SOC预估：改进的dQ/dV曲线和短脉冲方法

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-11 DOI: 10.1016/j.etran.2025.100466

Yizhao Gao, Simona Onori

Accurate state-of-charge (SOC) estimation for lithium iron phosphate (

{LiFePO}_{4}

) batteries remains challenging due to their inherently flat open-circuit voltage (OCV)–SOC characteristics, which impair observability for conventional voltage-based and equivalent circuit model (ECM) methods. To address this limitation, we propose a DQV-based SOC estimation framework that uses short-duration current pulses to extract informative voltage features. Complete DQV–SOC reference curves are constructed offline across multiple C-rates (

\pm

1/30C,

\pm

0.2C,

\pm

0.5C,

\pm

1C, and

\pm

2C). During operation, voltage responses from brief current pulses are processed via exponential fitting to generate smooth, noise-resilient DQV segments. These segments are fused with the reference data within an Unscented Kalman Filter (UKF), enabling closed-loop SOC estimation with low computational overhead. Experimental results highlight the significant influence of C-rates on the DQV-based SOC estimator. We observe that pulse currents significantly enhance SOC estimation convergence across the full SOC range [0, 1]. However, employing a single C-rate pulse may not ensure robustness across diverse SOC ranges, emphasizing the importance of carefully selecting C-rates to achieve SOC estimation convergence throughout the entire SOC range of [0, 1]. This research contributes to advancing reliable management practices for

{LiFePO}_{4}

batteries in electric vehicles.

由于磷酸铁锂（LiFePO4）电池固有的平坦开路电压(OCV) -SOC特性，影响了传统基于电压和等效电路模型（ECM）方法的可观察性，因此对其进行准确的荷电状态（SOC）估计仍然具有挑战性。为了解决这一限制，我们提出了一种基于dqv的SOC估计框架，该框架使用短持续时间电流脉冲提取信息电压特征。完整的DQV-SOC参考曲线在多种c -rate（±1/30C，±0.2C，±0.5C，±1C和±2C）下离线构建。在运行过程中，通过指数拟合处理短电流脉冲的电压响应，生成平滑、抗噪声的DQV段。这些片段与Unscented卡尔曼滤波器（UKF）中的参考数据融合，以低计算开销实现闭环SOC估计。实验结果表明，c率对基于dqv的SOC估计器有显著影响。我们观察到脉冲电流显著增强了整个SOC范围内SOC估计的收敛性[0,1]。然而，采用单一c -速率脉冲可能无法确保在不同SOC范围内的鲁棒性，这强调了在整个SOC范围内仔细选择c -速率以实现SOC估计收敛的重要性[0,1]。该研究有助于推进电动汽车磷酸铁锂电池的可靠管理实践。

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引用次数: 0

Three-dimensional modeling with experimental validation of non-PGM polymer electrolyte membrane fuel cells 非pgm聚合物电解质膜燃料电池的三维建模与实验验证

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1016/j.etran.2025.100479

Yiheng Pang , Rui Gao , Yujiang Song , Hui Xu , Yun Wang

High catalyst cost impedes PEM fuel cell (PEMFC) commercialization, making the development of high-performance non-platinum(Pt) group metal (PGM) cathode catalyst layers (CLs) critical for advancing fuel cell technology. CLs contribute to a major portion of PEMFCs cost due to the use of PGM catalysts. To reduce the cost, non-PGM catalysts offer a viable alternative to low-Pt loading. In this study, we develop a three-dimensional (3-D) model to investigate the reaction rate, oxygen, and liquid water distributions in PEMFCs with a focus on the non-PGM cathode catalyst layer, which provides unique insights into electrochemically coupled transport processes that cannot be resolved by reduced-dimension or experimental approaches. Experiments were conducted using two types of non-PGM catalysts, including Fe-N-C and Mn-N-C based materials, to validate the 3-D model predictions. It is shown that CL properties such as catalyst materials, porosity, and ionomer content can play important roles in PEMFCs voltage gain, highlighting the performance impact of non-PGM catalysts. Large variations in the liquid water and oxygen contents occur in the gas diffusion layer from the land to channel under 1 A/cm². The through-plane distributions under the channel show large spatial variations across the non-PGM CLs in oxygen and the electrolyte phase potential. Liquid water shows little change across the catalyst layer based on the 3-D model prediction. These findings advance PEMFC development by informing the design of durable, high-performance non-PGM CLs to reduce fuel cell cost for transportation applications.

高昂的催化剂成本阻碍了PEM燃料电池（PEMFC）的商业化，因此开发高性能的非铂族金属（Pt）阴极催化剂层（CLs）对于推进燃料电池技术至关重要。由于使用了PGM催化剂，CLs占了pemfc成本的很大一部分。为了降低成本，非pgm催化剂为低铂负载提供了可行的替代方案。在这项研究中，我们开发了一个三维（3-D）模型来研究pemfc中的反应速率、氧和液态水分布，重点研究了非pgm阴极催化剂层，这为电化学耦合输运过程提供了独特的见解，这些过程无法通过降维或实验方法来解决。实验使用了两种非pgm催化剂，包括Fe-N-C和Mn-N-C基材料，以验证三维模型的预测。结果表明，催化剂材料、孔隙度和离聚体含量等CL性质对pemfc的电压增益有重要影响，突出了非pgm催化剂对性能的影响。液态水和氧含量在1 A/cm2以下从陆地到通道的气体扩散层中发生较大变化。通道下的通平面分布在氧和电解质相电位中显示出较大的空间差异。根据三维模型预测，液态水在催化剂层上的变化不大。这些发现推动了PEMFC的发展，为设计耐用、高性能的非pgm CLs提供了信息，从而降低了运输应用中燃料电池的成本。

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引用次数: 0

Component-level analysis for developing an energy consumption model for battery electric vehicles (BEVs) in operation 基于组件级分析的纯电动汽车运行能耗模型开发

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-19 DOI: 10.1016/j.etran.2025.100472

Dongmin Kim, Kitae Jang

In battery electric vehicles (BEV), energy originates in the battery and is transmitted to the wheels through a series of energy conversion processes involving the inverter and motor. Therefore, understanding the energy conversion mechanisms in both the inverter and motor is essential for accurately modeling energy consumption. However, in previous studies, real-world driving data are often limited, making it challenging to fully analyze the complex and nonlinear relationships within each conversion component. In this study, we collected input–output data from the inverters and motors of fifty-four BEVs, measured repeatedly over time. The data revealed a piecewise nonlinear relationship between input and output, prompting us to partition the models by different phases: propulsion, regeneration, and battery status. For each phase, we applied linear mixed-effects models to account for the hierarchical structure of the data, estimating coefficients separately for the inverter and motor using a randomly selected 75% of the dataset. Through this component-level modeling approach, the models not only capture component-level random-effect parameters but also effectively model the nonlinear energy conversion characteristics at the component level. The two models were then integrated to estimate the total driving energy consumption of the BEVs, and the results were validated against actual observations using the total driving energy from the remaining 25% of the dataset. Model performance was evaluated using the Total Consumption Estimation Rate (TCER) and Mean Absolute Percentage Error (MAPE). The proposed model achieved at least 95.27% in TCER and 86.34% in MAPE, outperforming existing approaches with a 20% higher TCER and an MAPE approximately ten times lower on average. The comparison demonstrated that our model accurately estimates driving energy consumption, as it effectively captured the heterogeneous and nonlinear relationships between input and output energy for each component.

在纯电动汽车（BEV）中，能量来源于电池，并通过一系列涉及逆变器和电机的能量转换过程传递给车轮。因此，了解逆变器和电机的能量转换机制对于准确建模能量消耗至关重要。然而，在以往的研究中，真实驾驶数据往往是有限的，因此很难充分分析每个转换组件之间复杂的非线性关系。在这项研究中，我们收集了54辆纯电动汽车的逆变器和电机的输入输出数据，并在一段时间内反复测量。数据揭示了输入和输出之间的分段非线性关系，促使我们根据不同的阶段划分模型：推进，再生和电池状态。对于每个阶段，我们应用线性混合效应模型来解释数据的层次结构，使用随机选择的75%的数据集分别估计逆变器和电机的系数。通过构件级建模方法，模型不仅可以捕获构件级的随机效应参数，而且可以有效地模拟构件级的非线性能量转换特性。然后将这两个模型整合起来估算纯电动汽车的总驾驶能耗，并使用剩余25%的数据集中的总驾驶能耗对实际观测结果进行验证。使用总消耗估计率（TCER）和平均绝对百分比误差（MAPE）评估模型性能。该模型的TCER和MAPE分别达到95.27%和86.34%，优于现有的TCER高20%、MAPE平均低约10倍的方法。比较表明，我们的模型准确地估计了驱动能量消耗，因为它有效地捕获了每个组件的输入和输出能量之间的异质性和非线性关系。

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引用次数: 0

Predicting battery degradation for electric vertical take-off and landing (eVTOL) aircraft: A comprehensive review of methods, challenges, and future trends 预测电动垂直起降（eVTOL）飞机电池退化：方法、挑战和未来趋势的综合回顾

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.etran.2025.100477

Baoji Wang , Teng Xu , Bailin Zheng , Yue Kai , Kai Zhang

With the rapid development of intelligent technologies in aviation, electric vertical take-off and landing (eVTOL) aircraft have emerged as key players in the low-altitude economy, their battery performance directly impacts safety and cost, making accurate prediction essential. This paper presents a comprehensive review of the literature on battery degradation prediction methods for eVTOL aircraft, providing a brief overview on early modeling approaches and placing primary emphasis on recent advances in their applicability and limitations under unique operational scenarios of eVTOL, such as frequent takeoffs and landings, high power loads, and complex environmental conditions. Current prediction efforts primarily target key indicators including battery lifespan, health status, and capacity retention, employing a range of technical approaches such as electrochemical modeling, equivalent circuit modeling, data-driven algorithms like machine learning and deep learning, and hybrid physics-informed models that integrate domain knowledge with data analysis. The review systematically summarizes the main prediction methods and their evolution in different phases of the development of eVTOL technology. On this basis, we highlight existing technical bottlenecks and unresolved challenges, including the high demand for data and computational resources limiting real-time performance, poor accuracy of traditional models under high discharge rates and extreme conditions, challenges in accurately modeling complex multi-physics interactions and achieving a stable balance among prediction accuracy, interpretability, and real-time computational efficiency, as well as the scarcity of historical flight data affecting model reliability and generalization. This review also proposes future research directions to enhance the reliability and accuracy of battery degradation forecasting for eVTOL applications.

随着航空智能技术的快速发展，电动垂直起降飞机已成为低空经济领域的重要参与者，其电池性能直接影响到飞机的安全性和成本，因此准确的预测至关重要。本文全面回顾了eVTOL飞机电池退化预测方法的文献，简要概述了早期建模方法，并重点介绍了eVTOL在频繁起降、高功率负载和复杂环境条件等独特操作场景下的适用性和局限性。目前的预测工作主要针对关键指标，包括电池寿命、健康状态和容量保留，采用了一系列技术方法，如电化学建模、等效电路建模、数据驱动算法（如机器学习和深度学习），以及将领域知识与数据分析相结合的混合物理模型。本文系统地总结了eVTOL技术在不同发展阶段的主要预测方法及其演变。在此基础上，我们强调了现有的技术瓶颈和尚未解决的挑战，包括对数据和计算资源的高需求限制了实时性能，传统模型在高放电率和极端条件下的准确性较差，在准确建模复杂的多物理场相互作用以及实现预测精度，可解释性和实时计算效率之间的稳定平衡方面的挑战。历史飞行数据的稀缺性也影响了模型的可靠性和泛化。最后，提出了提高电池退化预测的可靠性和准确性的未来研究方向。

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引用次数: 0

Strain-rate-dependent failure behavior of lithium-ion batteries: Role of liquid electrolyte in impact safety 锂离子电池应变速率相关的失效行为：液体电解质在冲击安全中的作用

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-27 DOI: 10.1016/j.etran.2025.100490

Mingzhe Zhou , Jinyu Yan , Qingfei Ren , Yongrou Zhang , Lingling Hu

The structural integrity of lithium-ion batteries (LIBs) under dynamic loading is critical to their safe deployment in electric transportation systems. While dry-state testing of battery components is common, the influence of liquid electrolyte on battery failure under dynamic loading remains largely unexplored. This study investigates the out-of-plane compressive behavior of lithium iron phosphate (LFP) pouch cells in both dry and electrolyte-saturated states across a wide range of strain rates (0.005/s to 2000/s), using quasi-static and Split Hopkinson Pressure Bar (SHPB) tests. High-speed imaging and transparent cell designs enabled real-time visualization of electrolyte migration and structural deformation. The results show that, although electrolyte presence has little effect under quasi-static loading, it significantly lowers peak stress, strain, and stiffness at elevated strain rates. Microscopy reveals that confined electrolyte flow induces internal pore pressure, accelerates microcrack initiation in separators and electrode coatings. A mechanistic framework is proposed to explain how fluid–solid interactions degrade structural integrity at high rates. The findings demonstrate that dry-state testing overestimates battery resilience under impact and highlight the need to account for electrolyte effects in crash safety assessments. This work provides new insights into battery failure mechanisms relevant to electric mobility and supports the development of impact-tolerant energy storage systems and more comprehensive testing protocols for crashworthiness analysis.

动态载荷下锂离子电池的结构完整性对其在电力运输系统中的安全部署至关重要。虽然电池组件的干态测试很常见，但液体电解质对电池在动态负载下失效的影响在很大程度上仍未被探索。本研究通过准静态和分离式霍普金森压杆（SHPB）测试，研究了磷酸铁锂（LFP）袋状电池在干燥和电解质饱和状态下在大范围应变速率（0.005/s至2000/s）下的面外压缩行为。高速成像和透明电池设计使电解质迁移和结构变形的实时可视化成为可能。结果表明，虽然电解质的存在对准静态加载影响不大，但在高应变速率下，电解质显著降低峰值应力、应变和刚度。显微镜观察发现，受限的电解质流动引起内部孔隙压力，加速了隔膜和电极涂层的微裂纹萌生。提出了一个机制框架来解释流固相互作用如何以高速率降低结构完整性。研究结果表明，干状态测试高估了电池在冲击下的弹性，并强调了在碰撞安全评估中考虑电解质影响的必要性。这项工作为与电动汽车相关的电池故障机制提供了新的见解，并支持开发耐冲击储能系统和更全面的耐撞性分析测试协议。

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引用次数: 0

Insights into the failure mechanisms of leaky lithium-ion batteries for electric vehicles by a systematic multiscale analytical framework 基于系统多尺度分析框架的电动汽车锂离子电池泄漏失效机理研究

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.etran.2025.100504

Jing Hu , Caiping Zhang , Haoteng Guo , Jing Xu , Linjing Zhang , Tao Zhu , Yanru Zhang

Electrolyte leakage poses serious safety risks by shortening service life and elevating the risk of thermal runaway. A comprehensive understanding of the failure mechanisms is essential for effective safety management. However, such studies are hindered by the lack of reliable fault imitation methods, poor reproducibility of experimental data, and the complexity of side reactions. To address these challenges, this paper proposes a systematic, analytical framework that integrates reproducible fault imitation, cell regeneration, and systematic in-situ and ex-situ analyses to investigate external behaviors and reveal the failure mechanisms. Failure scenarios are imitated by drilling holes into the annular indentation of the aluminum safety valve. In-situ analyses reveal nonlinear degradation behavior and severe kinetic deterioration, primarily attributed to the degradation of the solid electrolyte interphase (SEI). Ex-situ techniques, including cell regeneration and comprehensive material characterization, are employed to distinguish between the impacts of electrolyte depletion and electrode damage. Electrolyte depletion is identified as the primary failure mechanism, which drives severe kinetic degradation and ultimately causing battery performance deterioration or even failure. In contrast, the electrode structure remains largely intact. Moreover, regeneration experiments have confirmed that partial performance recovery can be achieved through electrolyte replenishment. These methods and findings are expected to offer valuable insights for battery fault detection and recycling strategies.

电解液泄漏会缩短使用寿命，增加热失控的风险，造成严重的安全隐患。全面了解失效机制对有效的安全管理至关重要。然而，由于缺乏可靠的故障模拟方法，实验数据的可重复性差以及副反应的复杂性，这类研究受到阻碍。为了应对这些挑战，本文提出了一个系统的分析框架，该框架集成了可重复的故障模仿，细胞再生以及系统的原位和非原位分析，以研究外部行为并揭示故障机制。通过在铝制安全阀的环形压痕上钻孔来模拟故障情况。原位分析表明，固体电解质界面相（SEI）的降解导致了材料的非线性降解行为和严重的动力学劣化。非原位技术，包括细胞再生和综合材料表征，用于区分电解质耗尽和电极损伤的影响。电解质耗尽被认为是电池失效的主要机制，导致电池严重的动力学退化，最终导致电池性能下降甚至失效。相比之下，电极结构基本保持完整。此外，再生实验证实，通过补充电解质可以实现部分性能恢复。这些方法和发现有望为电池故障检测和回收策略提供有价值的见解。

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引用次数: 0

Thermal runaway behavior of large-format sodium-ion and lithium-iron phosphate batteries under different trigger sources: A comparative study 不同触发源下大规格钠离子电池和磷酸铁锂电池热失控行为的比较研究

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.etran.2025.100495

Zhen Liu , Mingjie Zhang , Kai Yang , Yuhong Jin , Hao Wang , Bin Wei , Jingbing Liu

Sodium-ion batteries (SIBs) have emerged as a promising complementary technology to lithium-ion batteries (LIBs), primarily due to their potential for cost-effectiveness and resource sustainability. However, the thermal safety of SIBs still needs to be evaluated, as it is crucial for their potential application in electric vehicles and energy storage fields. In this study, we systematically examine and compare the thermal runaway (TR) and gas venting behaviors of 185 Ah Cu-Fe-Mn-based sodium-ion (CFM) and 314 Ah LiFePO₄ (LFP) batteries under overcharging and overheating conditions-factors. Experimental results indicate that the TR process in CFM batteries exhibits distinct characteristics when compared to LFP batteries. Under overcharging conditions, CFM batteries experience more severe temperature fluctuations than those observed during overheating-maximum TR temperatures reach 620.9 °C and 587.3 °C, respectively-significantly higher than those recorded in LFP batteries. The activation time of the safety valve is similar to the onset of TR in both scenarios. Gas analysis reveals that the primary gaseous compositions generated during TR in CFM batteries are comparable to those produced by LFP batteries, with total gas volumes measuring 397.6 L during overheating and 699.3 L during overcharging. Although CFM batteries demonstrate superior resistance to overcharging relative to LFP counterparts, their elevated TR temperatures coupled with substantial emissions of combustible gases-including hydrogen, carbon monoxide, and methane considerably heighten combustion and explosion risks. These results may contribute to safer integration of CFM batteries in future applications, such as in electric vehicles, charging station and energy storage systems.

钠离子电池（sib）已经成为锂离子电池（lib）的一种有前途的补充技术，主要是因为它们具有成本效益和资源可持续性的潜力。然而，sib的热安全性仍然需要评估，因为它对于其在电动汽车和储能领域的潜在应用至关重要。在这项研究中，我们系统地研究和比较了185 Ah cu - fe - mn基钠离子（CFM）和314 Ah LiFePO4 （LFP）电池在过充和过热条件下的热失控（TR）和排气行为。实验结果表明，与LFP电池相比，CFM电池的TR过程具有明显的特点。在过充条件下，CFM电池的温度波动比过热时更严重，最高TR温度分别达到620.9°C和587.3°C，显著高于LFP电池。在两种情况下，安全阀的激活时间与TR的开始时间相似。气体分析表明，CFM电池在TR过程中产生的主要气体成分与LFP电池相当，过热时的总气体体积为397.6 L，过充时的总气体体积为699.3 L。尽管CFM电池相对于LFP电池具有更强的抗过充能力，但其较高的TR温度加上大量可燃气体（包括氢气、一氧化碳和甲烷）的排放大大增加了燃烧和爆炸的风险。这些结果可能有助于CFM电池在未来的应用中更安全的集成，例如电动汽车、充电站和储能系统。

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引用次数: 0

Battery field data and why it matters: Foundations for real-world electric vehicles 电池现场数据及其重要性：现实世界电动汽车的基础

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.etran.2025.100494

Kyunghyun Kim , Kyeongeun Cho , Kwangho Lee , Junyoung Yoon , Jung-Il Choi

The traditional paradigm of battery research, primarily rooted in controlled laboratory experiments, is being fundamentally reshaped by the influx of real-world field data. Although laboratory tests remain indispensable for isolating specific electrochemical mechanisms, they fall short of capturing the complex phenomena that arise under practical operating conditions. Field data offers essential insights into this complexity by revealing the intricate interplay among dynamic loads, thermal transients, and path-dependent degradation—interactions often obscured in simplified test protocols. This discrepancy underscores a significant gap in understanding, highlighting that field data is not merely a validation tool, but a vital source for uncovering new physics governing battery performance and aging in realistic environments. Harnessing this potential requires addressing critical challenges—from data quality and privacy to the integration of emerging methodologies in feature engineering, fleet analytics, and physics-informed machine learning. This review surveys large-scale fleet datasets alongside high-resolution vehicle- and cell-level measurements, and examines methodologies spanning state estimation, fault detection, and energy optimization. These developments collectively point to a paradigm shift in battery research—from passive diagnostics toward proactive lifecycle management. Ultimately, this trajectory leads to generalized battery foundation models: continuously evolving digital twins that actively shape, rather than merely predict, a battery’s entire lifecycle.

传统的电池研究模式，主要植根于受控的实验室实验，正在从根本上被现实世界现场数据的涌入所重塑。虽然实验室测试对于分离特定的电化学机制仍然是必不可少的，但它们无法捕捉在实际操作条件下出现的复杂现象。现场数据通过揭示动态载荷、热瞬态和路径相关的降解相互作用之间复杂的相互作用，为这种复杂性提供了重要的见解，而这些相互作用通常在简化的测试方案中被掩盖。这种差异凸显了人们在理解上的巨大差距，强调了现场数据不仅仅是一种验证工具，而且是揭示现实环境中控制电池性能和老化的新物理特性的重要来源。利用这一潜力需要解决关键挑战——从数据质量和隐私到特征工程、车队分析和物理信息机器学习中新兴方法的集成。本综述调查了大规模车队数据集以及高分辨率车辆和电池级测量数据，并研究了跨越状态估计、故障检测和能量优化的方法。这些发展共同表明了电池研究的范式转变——从被动诊断到主动生命周期管理。最终，这一轨迹导致了一般化的电池基础模型：不断发展的数字双胞胎积极塑造，而不仅仅是预测电池的整个生命周期。

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引用次数: 0

Holistic thermal management and charge stop optimization using model-based fast-charging strategies 采用基于模型的快速充电策略进行整体热管理和充电停止优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-08-26 DOI: 10.1016/j.etran.2025.100457

Kareem Abo Gamra, Igor Zlatković, Maximilian Zähringer, Christian Allgäuer, Markus Lienkamp

The growing need to decarbonize the transport sector can be addressed through wide-scale electrification, which is currently hampered by concerns regarding range anxiety and insufficient charging speeds. Therefore, it is critical to provide methodologies that ensure fast-charging capability regardless of route or ambient conditions. Model-based fast-charging and preconditioning strategies have been shown to offer a robust approach to achieve short charging times without endangering battery safety or longevity. However, they must be scaled to the vehicle application while considering factors such as route infrastructure and energy constraints. In this study, we utilize a dynamic programming approach to optimize a charge stop and preconditioning strategy for long-distance journeys. The methodology is validated by performing long-distance travel experiments on a route of 850 km using a Tesla Model 3 Standard Range, revealing that charging time can be reduced by 24 min while simultaneously consuming less thermal management energy compared to the onboard route planning algorithm. A simulation study with a hypothetical high-power cell using an anode potential control charging protocol to prevent lithium plating shows that the inherent self-heating behavior could be leveraged to achieve a charge time reduction of 50 min compared to the reference, while requiring almost no active preconditioning. Optimizing the vehicle speed between charging stations additionally allows total travel duration and energy consumption to be adjusted based on charging constraints and individual preferences regarding the value of time and energy costs.

交通运输部门日益增长的脱碳需求可以通过大规模电气化来解决，目前电气化受到对里程焦虑和充电速度不足的担忧的阻碍。因此，无论在何种路线或环境条件下，提供确保快速充电能力的方法至关重要。基于模型的快速充电和预处理策略已被证明是一种可靠的方法，可以在不危及电池安全或寿命的情况下实现短充电时间。然而，在考虑路线基础设施和能源限制等因素的同时，它们必须根据车辆应用进行扩展。在这项研究中，我们利用动态规划方法来优化长途旅行的充电停止和预处理策略。通过使用特斯拉Model 3标准续航里程850公里的长途行驶实验，验证了该方法的有效性，结果表明，与车载路线规划算法相比，充电时间可缩短24分钟，同时消耗的热管理能量更少。一项模拟研究表明，在几乎不需要主动预处理的情况下，利用阳极电位控制充电协议来防止镀锂的假设高功率电池，可以利用固有的自热行为，与参考相比，充电时间减少50分钟。此外，优化充电站之间的车速还可以根据充电限制和个人对时间和能源成本价值的偏好来调整总行程时间和能源消耗。

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