Etransportation最新文献

Unveiling water-thermal transport mechanisms under flight conditions for performance enhancement of a high-power aviation PEMFC stack 揭示飞行条件下的水热输运机制，以提高高功率航空PEMFC堆栈的性能

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.etran.2026.100557

Zixuan He , Xiao Ma , Xiaoqing Zhang , Shijin Shuai

Proton exchange membrane fuel cells (PEMFCs) offer a promising pathway to decarbonize regional aviation. However, the internal heat and mass transport mechanisms high-power PEMFC stacks under flight conditions remain insufficiently studied. To address this, this paper develops a novel multi-methodological framework that integrates a flow network model (FNM) of a shared-manifold configuration parameterized by CFD analysis of a novel large-scale modular flow field, a 1D PEMFC multi-physics model resolving core electrochemical phenomena, and key balance-of-plant (BoP) subsystems. This integrated approach establishes a scalable, minute-scale, physics-based modeling framework for 400-kW class stack performance prediction, calibrated against multi-scale experimental data and capable of capturing water-thermal-gas distributions from stack to individual cells. A multi-objective optimization using the NSGA-II algorithm is then applied to a specific flight mission to enhance operational uniformity and reduce hydrogen consumption. The results reveal that altitude-induced performance degradation above 4000 m is primarily driven by severe reactant maldistribution, leading to a 50 mV voltage loss increase and a tripling of the voltage deviation rate (CV) at 8000 m. As transitioning from a challenging water-thermal condition and maldistributed gas distribution state at take-off to a stable state at cruise, the high-load state result in an ohmic loss that is nearly double that of the cruise phase. Optimization significantly improves stack performance, achieving 13.2 % reduction in CV and 26.9 % and 17.2 % increases in oxygen and hydrogen concentrations at the catalytic layers during take-off phase. System-level analysis confirms hydrogen savings of 0.727 g/s per stack during cruise, resulting in a total 1569.7 L reduction in storage volume per 2-h flight for a 72-seat regional aircraft. This study establishes a high-fidelity, multi-scale modeling and optimization platform that bridges cell-to-stack level water-thermal transport mechanisms with system level design, providing critical insights and tools for developing next-generation aviation fuel cell systems.

质子交换膜燃料电池（pemfc）为区域航空脱碳提供了一条很有前景的途径。然而，高功率PEMFC堆在飞行条件下的内部热量和质量输运机制的研究还不够充分。为了解决这个问题，本文开发了一个新的多方法框架，该框架集成了一个共享流形配置的流动网络模型（FNM），该模型通过对新型大型模块化流场的CFD分析参数化，一个解决核心电化学现象的1D PEMFC多物理模型，以及关键的工厂平衡（BoP）子系统。这种集成方法建立了一个可扩展的、分钟尺度的、基于物理的建模框架，用于400千瓦级的堆性能预测，根据多尺度实验数据进行校准，能够捕获从堆到单个单元的水-热-气分布。然后将NSGA-II算法应用于特定飞行任务的多目标优化，以提高操作均匀性并降低氢消耗。结果表明，海拔高度导致的4000 m以上的性能下降主要是由严重的反应物分布不均匀驱动的，导致电压损失增加50 mV，电压偏差率（CV）增加三倍。当从起飞时具有挑战性的水热条件和不均匀的气体分布状态过渡到巡航时的稳定状态时，高负荷状态导致的欧姆损失几乎是巡航阶段的两倍。优化后的堆性能显著提高，在起飞阶段，催化层的CV降低13.2%，氧和氢浓度分别提高26.9%和17.2%。系统级分析证实，巡航期间每堆叠可节省0.727 g/s的氢气，对于一架72座支线飞机来说，每2小时飞行可减少1569.7 L的储存量。本研究建立了一个高保真、多尺度的建模和优化平台，将细胞到堆栈水平的水热传输机制与系统级设计联系起来，为开发下一代航空燃料电池系统提供关键的见解和工具。

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引用次数: 0

Real-time physics-aware battery health monitoring from partial charging profiles via physics-informed neural networks 通过物理信息神经网络从部分充电配置文件进行实时物理感知电池健康监测

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-31 DOI: 10.1016/j.etran.2026.100555

Xubo Gu , Xun Huan , Yao Ren , Wenqing Zhou , Weiran Jiang , Ziyou Song

Monitoring battery health is essential for ensuring safe and efficient operation. However, there is an inherent trade-off between assessment speed and diagnostic depth—specifically, between rapid overall health estimation and precise identification of internal degradation states. Capturing detailed internal battery information efficiently remains a major challenge, yet such insights are key to understanding the various degradation mechanisms. To address this, we develop a parameterized physics-informed neural network (P-PINNSPM) over the key aging-related parameter space for a single particle model. The model can accurately predict internal battery variables across the parameter space and identifies internal parameters in about 30 seconds—achieving a

47 \times

speedup over the finite volume method—while maintaining high accuracy. These parameters improve the battery state-of-health (SOH) estimation accuracy by at least 60.61%, compared to models without parameter incorporation. Moreover, they enable extrapolation to unseen SOH levels and support robust estimation across diverse charging profiles and operating conditions. Our results demonstrate the strong potential of physics-informed machine learning to advance real-time, data-efficient, and physics-aware battery management systems.

监测电池健康状况对于确保安全高效运行至关重要。然而，在评估速度和诊断深度之间存在固有的权衡，特别是在快速整体健康估计和精确识别内部退化状态之间。有效地获取详细的电池内部信息仍然是一个重大挑战，但这些信息是理解各种退化机制的关键。为了解决这个问题，我们在单个粒子模型的关键老化相关参数空间上开发了参数化物理信息神经网络（P-PINNSPM）。该模型可以准确地预测整个参数空间的内部电池变量，并在大约30秒内识别内部参数，在保持高精度的同时，比有限体积方法实现了47倍的加速。与未纳入参数的模型相比，这些参数将电池健康状态（SOH）估计精度提高了至少60.61%。此外，它们可以外推到看不见的SOH水平，并支持跨不同充电模式和操作条件的可靠估计。我们的研究结果证明了物理信息机器学习在推进实时、数据高效和物理感知电池管理系统方面的强大潜力。

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引用次数: 0

Layered electro-thermal modeling and self-heating optimization for large-capacity Li-ion batteries 大容量锂离子电池分层电热建模及自热优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-17 DOI: 10.1016/j.etran.2026.100544

Shenghao Li , Cheng Lin , Yu Tian , Zhenyi Tao , Peng Xie

Integrated internal/external heating at low temperatures is an important approach to improving the environmental adaptability of lithium-ion batteries. However, for large-capacity batteries, it faces the problem of temperature non-uniformity caused by inhomogeneous heat production and slow heat diffusion. Due to the lack of effective modeling of internal non-uniformity, the impact of temperature gradients during heating on battery degradation remains unclear, and there is a lack of theoretical constraints on temperature non-uniformity. In this study, a layered one-dimensional electro-thermal coupled model with 6 sections is proposed to analyze electro-thermal non-uniformity during battery heating, followed by experimental validation. Based on the model, a multi-stage variable duty cycle heating strategy is obtained through multi-objective optimization and constraints considering aging. Subsequently, the characteristics of internal non-uniformity are further analyzed to reveal the theoretically based control patterns of temperature non-uniformity. The results show that under various operating conditions, the relative error of the model is less than 5 %, and the calculation time for a single heating is less than 10 s. The proposed strategy can increase the heating rate by up to 12.5 % without increasing degradation. It is found that a control strategy with dynamically increasing heating power can ensure rapid heating while improving electro-thermal uniformity and reducing battery degradation. This work solves a critical challenge for electric vehicles, enabling rapid cold-start without accelerating degradation in large-format power batteries. The proposed model and method have broad applicability in the field of battery thermal management.

低温内外一体化加热是提高锂离子电池环境适应性的重要途径。但对于大容量电池来说，由于产热不均匀、热扩散缓慢，存在温度不均匀的问题。由于缺乏对内部不均匀性的有效建模，加热过程中温度梯度对电池退化的影响尚不清楚，并且缺乏对温度不均匀性的理论约束。本研究提出了一种分层的6段一维电热耦合模型来分析电池加热过程中的电热不均匀性，并进行了实验验证。在此基础上，通过多目标优化和考虑老化约束，得到了多阶段变占空比加热策略。随后，进一步分析了内部不均匀性的特性，揭示了基于理论的温度不均匀性控制模式。结果表明，在各种工况下，该模型的相对误差小于5%，单次加热的计算时间小于10 s。所提出的策略可以在不增加降解的情况下将加热速率提高12.5%。研究发现，采用动态增加加热功率的控制策略，既能保证快速加热，又能提高电热均匀性，减少电池退化。这项工作解决了电动汽车的一个关键挑战，实现了快速冷启动，而不会加速大型动力电池的退化。该模型和方法在电池热管理领域具有广泛的适用性。

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引用次数: 0

Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation 促进电池质量分类：使用序列采样数据增强的早期寿命预测

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-27 DOI: 10.1016/j.etran.2026.100553

Dongxu Guo , Tianpeng Lu , Tao Sun , Xin Lai , Xuebing Han , Yuejiu Zheng

With the rapid development of electric transportation systems, early-stage quality classification of lithium-ion batteries (LIBs) is crucial for improving the overall performance of battery systems throughout their life-cycle. However, the complex degradation mechanisms of LIBs lead to significant differences in the aging rates of individual cells under identical conditions, which directly affects the accuracy of early-stage quality classification. To address this challenge, this paper proposes a novel framework for predicting the full life-cycle end of life (EOL) of LIBs, combining a sequence sampling-based virtual battery construction scheme with semi-supervised learning. The framework achieves high-precision EOL prediction by augmenting early-cycle data and leveraging the automated feature extraction capabilities of a masked autoencoder (MAE), using only minimal labeled data. Experimental validation demonstrates that the mean absolute percentage error (MAPE) on the validation set can be reduced to 2.6%. This research not only provides a new approach for early-stage battery quality classification utilizing minimal labeled data but also offers robust support for enhancing pack efficiency and enabling pre-screening of abnormal cells, through efficient data utilization and precise predictive capabilities.

随着电动交通系统的快速发展，锂离子电池的早期质量分级对于提高电池系统全生命周期的整体性能至关重要。然而，由于LIBs复杂的降解机制，导致相同条件下单个细胞的衰老速率存在显著差异，这直接影响了早期质量分类的准确性。为了解决这一挑战，本文提出了一个新的框架来预测lib的全生命周期寿命结束（EOL），将基于序列采样的虚拟电池构建方案与半监督学习相结合。该框架通过增加早期周期数据和利用掩码自动编码器（MAE）的自动特征提取功能，仅使用最小的标记数据，实现高精度的EOL预测。实验验证表明，该方法可以将验证集的平均绝对百分比误差（MAPE）降低到2.6%。这项研究不仅为早期电池质量分类提供了新的方法，利用最小的标记数据，而且通过有效的数据利用和精确的预测能力，为提高电池组效率和实现异常电池的预筛选提供了强有力的支持。

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引用次数: 0

Driving and environmental factors affecting lithium-ion battery capacity degradation in micro battery electric vehicles 影响微电池电动汽车锂离子电池容量退化的驱动和环境因素

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-02-02 DOI: 10.1016/j.etran.2026.100565

Dongmin Kim , Seungmin Oh , Eunjeong Ko , Jisup Shim

This study empirically investigates vehicle-level parameters influencing battery capacity degradation in Micro Battery Electric Vehicles (MBEVs). Real-world driving and recharging data were collected from multiple MBEVs and passenger BEVs for comparison, integrated with meteorological, geographical, and road datasets to examine environmental impacts. A Constant-Current Charging Time-based regression model was developed to quantify degradation, demonstrating high reliability (R² = 0.967) across 68 BEVs. Using this model, degraded and non-degraded BEVs were classified, and statistical analyses revealed that MBEVs are more susceptible to capacity degradation caused by external environmental conditions and driving dynamics than PBEVs. Further, degraded MBEVs operated under higher-speed, topographically variable conditions with frequent acceleration and deceleration, increasing power demand and energy throughput. We identified that these behaviors accelerate capacity degradation compared to non-degraded MBEVs. This research demonstrates that vehicle-level operational data can effectively indicate long-term battery health in real-world MBEV fleets, supporting data-driven diagnostics and lifecycle management strategies for MBEVs.

本文对影响微电池电动汽车（mbev）电池容量退化的车用参数进行了实证研究。从多辆mbev和客运bev中收集真实驾驶和充电数据进行比较，并结合气象、地理和道路数据集来检查环境影响。基于恒流充电时间的退化回归模型对68辆纯电动汽车进行了量化，结果表明该模型具有较高的可靠性（R2 = 0.967）。利用该模型对退化和未退化的纯电动汽车进行了分类，统计分析表明，与纯电动汽车相比，mbev更容易受到外部环境条件和驾驶动力学引起的容量退化。此外，退化的mbev在高速、地形多变的条件下运行，频繁加速和减速，增加了电力需求和能量吞吐量。我们发现，与未退化的mbev相比，这些行为加速了容量退化。该研究表明，车辆级运行数据可以有效地显示实际MBEV车队的长期电池健康状况，支持数据驱动的MBEV诊断和生命周期管理策略。

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引用次数: 0

Joint prediction of polarization losses and internal states in fuel cell via time–frequency feature fusion and machine learning 基于时频特征融合和机器学习的燃料电池极化损耗和内部状态联合预测

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-16 DOI: 10.1016/j.etran.2026.100548

Jiaping Xie , Kunyi Feng , Hao Yuan , Zhaoming Liu , Chao Wang , Wei Tang , Yabo Wang , Penglong Bao , Xuezhe Wei , Haifeng Dai

The real-time decoupling of polarization losses and internal states is fundamental for extending the lifespan of proton exchange membrane fuel cells (PEMFCs), yet existing methods struggle with the trade-off between measurement speed and information depth. This study proposes a novel synergistic time–frequency fusion framework for the joint prediction of polarization losses and internal state distributions. By leveraging a two-dimensional multi-scale agglomerate model, we construct a high-fidelity dataset that captures the intricate mapping between frequency-domain signatures and microscopic reaction distributions. A comprehensive sensitivity analysis identifies impedance amplitude and phase angle at 79.43 Hz and 10 Hz as optimal features, capturing critical information about reaction interfaces and mass transport that are often neglected in traditional time-domain analysis. These identified features, integrated with macro-level operating conditions, are fed into a Gaussian Process Regression (GPR) model. Results demonstrate superior predictive accuracy with a Mean Absolute Percentage Error (MAPE) below 4% for all key variables. Furthermore, the model exhibits exceptional robustness under 30 dB noise levels and dynamic New European Driving Cycle (NEDC) conditions, successfully tracking transient concentration fluctuations. This work offers a highly efficient and cost-effective approach for online health management by extracting physical insight from less on-board measurement information.

极化损失和内部状态的实时解耦是延长质子交换膜燃料电池（pemfc）寿命的基础，但现有的方法在测量速度和信息深度之间进行权衡。本研究提出了一种新的时频协同融合框架，用于联合预测极化损失和内态分布。通过利用二维多尺度凝聚体模型，我们构建了一个高保真的数据集，该数据集捕获了频域特征和微观反应分布之间的复杂映射。综合灵敏度分析确定了79.43 Hz和10 Hz的阻抗幅值和相位角为最佳特征，捕获了传统时域分析中经常忽略的反应界面和质量输运的关键信息。这些识别的特征，与宏观层面的操作条件相结合，被输入到高斯过程回归（GPR）模型中。结果表明，所有关键变量的平均绝对百分比误差（MAPE）低于4%，具有优越的预测准确性。此外，该模型在30 dB噪声水平和动态新欧洲驾驶循环（NEDC）条件下表现出出色的鲁棒性，成功跟踪瞬态浓度波动。这项工作通过从较少的机载测量信息中提取物理洞察，为在线健康管理提供了一种高效且具有成本效益的方法。

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引用次数: 0

STL-LLM: A seasonal-trend decomposition-enhanced large language model for battery capacity aging trajectory prediction 基于季节趋势分解的电池容量老化轨迹预测大语言模型

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-19 DOI: 10.1016/j.etran.2026.100549

Xuan Liu , Yan Lyu , Jie Gao , Cunfu He , Mengmeng Geng , Maosong Fan

Reliable monitoring of lithium-ion battery health is critical for electric vehicles and energy storage systems. Accurate prediction of the remaining capacity aging trajectory remains essential for battery management, yet current machine learning approaches often fail to capture long-term temporal dependencies in degradation data or leverage heterogeneous datasets effectively. In particular, while Pre-trained Large Language Models (LLMs) exhibit powerful reasoning abilities, their application to time-series-based capacity aging trajectory prediction is hindered by a fundamental modality mismatch. To address this, we propose STL-LLM, a novel framework integrating Seasonal-Trend decomposition using LOESS (STL) with frozen LLMs. STL-LLM disentangles battery health sequences into seasonal and trend components, reprograms these temporal features into text-aligned prompts, and employs prefix-based prompting to enhance temporal reasoning. The LLM's output is projected to generate a capacity aging trajectory prediction. Evaluations demonstrate STL-LLM's state-of-the-art accuracy across three public battery datasets, with consistent superiority in ablation and sensitivity studies. From a methodological perspective, STL-LLM offers a principled cross-modal representation learning solution for time-series forecasting, enabling frozen LLM deployment in non-text domains with minimal tuning. Practically, the framework provides a scalable and generalizable approach for battery prognostics, with potential applications in predictive maintenance and cloud-based battery management systems. More broadly, this work bridges the modality gap between structured time-series signals and pre-trained language models. It introduces a transferable paradigm for leveraging LLMs, which holds significant potential for advancing scientific time-series analysis and sequence modeling. While the direct application lies in battery health monitoring for new energy vehicles, this framework creates a pathway for broader impacts across energy systems.

对锂离子电池健康状况的可靠监测对电动汽车和储能系统至关重要。准确预测剩余容量老化轨迹对于电池管理至关重要，但目前的机器学习方法往往无法捕获退化数据中的长期时间依赖性或有效利用异构数据集。特别是，虽然预训练的大型语言模型（llm）具有强大的推理能力，但它们在基于时间序列的容量老化轨迹预测中的应用受到基本模态不匹配的阻碍。为了解决这个问题，我们提出了STL- llm，这是一个利用黄土（STL）和冷冻llm结合季节趋势分解的新框架。STL-LLM将电池健康序列分解为季节和趋势组件，将这些时间特征重新编程为与文本对齐的提示，并使用基于前缀的提示来增强时间推理。预计LLM的输出将生成产能老化轨迹预测。评估表明，STL-LLM在三个公共电池数据集上具有最先进的准确性，在烧蚀和灵敏度研究中具有一贯的优势。从方法学的角度来看，STL-LLM为时间序列预测提供了原则性的跨模态表示学习解决方案，使LLM能够以最小的调优在非文本域中进行冻结部署。实际上，该框架为电池预测提供了一种可扩展和通用的方法，在预测性维护和基于云的电池管理系统中具有潜在的应用前景。更广泛地说，这项工作弥合了结构化时间序列信号和预训练语言模型之间的模态差距。它为利用llm引入了一个可转移的范例，这对于推进科学的时间序列分析和序列建模具有重要的潜力。虽然直接应用于新能源汽车的电池健康监测，但该框架为整个能源系统的更广泛影响创造了途径。

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引用次数: 0

Optimization of mass transport in PEM electrolysis cell via Triply Periodic Minimal Surfaces (TPMS) based integrated transport layer 基于三周期最小表面（TPMS）的集成传输层优化PEM电解池的质量传输

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-23 DOI: 10.1016/j.etran.2026.100551

Dachen Tao , Yudong Zhang , Jun Li , Xun Zhu , Dingding Ye , Yang Yang , Masrur Khodiev , Qiang Liao

The decarbonization of heavy-duty transport depends critically on affordable green hydrogen, with proton exchange membrane electrolysis cell (PEMEC) serving as a key green-hydrogen production technology due to its high efficiency and dynamic response to renewable power. However, severe mass transfer limitations at the anode—primarily caused by oxygen bubble accumulation—restrict PEMEC performance at high current densities (>2 A cm⁻²), thereby elevating hydrogen production cost and hindering its competitiveness for mobility applications. In the study, an innovative integrated transport layer (ITL) is proposed by inspiring from the triply periodic minimal surface (TPMS) structure. The TPMS structure is optimized for mass transfer through gas-liquid two-phase flow simulations. Guided by the results, the TPMS-based flow field is fabricated via 3D printing and evaluated in an electrolyzer. The simulations reveal that the TPMS structure significantly enhances gas-liquid distribution uniformity. Specifically, it increases water saturation at the catalytic layer interface by 110 %, and improves the oxygen distribution uniformity index by 78 % over conventional flow fields. The TPMS flow field reduces the cell voltage by 50 mV at 2 A cm⁻² and decreases mass transfer loss by 44.6 %, compared to conventional serpentine flow fields. This work provides a critical theoretical foundation for designing high-performance mass transport structures in PEMEC.

重型运输的脱碳关键取决于价格合理的绿色氢，而质子交换膜电解电池（PEMEC）因其高效率和对可再生能源的动态响应而成为关键的绿色氢生产技术。然而，阳极处严重的传质限制（主要是由氧泡积累引起的）限制了PEMEC在高电流密度（>2 A cm - 2）下的性能，从而提高了制氢成本，阻碍了其在迁移应用中的竞争力。本文从三周期最小表面（TPMS）结构出发，提出了一种创新的集成传输层（ITL）。通过气液两相流模拟，优化了TPMS结构的传质性能。在实验结果的指导下，利用3D打印技术制作了基于tpms的流场，并在电解槽中进行了评估。仿真结果表明，TPMS结构显著提高了气液分布均匀性。与常规流场相比，催化层界面水饱和度提高了110%，氧分布均匀性指数提高了78%。与传统的蛇形流场相比，TPMS流场在2 A cm−2时可使电池电压降低50 mV，传质损失降低44.6%。这项工作为设计高性能的质量传输结构提供了重要的理论基础。

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引用次数: 0

From hype to impact: A roadmap for trustworthy battery AI 从炒作到影响：值得信赖的电池AI路线图

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-13 DOI: 10.1016/j.etran.2026.100546

Jingyuan Zhao , Yunhong Che , Yuqi Li , Stephen Harris

Artificial intelligence is increasingly used across the battery lifecycle, including materials screening, manufacturing quality control, diagnostics, and second-life assessment, yet its real-world impact remains limited by fragmented data, constrained interpretability, and the absence of deployment-ready governance. This Commentary proposes a roadmap for trustworthy, field-ready battery AI shaped by three structural priorities. First, open and standardized data ecosystems, supported by interoperable metadata and benchmark tasks, are essential for overcoming heterogeneous and siloed datasets. Second, privacy-preserving industrial collaboration can be enabled through federated learning, encrypted inference, synthetic data, and auditable governance frameworks aligned with safety-critical expectations. Third, physically grounded and interpretable models that embed electrochemical priors, enforce physical constraints, and quantify uncertainty are required to ensure robustness across chemistries, formats, and operating regimes. This roadmap reframes battery AI from isolated performance gains toward trustworthy, system-level intelligence capable of delivering sustained scientific and industrial impact.

人工智能在电池生命周期中的应用越来越广泛，包括材料筛选、制造质量控制、诊断和二次使用评估，但其对现实世界的影响仍然受到数据碎片化、可解释性受限以及缺乏部署就绪治理的限制。本评论提出了一个值得信赖的、现场就绪的电池AI路线图，该路线图由三个结构优先事项构成。首先，开放和标准化的数据生态系统，由可互操作的元数据和基准任务支持，对于克服异构和孤立的数据集至关重要。其次，可以通过联邦学习、加密推理、合成数据和符合安全关键期望的可审计治理框架来实现保护隐私的工业协作。第三，物理基础和可解释的模型需要嵌入电化学先验、强制物理约束和量化不确定性，以确保跨化学、格式和操作制度的稳健性。该路线图将电池AI从孤立的性能提升重新定义为可信赖的系统级智能，能够提供持续的科学和工业影响。

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引用次数: 0

Degradation analysis and tanks-in-series modeling of lithium-ion batteries with state of health-adaptive charging strategies 基于健康状态自适应充电策略的锂离子电池劣化分析与罐串联建模

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.etran.2026.100561

Xingjun Li , Dan Yu , Søren Byg Vilsen , Puneet Jindal , Venkat R. Subramanian , Daniel Ioan Stroe

Dynamic operating conditions significantly impact the lifetime of lithium-ion batteries in electric vehicles. While battery lifetime can be extended by optimizing charging profiles to reduce degradation, many existing charging optimization approaches are developed based on fixed, idealized full charge-discharge cycles. These differ from the random and partial charge-discharge behavior in real-world operation and do not consider the influence of battery degradation on charging current profile optimization. To address these limitations, this study designs two groups of battery aging tests to study charging optimization in real-world operation: one subjected to four fixed charging scenarios based on typical daily commuting patterns, and the other to dynamically changing charging scenarios based on state of health change. Capacity degradation, internal resistance increase, and charging time of all cells are analyzed and compared. Degradation modes such as loss of active materials and lithium inventory are examined through incremental capacity and differential voltage analyses. A novel tanks-in-series thermal-aging model is proposed to rapidly simulate battery behavior under dynamic charging, enabling rapid exploration of more charging scenarios constrained by experimental channels or costly to perform. Results demonstrate that dynamically switching charging strategies based on state of health can effectively extend battery lifetime while reducing overall charging time. Moreover, the model proves efficient in identifying optimal charging strategies. These findings offer valuable insights into charging optimization considering practical use scenarios, and present a promising tool for charging optimization.

动态工况对电动汽车锂离子电池的使用寿命影响很大。虽然可以通过优化充电曲线来延长电池寿命，以减少电池劣化，但许多现有的充电优化方法都是基于固定的、理想的完全充放电循环。这与实际操作中的随机和局部充放电行为不同，并且没有考虑电池退化对充电电流曲线优化的影响。针对这些局限性，本研究设计了两组电池老化试验来研究现实运行中的充电优化：一组是基于典型日常通勤模式的四种固定充电场景，另一组是基于健康状态变化的动态变化充电场景。对各电池的容量退化、内阻增大和充电时间进行了分析比较。降解模式，如活性物质的损失和锂库存通过增量容量和差分电压分析检查。提出了一种新型的串联罐热老化模型，用于快速模拟电池在动态充电下的行为，从而能够快速探索受实验通道限制或执行成本高的更多充电场景。结果表明，基于健康状态动态切换充电策略可以有效延长电池寿命，同时缩短整体充电时间。此外，该模型还能有效地识别出最优收费策略。这些发现为考虑实际使用场景的充电优化提供了有价值的见解，并为充电优化提供了一个有前途的工具。

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引用次数: 0