Etransportation最新文献

{"title":"Unveiling water-thermal transport mechanisms under flight conditions for performance enhancement of a high-power aviation PEMFC stack","authors":"Zixuan He ,&nbsp;Xiao Ma ,&nbsp;Xiaoqing Zhang ,&nbsp;Shijin Shuai","doi":"10.1016/j.etran.2026.100557","DOIUrl":"10.1016/j.etran.2026.100557","url":null,"abstract":"<div><div>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 (<em>CV</em>) 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 <em>CV</em> 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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100557"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time physics-aware battery health monitoring from partial charging profiles via physics-informed neural networks","authors":"Xubo Gu ,&nbsp;Xun Huan ,&nbsp;Yao Ren ,&nbsp;Wenqing Zhou ,&nbsp;Weiran Jiang ,&nbsp;Ziyou Song","doi":"10.1016/j.etran.2026.100555","DOIUrl":"10.1016/j.etran.2026.100555","url":null,"abstract":"<div><div>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 <span><math><mrow><mn>47</mn><mo>×</mo></mrow></math></span> 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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100555"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layered electro-thermal modeling and self-heating optimization for large-capacity Li-ion batteries","authors":"Shenghao Li ,&nbsp;Cheng Lin ,&nbsp;Yu Tian ,&nbsp;Zhenyi Tao ,&nbsp;Peng Xie","doi":"10.1016/j.etran.2026.100544","DOIUrl":"10.1016/j.etran.2026.100544","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100544"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation","authors":"Dongxu Guo ,&nbsp;Tianpeng Lu ,&nbsp;Tao Sun ,&nbsp;Xin Lai ,&nbsp;Xuebing Han ,&nbsp;Yuejiu Zheng","doi":"10.1016/j.etran.2026.100553","DOIUrl":"10.1016/j.etran.2026.100553","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100553"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving and environmental factors affecting lithium-ion battery capacity degradation in micro battery electric vehicles","authors":"Dongmin Kim ,&nbsp;Seungmin Oh ,&nbsp;Eunjeong Ko ,&nbsp;Jisup Shim","doi":"10.1016/j.etran.2026.100565","DOIUrl":"10.1016/j.etran.2026.100565","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100565"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STL-LLM: A seasonal-trend decomposition-enhanced large language model for battery capacity aging trajectory prediction","authors":"Xuan Liu ,&nbsp;Yan Lyu ,&nbsp;Jie Gao ,&nbsp;Cunfu He ,&nbsp;Mengmeng Geng ,&nbsp;Maosong Fan","doi":"10.1016/j.etran.2026.100549","DOIUrl":"10.1016/j.etran.2026.100549","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100549"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint prediction of polarization losses and internal states in fuel cell via time–frequency feature fusion and machine learning","authors":"Jiaping Xie ,&nbsp;Kunyi Feng ,&nbsp;Hao Yuan ,&nbsp;Zhaoming Liu ,&nbsp;Chao Wang ,&nbsp;Wei Tang ,&nbsp;Yabo Wang ,&nbsp;Penglong Bao ,&nbsp;Xuezhe Wei ,&nbsp;Haifeng Dai","doi":"10.1016/j.etran.2026.100548","DOIUrl":"10.1016/j.etran.2026.100548","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100548"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalizable Fourier Neural Operator for estimation of lithium-ion battery temperature distribution","authors":"Dominic Karnehm ,&nbsp;Yusheng Zheng ,&nbsp;Antje Neve ,&nbsp;Remus Teodorescu","doi":"10.1016/j.etran.2026.100596","DOIUrl":"10.1016/j.etran.2026.100596","url":null,"abstract":"<div><div>Accurate and fast estimation, monitoring, and control of battery temperature is critical for modern battery management systems. This paper benchmarks two neural operators for estimating the temperature distribution of cylindrical batteries: the Fourier Neural Operator (FNO) and the Parameter-Embedded FNO (PE-FNO). Parameter embedding enables temperature distribution estimation by leveraging the parameter space of the battery’s density and specific heat capacity, which are included as input parameters. The Channel-Attention Parameter Embedding (CAPE) module embeds Partial Differential Equation (PDE) parameters into the FNO, enabling generalization across the parameter space of defined parameters even after training. From a BMS deployment perspective, this allows a model to be trained once and subsequently adapted to different cell variants by supplying parameter values, without retraining. In a first step, the models are trained on simulated data generated from a one-dimensional electro-thermal coupled model under multiple drive cycles and cooling conditions. To assess transferability under changed thermal parameter settings, transfer learning is performed with varying thermal conductivity and convection coefficients. Furthermore, transfer learning is applied to the experimental data, achieving root-mean-square errors (RMSEs) of 0.09 °C and 0.12 °C at core temperature for FNO and PE-FNO, respectively. Parameter embedding increases error but enables generalization through the PDE parameter density and the specific heat capacity. In terms of RMSE, the two methods yield results at least as good as those of the baseline models. Nevertheless, FNO demonstrates superior performance. The computational time evaluation shows that FNO and PE-FNO run approximately 6 and 5 times faster than a conventional PDE solver, respectively. Overall, the results show that neural operators enable fast, accurate temperature distribution and that, with a slight decrease in accuracy, parameter embedding enables generalization of machine learning models.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100596"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of mass transport in PEM electrolysis cell via Triply Periodic Minimal Surfaces (TPMS) based integrated transport layer","authors":"Dachen Tao ,&nbsp;Yudong Zhang ,&nbsp;Jun Li ,&nbsp;Xun Zhu ,&nbsp;Dingding Ye ,&nbsp;Yang Yang ,&nbsp;Masrur Khodiev ,&nbsp;Qiang Liao","doi":"10.1016/j.etran.2026.100551","DOIUrl":"10.1016/j.etran.2026.100551","url":null,"abstract":"<div><div>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 (&gt;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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100551"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From hype to impact: A roadmap for trustworthy battery AI","authors":"Jingyuan Zhao ,&nbsp;Yunhong Che ,&nbsp;Yuqi Li ,&nbsp;Stephen Harris","doi":"10.1016/j.etran.2026.100546","DOIUrl":"10.1016/j.etran.2026.100546","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100546"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}