Etransportation最新文献_第6页

Digital twins for battery health prognosis: A comprehensive review of recent advances and challenges 用于电池健康预测的数字孪生：最近进展和挑战的综合回顾

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-30 DOI: 10.1016/j.etran.2025.100489

Yujie Wang, Jiayin Xiao, Yin-Yi Soo, Yifan Chen, Zonghai Chen

This review systematically examines the integration of Digital Twin (DT) technology with lithium-ion battery health prognosis systems. As electrification accelerates across multiple domains, accurate prediction of battery health indicators – including State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and fault conditions – becomes increasingly critical for ensuring safety, reliability, and optimal performance. The core contribution of this review lies in proposing a novel four-layer conceptual framework, comprising the Physical, Data & Communication, Virtual Model, and Twin Service layers, as an analytical tool for structuring the field. After establishing the theoretical foundations of DTs and battery aging, we leverage this framework to systematically survey recent advancements in data augmentation, online state estimation, and fault diagnosis. Through this structured analysis, we then identify critical implementation challenges, including performance in extreme degradation phases, battery pack inconsistencies, and operation under complex conditions. We conclude by proposing future research directions focused on enhancing model generalization and creating standardized architectures through the integration of cloud computing and IoT technologies, and applying federated learning to solve potential privacy and security problems. This review serves as a critical reference by providing a structured, application-centric understanding of DTs in battery health management.

本文系统地研究了数字孪生（DT）技术与锂离子电池健康预测系统的集成。随着电气化在多个领域的加速发展，准确预测电池健康指标（包括充电状态（SOC）、健康状态（SOH）、剩余使用寿命（RUL）和故障状况）对于确保安全性、可靠性和最佳性能变得越来越重要。本综述的核心贡献在于提出了一个新的四层概念框架，包括物理层、数据和通信层、虚拟模型层和双服务层，作为构建该领域的分析工具。在建立了dt和电池老化的理论基础之后，我们利用这个框架系统地调查了数据增强、在线状态估计和故障诊断方面的最新进展。通过这种结构化分析，我们确定了关键的实施挑战，包括极端退化阶段的性能、电池组不一致以及复杂条件下的运行。最后，我们提出了未来的研究方向，重点是通过云计算和物联网技术的集成来增强模型泛化和创建标准化架构，并应用联邦学习来解决潜在的隐私和安全问题。这篇综述通过对电池健康管理中的dt提供结构化的、以应用为中心的理解，作为一个重要的参考。

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引用次数: 0

Trace multi-cation high-entropy engineering enables ultra-stable cobalt-free LiNiO2 with >230 mAh/g 痕量多阳离子高熵工程可实现>230 mAh/g的超稳定无钴LiNiO2

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-29 DOI: 10.1016/j.etran.2025.100493

Peng Zhang , Jinquan Liu , Qiqiang Huang , Yang Li , Yi Guo , Zuoguo Xiao , Chenxi Li , Lianghao Wen , Wei Peng , Weijing Yuan , Gaolong Zhu , Liang Yin , Longlong Fan , Lirong Zheng , Jing Zhang , Tiening Tan , Jianfeng Hua , Dongsheng Ren , Languang Lu , Xiang Liu

The cobalt-free LiNiO₂ (LNO) cathode, composed solely of transition metal nickel, stands out as a prime candidate for next-generation commercial cathodes, offering an exceptional theoretical capacity of 275 mAh/g, cost efficiency, and environmental sustainability. Unlike LiNi_xMn_yCo₂O₂ (NMC) counterparts, LiNiO₂ (LNO) cathode is plagued by rapid capacity degradation and safety risks due to absence of Co/Mn, which act as structural stabilizers ('rivets') in transition metal layer. This deficiency induces severe anisotropic lattice distortion and multi-phase transitions during charge/discharge cycles. These distortions are exacerbated at elevated temperatures (>45 °C) and at high de-lithiation state with initial discharge capacities exceeding 230 mAh/g. To mitigate these issues, we introduced a high-entropy engineering approach for LNO, exemplified by LiNi_0.98Mo_0.005Nb_0.005Ti_0.005Mg_0.005O₂ (LNO-2 %HE). In situ XRD, synchrotron XAS and ex situ analyses reveal that the compositional complexity of LNO-2 %HE enhances structural disorder and amorphous character, which suppresses high-voltage phase transition. This design achieves 96.1 % capacity retention over 100 cycles at 25 °C and 97.5 % retention after 50 cycles at 45 °C, alongside an initial discharge capacity of 238 mAh/g at 0.1C. Furthermore, improved lattice oxygen stability in LNO-2 %HE inhibits oxygen release during thermal phase transitions, significantly enhancing safety. This strategy advances the viability of LNO cathode for high-energy-density batteries.

无钴LiNiO2 （LNO）阴极仅由过渡金属镍组成，具有275 mAh/g的理论容量、成本效益和环境可持续性，是下一代商用阴极的主要候选者。与LiNixMnyCo2O2 （NMC）不同，LiNiO2 （LNO）阴极由于缺乏Co/Mn而受到容量快速下降和安全风险的困扰，Co/Mn在过渡金属层中充当结构稳定剂（“铆钉”）。这一缺陷在充放电循环中引起严重的各向异性晶格畸变和多相转变。在高温（45°C）和高去锂化状态下（初始放电容量超过230 mAh/g），这些扭曲会加剧。为了缓解这些问题，我们引入了LNO的高熵工程方法，例如lini0.98 mo0.005 nb0.005 ti0.005 mg0.0050 o2 （LNO-2 %HE）。原位XRD、同步XAS和非原位分析表明，lno - 2% HE的成分复杂性增强了结构无序性和非晶态特性，抑制了高压相变。该设计在25°C条件下100次循环的容量保持率为96.1%，在45°C条件下50次循环的容量保持率为97.5%，在0.1C条件下的初始放电容量为238 mAh/g。此外，lno - 2% HE中晶格氧稳定性的提高抑制了热相变过程中的氧释放，显著提高了安全性。这一策略提高了LNO阴极用于高能量密度电池的可行性。

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引用次数: 0

The role of resource sustainability for lithium-ion batteries -A review of existing carbon emission reduction perspectives 锂离子电池资源可持续性的作用——现有碳减排观点综述

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-29 DOI: 10.1016/j.etran.2025.100491

Wenfang Gao , Xianju Zeng , Weiguang Lv , Zhengqing Ye , Bingxin Zhou , Guangming Zhang , Zhijun Ren , Zhiyuan Feng , Wei Jin , Zhi Sun

The resource recycling of lithium-ion batteries (LIBs) can significantly reduce the carbon emission, which has received unprecedented attention from both the academic and industrial communities. However, the large consumption of valuable materials (e.g., Li, Co, Ni, Mn, graphite) for LIBs not only intensifies the pressure on global resource supply, but also raises the carbon footprint of the industry. Herein, this review systematically analyses the LIBs industry from the aspects of resource supply and resource cycle, in combination with the carbon emission reduction analyzation of LIBs industry. By analyzing the development status of LIBs materials, the resource composition and critical metals are clearly clarified. With LIBs demand increasing, the resource criticality, primary and secondary resource supply are deeply evaluated where Li and Co supply face enormous challenges. The recycling of spent LIBs gives an effective way for resource utilization and circulation. The carbon emission intensity of the whole LIBs industrial chain is discussed from the perspective of resource supply, utilization, and balance, where the carbon emission reduction mainly relies on the use of low-carbon energy and the recycling/reproduction processes. This critical review revealed that resource sustainability and carbon neutralization are an inseparable system, and can give guidance to the development of LIBs materials to ensure the sustainable development of resources in the future.

锂离子电池（LIBs）的资源回收利用可以显著降低碳排放，受到了学术界和工业界前所未有的关注。然而，锂离子电池大量消耗有价值的材料（如Li， Co, Ni, Mn，石墨）不仅加剧了全球资源供应的压力，而且还增加了该行业的碳足迹。本文结合碳减排分析，从资源供给和资源循环两个方面对锂离子电池产业进行了系统分析。通过分析lib材料的发展现状，明确了lib材料的资源组成和关键金属。随着锂离子电池需求的增加，对锂和钴供应面临巨大挑战的资源临界性、一次和二次资源供应进行了深入评估。废lib的回收利用为资源利用和循环利用提供了有效途径。从资源供给、利用和平衡的角度探讨整个lib产业链的碳排放强度，其中碳减排主要依靠低碳能源的使用和循环/再生产过程。这一批判性综述揭示了资源可持续性与碳中和是一个不可分割的系统，可以为lib材料的发展提供指导，以确保未来资源的可持续发展。

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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 : 2025-09-27 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

SOC gradient-based passive safety design: a chessboard-inspired structural configuration for mitigating thermal runaway propagation in lithium-ion battery packs 基于SOC梯度的被动安全设计：棋盘式结构配置，可缓解锂离子电池组的热失控传播

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-26 DOI: 10.1016/j.etran.2025.100487

C.X. He , Z.X. Guo , L.M. Pan , P.Z. Lin , J.J. Chen , L. Wei , B.L. Huang , J. Sun , T.S. Zhao

In conventional lithium-ion battery packs, adjacent cells are interconnected through series-parallel arrangements, where thermal runaway in a single cell can trigger cascading thermal propagation, leading to catastrophic thermal events. To address this issue, we propose a novel chessboard-inspired battery pack featuring two interdigitated cell groups with alternating state-of-charge (SOC) distribution. During operation, the sequential discharge of these groups creates spatial SOC differentiation. Each high-SOC cell is strategically surrounded by lower-SOC neighbors that serve as inherent thermal buffers. A comparative safety analysis was conducted between conventional and chessboard-inspired pack configurations using a validated thermal runaway propagation model. Simulation results reveal that only the chessboard architecture successfully inhibits the thermal runaway propagation in the 75 %-SOC battery pack. The 50 %-SOC cells surrounding thermal failed cell unit increase the thermal propagation threshold by 22 °C compared to conventional packs with 75 %-SOC cells. In addition, energy flow analysis indicates that the specialized tab design redirects approximately 10 % of the energy released during thermal runaway to non-adjacent cells. This redistribution further increases the required energy release from the initial thermal runaway cell to trigger propagation. Through the integration of geometric layout and operational strategies, the chessboard-inspired configuration demonstrates strong potential for practical applications in electric vehicles and energy storage systems, offering a promising pathway for advancing passive safety technologies in battery system design.

在传统的锂离子电池组中，相邻的电池通过串并联的方式相互连接，单个电池的热失控会引发级联热传播，导致灾难性的热事件。为了解决这个问题，我们提出了一种新颖的棋盘式电池组，具有两个交错的电池组，具有交替充电状态（SOC）分布。在运行过程中，这些群体的连续放电产生了空间SOC分化。每个高soc电池都被低soc邻居策略性地包围，作为固有的热缓冲。采用经过验证的热失控传播模型，对传统和棋盘式包结构进行了安全性对比分析。仿真结果表明，在75% -SOC电池组中，只有棋盘结构才能成功抑制热失控传播。与采用75% -SOC电池的传统电池组相比，热失效电池单元周围的50% -SOC电池可将热传播阈值提高22°C。此外，能量流分析表明，特殊的标签设计将热失控过程中释放的大约10%的能量重定向到非相邻的电池。这种再分配进一步增加了从初始热失控细胞释放的触发传播所需的能量。通过几何布局和操作策略的整合，棋盘启发的配置显示出在电动汽车和储能系统中实际应用的强大潜力，为推进电池系统设计中的被动安全技术提供了一条有希望的途径。

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引用次数: 0

External pressure effects on thermal runaway in prismatic LiFePO4 batteries: Mechanistic insights for safer battery systems in electric vehicles 外部压力对柱状LiFePO4电池热失控的影响：电动汽车中更安全的电池系统的机理见解

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-25 DOI: 10.1016/j.etran.2025.100488

Haipeng Chen , Yingying Xu , Yaobo Wu , Zongrong Wang , Yuqi Huang

External pressure significantly influences the thermal runaway (TR) behavior of lithium-ion batteries (LIBs). However, the underlying mechanisms by which external pressure affects exothermic reactions, heat transfer, and gas generation during TR remain to be fully clarified. In this study, the mechanistic effects of external pressure on TR in prismatic lithium iron phosphate (LFP) batteries were systematically investigated through thermal analysis, time-resolved gas chromatography, and postmortem material characterization. Results indicate that external pressures of 0.1 and 0.2 MPa enhance interfacial contact within the battery, thereby increasing internal thermal conductivity. This improvement results in a more uniform temperature distribution, which raises the TR initiation temperature and shifts the initial TR location inward from the battery edge toward the center. However, external pressure accelerates thermal runaway propagation (TRP), with propagation speed at 0.2 MPa increasing by approximately 65 % compared to 0 MPa. Moreover, gas evolution analysis reveals a substantial reduction in total gas yield with increasing external pressure, exhibiting decreases of about 28 % at 0.1 MPa and 53 % at 0.2 MPa relative to 0 MPa. This reduction is primarily attributed to earlier safety venting and prolonged electrolyte evaporation periods. Postmortem characterization highlights intensified exothermic reactions under elevated external pressure, reflecting deeper electrode material degradation. These findings highlight the risk-mitigation effect of external pressure, thereby lowering explosion risk despite the acceleration of TRP, and inform the design and modeling of safer battery systems under realistic mechanical constraints.

外部压力对锂离子电池热失控（TR）行为有显著影响。然而，外界压力在TR过程中影响放热反应、传热和气体生成的潜在机制仍有待完全阐明。在这项研究中，通过热分析、时间分辨气相色谱和死后材料表征，系统地研究了外部压力对柱状磷酸铁锂（LFP）电池TR的机制影响。结果表明，0.1和0.2 MPa的外部压力增强了电池内部的界面接触，从而提高了内部导热系数。这一改进使得温度分布更加均匀，从而提高了TR起始温度，使初始TR位置从电池边缘向中心内移动。然而，外部压力加速了热失控传播（TRP），与0 MPa相比，0.2 MPa时的传播速度增加了约65%。此外，气体演化分析表明，随着外部压力的增加，总产气量大幅下降，在0.1 MPa时，总产气量相对于0 MPa下降约28%，在0.2 MPa时，总产气量相对于0 MPa下降53%。这种减少主要归因于早期的安全排气和延长的电解质蒸发周期。死后表征强调了在升高的外部压力下加剧的放热反应，反映了更深层次的电极材料降解。这些发现强调了外部压力的风险缓解作用，从而降低了TRP加速时的爆炸风险，并为在现实机械约束下设计和建模更安全的电池系统提供了信息。

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引用次数: 0

Breaking the voltage plateau barrier: Slope-adaptive state-of-charge estimation for LFP batteries with temperature-aware hysteresis modeling 突破电压平台障碍：基于温度感知迟滞模型的LFP电池的斜率自适应充电状态估计

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-23 DOI: 10.1016/j.etran.2025.100473

Lisen Yan , Jun Peng , Heng Li , Zhiwu Huang , Dirk Uwe Sauer , Weihan Li

The open-circuit voltage (OCV) hysteresis effect significantly complicates state-of-charge (SOC) estimation of

{LiFePO}_{4}

batteries. While prior research has focused on major-loop hysteresis between full charge and discharge, accurately modeling minor-loop hysteresis during partial charge/discharge remains a persistent challenge. This paper proposes a data-driven hysteresis model that incorporates historical SOC and temperature data, with which an adaptive SOC estimator is designed to accommodate slope variations in minor-loop hysteresis. The proposed model accurately captures complex voltage hysteresis across different charge/discharge paths and temperature conditions using deep long short-term memory neural networks trained on hysteresis test data. This OCV component is integrated into a second-order equivalent circuit model, achieving both high-precision battery modeling and computational efficiency. The model parameters are optimized effectively using a multistep parameter identification method enhanced by a meta-heuristic algorithm. The proposed SOC estimator dynamically adjusts its covariance matrices in response to voltage slope variations during the plateau, improving Kalman gain matching to eliminate cumulative errors and enhance accuracy. Extensive experimental results show that over 95% of samples achieve a mean absolute error of less than 0.56% across various usage scenarios. The proposed method outperforms two state-of-the-art methods by 46.2% and 45.7% in root mean square error, demonstrating fast convergence and robust estimation even within the voltage plateau.

开路电压（OCV）滞后效应使LiFePO4电池的荷电状态（SOC）估算变得非常复杂。虽然先前的研究主要集中在完全充电和放电之间的主回路滞后，但准确建模部分充电/放电期间的小回路滞后仍然是一个持续的挑战。本文提出了一个数据驱动的滞后模型，该模型结合了历史SOC和温度数据，并设计了一个自适应SOC估计器，以适应小环滞后的斜率变化。该模型利用基于滞后测试数据训练的深度长短期记忆神经网络，准确捕获了不同充放电路径和温度条件下的复杂电压滞后。该OCV组件集成到二阶等效电路模型中，实现了高精度电池建模和计算效率。采用基于元启发式算法的多步参数辨识方法对模型参数进行了有效优化。本文提出的SOC估计器可以根据平台电压斜率的变化动态调整协方差矩阵，改进卡尔曼增益匹配，消除累积误差，提高估计精度。大量的实验结果表明，超过95%的样本在各种使用场景下的平均绝对误差小于0.56%。该方法的均方根误差比两种最先进的方法分别高出46.2%和45.7%，证明了即使在电压平台内也能快速收敛和鲁棒估计。

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引用次数: 0

Bayesian analysis of interpretable aging across thousands of lithium-ion battery cycles 对数千个锂离子电池循环的可解释老化进行贝叶斯分析

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-22 DOI: 10.1016/j.etran.2025.100486

Marc D. Berliner , Minsu Kim , Xiao Cui , Vivek N. Lam , Shakul Pathak , Yunhong Che , Patrick A. Asinger , Martin Z. Bazant , William C. Chueh , Richard D. Braatz

The Doyle–Fuller–Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode’s diffusion coefficients and reaction rate constants of 95 T Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide–graphite (

{LiC}_{6}

–

{SiO}_{x}

) anode. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for a total of 7776 cycles at various discharge C-rates. While one or more anode parameters are uniquely identifiable over every cell’s lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the aging parameters. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions.

Doyle-Fuller-Newman （DFN）模型是锂离子电池的常见机理模型。反应速率常数和扩散系数是直接影响锂离子运动的关键参数，从而为细胞老化提供了解释。本研究研究了95 T Model 3电池在镍钴铝氧化物（NCA）阴极和氧化硅-石墨（LiC6-SiOx）阳极下每个电极的扩散系数和反应速率常数的唯一估计能力。利用马尔科夫链蒙特卡罗（MCMC）估计了在不同放电c率下共7776个循环的四个参数。虽然一个或多个阳极参数在每个电池的使用寿命中都是唯一可识别的，但阴极参数在使用寿命中期到结束时才可识别，这表明阴极的电阻增长是可测量的。关键参数对健康状态（SOH）的贡献用幂律表示。该SOH模型与MCMC结果在每个细胞的整个生命周期内表现出高度一致性。我们的方法表明，可以通过预测衰老参数的轨迹来实现有效的衰老诊断。因此，在DFN上建立更精确的物理模型来扩展我们的分析可能会产生更多可识别的参数，并进一步改进老化预测。

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引用次数: 0

A spatiotemporal clustering method for mobile energy storage routing and vehicle-to-grid 移动储能路径和车到网的时空聚类方法

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-20 DOI: 10.1016/j.etran.2025.100478

Xinjiang Chen , Jiayang Yao , Guannan He

Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases.

移动储能系统由于其灵活的部署，在整合可再生能源和缓解电网拥堵方面已被证明是有效的。然而，在MES路由和车辆到电网应用（如能源套利）中，涉及多个车辆和节点的大规模路由问题包含高维时空决策变量，使得一般商业求解者难以有效解决。为了解决这一挑战，我们开发了一种改进的基于时空网络的模型，该模型使用可行的时空弧线来表示MES在整个调度期间的路由方案。在此基础上，提出了一种基于时空网络聚合-分裂的自适应时空聚类算法来快速求解该模型。在聚集阶段，给定聚类数量的下界，将时空距离密切相关的节点聚为一个代表性节点。在分割阶段，我们设计了一种时空包络方法来识别每个集群中具有潜在套利机会的节点，并将其分类到一个单独的集群中。我们将所提出的模型和算法应用于加州电网MES的能源套利。结果表明，与商业求解器相比，该算法在300多个日常调度案例中，平均时间开销显著降低92.7%，而最优性仅牺牲0.9%。

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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 : 2025-09-19 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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