Advances in Applied Energy最新文献_第4页

Providing load flexibility by reshaping power profiles of large language model workloads 通过重塑大型语言模型工作负载的功率配置文件来提供负载灵活性

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-07-15 DOI: 10.1016/j.adapen.2025.100232

Yi Wang, Qinglai Guo, Min Chen

The emergence of large language models (LLM) has driven a significant increase of AI workload in data center power demand. Renewable-powered solutions to decarbonizing LLM workload and reducing electricity costs are faced with the combined volatility of stochastic user requests and renewable energy. The key to removing the barriers in sustainable AI development lies in the adjustable capability of LLM power profiles. Therefore, this paper focuses on exploring the potential load flexibility of LLM workload and proposes a coordinated scheduling framework, notably, without computing performance degradation. Driven by the existence of the energy-optimal core frequency for graphics processing units (GPU), the energy-performance decoupling phenomenon is discovered and proved, where collaborative scaling in GPU quantity and frequency can change power but not computing performance. Motivated by this, the framework slows down the fine-tuning cluster and utilizes idle GPU resources from the inference cluster to maintain the computing performance of fine-tuning tasks. Consequently, the power consumption of the total cluster is reduced, which provides a fresh source of load flexibility. Furthermore, the framework employs dynamic frequency scaling to more flexibly modify the power profile of the expanded fine-tuning cluster. The computing performance is particularly guaranteed through temporal coupling constraints. In a simulated study supported by real-world data, the results prove a 6.8% power-saving ability and 11.3% cost-saving gains on average.

大型语言模型（LLM）的出现推动了数据中心电力需求中人工智能工作负载的显著增加。可再生能源解决方案既可以降低LLM工作量，又可以降低电力成本，同时还面临着随机用户需求和可再生能源的综合波动性。消除人工智能可持续发展障碍的关键在于LLM功率配置的可调能力。因此，本文重点探讨LLM工作负载的潜在负载灵活性，并提出一个协调的调度框架，特别是在不降低计算性能的情况下。在图形处理单元（GPU）能量最优核心频率存在的驱动下，发现并证明了能量性能解耦现象，即GPU数量和频率的协同缩放可以改变功耗，但不会改变计算性能。在此驱动下，框架降低了微调集群的速度，并利用推理集群的空闲GPU资源来维持微调任务的计算性能。因此，整个集群的功耗降低了，这为负载灵活性提供了新的来源。此外，该框架还采用了动态频率缩放，可以更灵活地修改扩展后的微调簇的功率分布。通过时间耦合约束特别保证了计算性能。在一个由真实数据支持的模拟研究中，结果证明了平均节省6.8%的功率和11.3%的成本收益。

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引用次数: 0

Transforming building retrofits: Linking energy, equity, and health insights from The World Avatar 改造建筑：从世界化身链接能源，公平和健康见解

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-07-11 DOI: 10.1016/j.adapen.2025.100230

Jiying Chen , Jiaru Bai , Jieyang Xu , Feroz Farazi , Sebastian Mosbach , Jethro Akroyd , Markus Kraft

The upgrading of energy-inefficient buildings is a critical part of the energy transition. Holistic analyses that foster informed and equitable policy interventions require interoperable data. We apply a principled approach that leverages The World Avatar to create a virtual knowledge graph underpinned by machine-understandable data representations. This approach provides a common terminology to integrate heterogeneous data sources to support multi-scale analysis of building energy retrofit options. We consider a case study in the UK based on the holistic analysis of household-level energy performance data, public health statistics and socio-economic metrics across geographic hierarchies. The analysis identifies regions with critical retrofit necessities, revealing disparities between these imperatives and extant policy levers. Granular retrofit targets are proposed to optimise resource allocation to the most vulnerable areas. Bespoke retrofit strategies are developed for 14.4 million households in the UK, providing actionable insights to support the targeted application of ‘fabric-first’ or ‘system-led’ retrofit pathways.

节能建筑的改造是能源转型的重要组成部分。促进知情和公平的政策干预的整体分析需要可互操作的数据。我们采用了一种原则性的方法，利用The World Avatar来创建一个虚拟的知识图谱，该图谱以机器可理解的数据表示为基础。这种方法提供了一个通用的术语来集成异构数据源，以支持建筑能源改造方案的多尺度分析。我们考虑了一个案例研究在英国基于家庭层面的能源绩效数据，公共卫生统计和跨地理层次的社会经济指标的整体分析。该分析确定了具有关键改造需求的地区，揭示了这些需求与现有政策杠杆之间的差异。提出了细化改造目标，以优化资源分配到最脆弱的地区。为英国1440万户家庭开发了定制改造策略，提供了可操作的见解，以支持“织物优先”或“系统主导”改造途径的目标应用。

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引用次数: 0

Net Zero without the gridlock through peer-to-peer coordinated flexibility 通过点对点协调灵活性实现零网络

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-29 DOI: 10.1016/j.adapen.2025.100231

Wei Gan , Yue Zhou , Jianzhong Wu , Philip C. Taylor

In the pursuit of Net Zero, the rapid adoption of electric vehicles, heat pumps, and distributed generation is placing unprecedented pressure on low-voltage electrical distribution networks. Can these networks adapt and evolve without facing gridlock? Our study proposes an innovative peer-to-peer coordinated flexibility strategy that has the potential to significantly transform the landscape. By aggregating individual flexibility through peer-to-peer coordination, this approach enhances local power balance, mitigates gridlock, and safeguards individual benefits. Through a novel large-scale network analysis method based on statistically similar networks, we have quantified the maximal potential of peer-to-peer coordinated flexibility in alleviating gridlock and deferring network expansion. Using real-world UK low-voltage electrical distribution network data and authoritative distributed energy resources roadmaps, our findings reveal that peer-to-peer coordinated flexibility can reduce peak power flows by up to 20 % and enable as much as 91 % of UK residential low-voltage electrical distribution networks to meet peak demand without gridlock by 2050, significantly reducing the need for network expansion. Furthermore, with the adoption of peer-to-peer coordinated flexibility, the network's peak is projected to occur between 2045–2050, postponing it by 8–10 years compared to scenarios without it. These results underscore the critical role of peer-to-peer coordinated flexibility and serve as a benchmark for the co-development of future grids and flexible resources when addressing associated implementation challenges such as technological infrastructure and consumer engagement.

在追求净零排放的过程中，电动汽车、热泵和分布式发电的迅速普及给低压配电网络带来了前所未有的压力。这些网络能够适应和发展而不面临僵局吗？我们的研究提出了一种创新的点对点协调灵活性策略，它有可能显著改变景观。这种方法通过点对点协调聚合个人灵活性，增强了地方权力平衡，缓解了僵局，保障了个人利益。通过一种基于统计相似网络的新型大规模网络分析方法，我们量化了对等协调灵活性在缓解交通拥堵和延缓网络扩张方面的最大潜力。使用真实的英国低压配电网络数据和权威的分布式能源路线图，我们的研究结果显示，点对点协调的灵活性可以减少峰值功率流高达20%，并使多达91%的英国住宅低压配电网络在2050年之前满足峰值需求而不会出现僵局，大大减少了对网络扩展的需求。此外，随着点对点协调灵活性的采用，预计网络峰值将在2045-2050年之间出现，与没有它的情况相比，它将推迟8-10年。这些结果强调了点对点协调灵活性的关键作用，并在解决技术基础设施和消费者参与等相关实施挑战时，作为未来电网和灵活资源共同开发的基准。

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引用次数: 0

Weather conditions severely impact optimal direct air capture siting 天气条件严重影响最佳的直接空气捕获选址

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-13 DOI: 10.1016/j.adapen.2025.100229

Henrik Wenzel , Freia Harzendorf , Kenneth Okosun , Thomas Schöb , Jann Michael Weinand , Detlef Stolten

Direct air capture (DAC) is rapidly gaining attention as a key technological approach to mitigating climate change. While techno-economic assessments increasingly incorporate DAC, they often overlook the influence of weather variability on both energy demand and plant productivity. In this study, we analyze how local weather patterns affect the two most promising DAC approaches: the solid sorbent and the liquid solvent processes. We reveal for a German case study, that the integration of DAC with renewable energy sources necessitates temporal and spatial considerations, as fluctuations in energy supply and demand can significantly impact operational feasibility. We demonstrate energy demand fluctuations of DAC exceeding 100 % over the course of a year and estimate future DAC costs in Germany in a range from 197 €/t_CO2 to 1035 €/t_CO2, depending on the region and technology. These results emphasize the need for detailed, site-specific assessments to ensure future cost-optimal DAC deployment.

直接空气捕获（DAC）作为缓解气候变化的关键技术途径正迅速受到关注。虽然技术经济评估越来越多地纳入DAC，但它们往往忽略了天气变化对能源需求和植物生产力的影响。在这项研究中，我们分析了当地天气模式如何影响两种最有前途的DAC方法：固体吸附剂和液体溶剂工艺。我们对德国的一个案例研究表明，DAC与可再生能源的整合需要考虑时间和空间因素，因为能源供需的波动会对运营可行性产生重大影响。我们证明了在一年的时间里，DAC的能源需求波动超过100%，并估计德国未来的DAC成本在197欧元/吨二氧化碳到1035欧元/吨二氧化碳之间，具体取决于地区和技术。这些结果强调需要进行详细的、具体地点的评估，以确保未来成本最优的DAC部署。

引用次数: 0

Pathways to sustainable fuel design from a probabilistic deep learning perspective 从概率深度学习的角度看可持续燃料设计的途径

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-11 DOI: 10.1016/j.adapen.2025.100226

Rodolfo S.M. Freitas , Zhihao Xing , Fernando A. Rochinha , Roger F. Cracknell , Daniel Mira , Nader Karimi , Xi Jiang

To achieve net zero CO₂ emissions by 2050–2060, decarbonising the hard-to-abate sectors such as long-distance, heavy-duty transport is a top priority worldwide. These sectors are particularly challenging to decarbonise due to the use of high-energy-density liquid fossil fuels. In this context, designing low-carbon alternative fuels compatible with existing engines and fuel infrastructures is essential. This work presents an advanced fuel design framework to develop sustainable fuels that meet the high energy density requirements of heavy-duty vehicles. The fuel design approach is built upon a probabilistic perspective by considering a conditional generative model to predict the physicochemical properties of pure compounds and fuel blends with confidence bounds required for decision-making tasks. The probabilistic model is then integrated into an inverse design framework to design fuels with specific requirements. Finally, the fuel design framework is employed to develop new diesel fuel compositions according to the desired targets: ignition quality (cetane number) and sooting tendency (yielding sooting index). The AI-assisted fuel design approach can potentially lead to sustainable liquid fuels that are fully compatible with the existing utilisation equipment and can satisfy the requirements of different application sectors.

为了在2050-2060年之前实现二氧化碳净零排放，对长途、重型运输等难以减排的行业进行脱碳是全球的首要任务。由于使用高能量密度的液体化石燃料，这些部门在脱碳方面尤其具有挑战性。在这种情况下，设计与现有发动机和燃料基础设施兼容的低碳替代燃料至关重要。这项工作提出了一种先进的燃料设计框架，用于开发满足重型车辆高能量密度要求的可持续燃料。燃料设计方法建立在概率的角度上，通过考虑条件生成模型来预测纯化合物和燃料混合物的物理化学性质，并具有决策任务所需的置信限。然后将概率模型集成到逆设计框架中，以设计具有特定要求的燃料。最后，根据点火质量（十六烷值）和烟尘倾向性（产生烟尘指数）这两个目标，利用燃料设计框架开发新的柴油成分。人工智能辅助燃料设计方法可能会产生与现有利用设备完全兼容的可持续液体燃料，并能满足不同应用领域的要求。

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引用次数: 0

Potential impact of controlled electric car charging and vehicle-to-grid on Germany’s future power system 受控电动汽车充电和汽车到电网对德国未来电力系统的潜在影响

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-11 DOI: 10.1016/j.adapen.2025.100227

Fabio Frank, Till Gnann, Daniel Speth, Bastian Weißenburger, Benjamin Lux

The increasing diffusion of electric vehicles contributes to a growing electricity demand in the coming years. At the same time, this integrates millions of mobile storage units into the electricity system, which has a rising need for flexibility to balance the intermittent generation from photovoltaic systems and wind turbines. To capture the potential of electric cars as a flexibility resource, we simulate 7,000 vehicle driving profiles in an agent-based model, generating load profiles as well as charging power and state-of-charge boundaries for the German car fleet, which serve as restrictions in energy system optimization. In a scenario-based study for Germany in 2030 and 2045, we compare the installed electric capacities in the optimized system, depending on whether electric vehicle charging is uncontrolled, controlled, or bidirectional. Here we show that a bidirectionally charged car fleet has the potential to replace 32 GW (84 %) of stationary battery storage and 31 GW (64 %) of hydrogen-fired peaking power plants, while enabling an additional solar power expansion of 7 GW (2 %) until 2045. Notably, implementing vehicle-to-grid can limit hydrogen-fired electricity generation to winter months and enable a shift toward combined heat and power plants. On the demand side, it can reduce the expansion of electrolyzers by 19 GW (28 %) and power-to-heat capacities by 25 GW (60 %). Overall, the integrated energy system can substantially benefit from the implementation of smart and especially bidirectional charging as it lowers the need for future capacity expansion in the electricity system but also in coupled hydrogen and heat systems.

随着电动汽车的日益普及，未来几年的电力需求将不断增长。同时，这将数以百万计的移动存储单元集成到电力系统中，这对平衡光伏系统和风力涡轮机间歇性发电的灵活性的需求越来越大。为了捕捉电动汽车作为一种灵活性资源的潜力，我们在一个基于代理的模型中模拟了7000辆汽车的驾驶剖面，生成了德国车队的负载剖面、充电功率和充电状态边界，这些都是能源系统优化的限制条件。在2030年和2045年的德国情景研究中，我们比较了优化系统中安装的电力容量，这取决于电动汽车充电是不受控制的，受控的还是双向的。在这里，我们表明，双向充电的汽车车队有潜力取代32吉瓦（84%）的固定电池存储和31吉瓦（64%）的氢燃料调峰发电厂，同时在2045年之前实现7吉瓦（2%）的额外太阳能扩张。值得注意的是，实施车辆到电网可以将氢燃料发电限制在冬季，并实现向热电联产发电厂的转变。在需求方面，它可以减少19吉瓦（28%）的电解槽扩张，减少25吉瓦（60%）的电热容量。总体而言，集成能源系统可以从智能充电，特别是双向充电的实施中受益匪浅，因为它降低了电力系统以及耦合氢和热系统未来容量扩张的需求。

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引用次数: 0

Evaluating coloured thermochromic windows for energy efficiency and visual comfort in buildings 评价建筑中彩色热致变色窗的能源效率和视觉舒适度

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-01 DOI: 10.1016/j.adapen.2025.100225

Dingming Liu, Yupeng Wu

In recent years, significant advancements have been made in thermochromic (TC) window technologies, particularly in vanadium dioxide (VO₂)-based TC glazing. Innovations such as integrating pigments with polyurethane (PU) composite coatings have enabled colour modulation and improved colorimetric properties. However, their effects on building energy performance and indoor luminance environment are both critical for occupant comfort, health, and broader energy efficiency goals have been underexplored. This study evaluates conventional and coloured TC windows (blue, red, and grey), fabricated with one to three VO₂ layers, focusing both on building energy consumption and daylight performance. TC windows were assessed under three window-to-wall ratios of 30%, 60%, and 90% across three climatic conditions: Changsha, Ankara, and New York. Five key criteria were evaluated: energy savings, daylight availability, glare control, daylight uniformity, and colour quality. A multi-objective analysis revealed that the conventional 2-layer TC (TC2), 3-layer TC (TC3), red 3-layer TC (Red-TC3), and grey 2-layer TC (Grey-TC2) consistently outperformed other variants. These windows achieved up to 14% higher annual energy savings and 5–15% greater daylight availability (UDI_300-2000lux) compared to standard double-glazed (DG) windows. The results highlight the strong potential of coloured TC windows as climate-adaptive solutions for reducing building operational energy demand and enhancing indoor environmental quality, contributing to future energy transition and sustainable building practices.

近年来，热致变色（TC）窗户技术取得了重大进展，特别是基于二氧化钒（VO2）的热致变色（TC）玻璃。诸如将颜料与聚氨酯（PU）复合涂料相结合的创新使颜色调制和比色性能得到改善。然而，它们对建筑能源性能和室内照明环境的影响对居住者的舒适、健康和更广泛的能源效率目标都至关重要，但尚未得到充分的探索。本研究评估了传统的和彩色的TC窗（蓝色、红色和灰色），由一到三层VO2制成，重点关注建筑能耗和日光性能。在长沙、安卡拉和纽约三种气候条件下，以30%、60%和90%的窗墙比对TC窗进行了评估。评估了五个关键标准：节能、日光可用性、眩光控制、日光均匀性和色彩质量。多目标分析显示，常规2层TC （TC2）、3层TC （TC3）、红色3层TC （red -TC3）和灰色2层TC （grey -TC2）的表现始终优于其他变体。与标准双层玻璃（DG）窗户相比，这些窗户每年可节省高达14%的能源，并可获得5-15%的日光（UDI300-2000lux）。结果强调了彩色TC窗作为气候适应性解决方案的巨大潜力，可以减少建筑运营能源需求，提高室内环境质量，为未来的能源转型和可持续建筑实践做出贡献。

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引用次数: 0

Enhancing building energy efficiency with thermal mass optimization 通过热质量优化提高建筑能效

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-05-18 DOI: 10.1016/j.adapen.2025.100224

Yichen Han, Zhengyu He, Shuangdui Wu, Yuqiu Liu, Yingkai Lian, Chaohong Wang, Jiajia Feng, Zhengnan Zhou

High-density urban buildings contain substantial thermal mass, storing significant energy and offering notable potential for heating energy savings. However, effectively harnessing this energy remains challenging due to the spatiotemporal variability of heat storage–release behavior in building components, which often misaligns with building operational demands. This study reveals that thermal mass tends to store heat when it is not needed and release it when buildings do not require it, especially in cities where some buildings are only occupied during the day and others at night. To address these challenges, this study proposes a novel thermal mass arrangement strategy, derived from extensive real-world data analysis. Significant variations in component thermal behavior across different operational schedules were first identified from data collected in 76 rooms. Subsequently, key factors influencing these variations were pinpointed using stepwise linear regression, informing optimization strategies developed through simulations. These strategies were then validated in cold regions using conduction transfer function models (error margin of 3.6 %), which confirmed their year-round effectiveness for both individual buildings with distinct occupancy patterns and groups of buildings. The results demonstrate that optimizing thermal mass arrangements tailored to specific building schedules can significantly enhance energy efficiency. Contrary to prior research advocating for the sole increase in thermal mass, this study indicates that without strategic guidelines, such measures may exacerbate thermal utilization inefficiencies, complementing existing research on thermal storage materials in buildings. Reducing excess heat storage is shown to be beneficial for daytime-use buildings, while nighttime-use buildings benefit from storing heat for evening use. Adjusting the quantity and orientation of thermal mass, alongside optimizing operational schedules, achieves 4–12 % energy savings, with greater benefits in high-solar-radiation areas.

高密度的城市建筑包含大量的热质量，储存了大量的能量，并提供了显著的供暖节能潜力。然而，由于建筑构件的储热-释放行为的时空变异性，有效利用这种能量仍然具有挑战性，这往往与建筑的运行需求不一致。这项研究表明，热质量倾向于在不需要时储存热量，并在建筑物不需要时释放热量，特别是在一些建筑物只在白天使用而其他建筑物只在夜间使用的城市中。为了应对这些挑战，本研究提出了一种新的热质量安排策略，该策略来源于广泛的现实世界数据分析。首先从76个房间收集的数据中确定了不同运行计划下组件热行为的显著变化。随后，使用逐步线性回归确定影响这些变化的关键因素，并通过模拟制定优化策略。然后使用传导传递函数模型（误差范围为3.6%）在寒冷地区验证了这些策略，这证实了它们对具有不同使用模式的单个建筑物和建筑群的全年有效性。结果表明，根据特定的建筑时间表优化热质量安排可以显著提高能源效率。与先前的研究相反，该研究表明，如果没有战略指导方针，这些措施可能会加剧热利用效率的降低，补充了现有的建筑储热材料研究。减少多余的热量储存被证明对白天使用的建筑物有益，而夜间使用的建筑物则受益于储存热量供晚上使用。调整热质量的数量和方向，同时优化运行时间表，可节省4 - 12%的能源，在高太阳辐射区域效益更大。

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引用次数: 0

Electrifying aviation: Innovations and challenges in airport electrification for sustainable flight 电气化航空：可持续飞行的机场电气化创新与挑战

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-05-16 DOI: 10.1016/j.adapen.2025.100222

Martin Lindberg, Jennifer Leijon

The aviation sector is investigating opportunities to reduce pollution and to limit the dependence on fossil fuels. The design of new electric and hybrid aircraft requires airport developments to meet the need for charging. This review article provides an overview of recent developments and the latest research and innovation on electrification at and around airports. The paper describes technical innovations in electrified aviation, sustainable aviation fuels, and hydrogen, and the infrastructure needed at airports to meet the future electricity demand of electric aircraft charging. This study finds that plug-in charging of future electric aircraft will lead to elevated fluctuations in electric power demand at airports, while battery swapping has a more constant electricity demand. The review reveals a significant interest in energy storage and renewable energy systems to supply electricity and mitigate peak power at airports, suggesting high potential for batteries and solar power. Hydrogen for airport energy storage could support electric aircraft charging and be used as a fuel for hydrogen-powered aircraft. More research is needed regarding the optimal configuration of airport infrastructure to support electric aircraft development.

航空业正在研究减少污染和限制对化石燃料依赖的机会。新型电动和混合动力飞机的设计要求机场发展以满足充电需求。本文综述了机场及周边电气化的最新发展和最新研究与创新。本文描述了电气化航空、可持续航空燃料和氢的技术创新，以及机场所需的基础设施，以满足电动飞机充电的未来电力需求。本研究发现，未来电动飞机的插电式充电将导致机场电力需求波动加剧，而换电池的电力需求则更为恒定。该报告显示，人们对能源储存和可再生能源系统产生了浓厚的兴趣，这些系统可用于供电和缓解机场的峰值电力，这表明电池和太阳能的潜力很大。用于机场储能的氢气可以支持电动飞机充电，并用作氢动力飞机的燃料。为了支持电动飞机的发展，需要对机场基础设施的优化配置进行更多的研究。

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引用次数: 0

Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries 锂离子电池电化学-热模型无创参数化的全局敏感性分析

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-05-15 DOI: 10.1016/j.adapen.2025.100221

Jue Chen , Sven Patrick Mattus , Wenjiong Cao , Dirk Uwe Sauer , Weihan Li

High-fidelity electrochemical-thermal models are essential for performance improvement, charge/discharge strategy optimization, and the safe operation of lithium-ion batteries. However, model performance significantly relies on the accuracy of parameters, whose measurement is limited by laboratory conditions. Non-invasive methods based on relatively accessible current, voltage, and temperature data combined with artificial intelligence are promising for rapid parameterization of battery models. However, the model’s complexity and the data’s poor quality increase the difficulty of applying the methodology. To design a reasonable identification framework and obtain reliable data, the identifiability of model parameters must be analyzed under different operating conditions. This paper develops an identifiability analysis framework to investigate the impact of model parameters on voltage and temperature outputs and the impact of key operating variables, i.e., current rate and ambient temperature. By adjusting operating conditions, the sensitivity of specific parameters can be improved by two orders of magnitude. The results are discussed in detail concerning the model modeling mechanism and the physical meaning of the parameters, with a focus on improving non-invasive parameterization in terms of experimental design and identification strategy.

高保真的电化学-热模型对于提高锂离子电池的性能、优化充放电策略和安全运行至关重要。然而，模型的性能很大程度上依赖于参数的准确性，而参数的测量受到实验室条件的限制。基于相对容易获取的电流、电压和温度数据，结合人工智能的非侵入性方法有望实现电池模型的快速参数化。然而，模型的复杂性和数据的低质量增加了该方法应用的难度。为了设计合理的识别框架，获得可靠的数据，必须对不同工况下模型参数的可识别性进行分析。本文开发了一个可识别性分析框架，以研究模型参数对电压和温度输出的影响以及关键操作变量（即电流速率和环境温度）的影响。通过调整操作条件，特定参数的灵敏度可提高两个数量级。详细讨论了模型的建模机制和参数的物理意义，重点从实验设计和识别策略方面改进了无创参数化。

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引用次数: 0