Applied Energy最新文献_第6页

Optimal operation of grid-friendly megawatt-level ultra-fast EV charging stations: A review on constraints, objectives and algorithms for grid-interactive operation 并网友好型兆瓦级超快电动汽车充电站优化运行：并网交互运行约束、目标与算法综述

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-09 DOI: 10.1016/j.apenergy.2025.127202

Jing Li , Xueru Lin , Hao Huang , Rui Wang , Wei Zhong , Xiaojie Lin , Wei Wei

The rapid adoption of electric vehicles is driving an increasing demand for advanced charging infrastructure. High-voltage, megawatt-level ultra-fast charging technologies can significantly reduce charging time, offering a promising solution to alleviate range anxiety. This emerging field benefits from advancements in charger technology, site planning techniques, and energy management strategies. In this review, we comprehensively examine the distinctions between ultra-fast charging stations and conventional charging stations, highlighting the core challenges across grid integration, user experience, economic viability, and environmental sustainability. We explore the constraints, objectives, variables, and algorithms involved in site selection and capacity planning, and then discuss the impact of integrating renewable energy and energy storage, as well as demand uncertainty on system planning. We analyze the operational strategies of charging stations under deterministic, uncertain, and intelligent control paradigms. This review also presents emerging trends in multi-energy integrated charging stations, the integration of artificial intelligence techniques such as spatiotemporal modeling, reinforcement learning, and multi-agent coordination for modeling and control, and potential development roadmaps. This review aims to provide valuable insights to support the sustainable and low-carbon development of future transportation and energy systems.

电动汽车的迅速普及推动了对先进充电基础设施的需求不断增长。高压、兆瓦级的超快充电技术可以显著缩短充电时间，为缓解里程焦虑提供了一个有希望的解决方案。这一新兴领域得益于充电器技术、场地规划技术和能源管理策略的进步。在这篇综述中，我们全面研究了超快速充电站和传统充电站之间的区别，强调了电网整合、用户体验、经济可行性和环境可持续性方面的核心挑战。我们探讨了选址和容量规划中涉及的约束、目标、变量和算法，然后讨论了可再生能源和储能的集成以及需求不确定性对系统规划的影响。本文分析了充电站在确定性、不确定性和智能控制模式下的运行策略。本文还介绍了多能综合充电站的发展趋势、人工智能技术（如时空建模、强化学习、多智能体协调建模和控制）的集成以及潜在的发展路线图。本综述旨在为支持未来交通和能源系统的可持续低碳发展提供有价值的见解。

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引用次数: 0

Adaptive optimization algorithms for scheduling multiple battery energy storage systems in complex grid scenarios 复杂电网场景下多电池储能系统调度的自适应优化算法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-08 DOI: 10.1016/j.apenergy.2025.127149

Fan Ji , Ziyu Zhu , Hongtao Fan , Lei Ma , Xinran Li , Renyun Ji , Zhuying Yu , Shiyi Fu , Yaojie Sun

The rapid proliferation of renewable energy sources has compounded the complexity of power grid management, particularly in scheduling multiple Battery Energy Storage Systems (BESS). Addressing this challenge, we present the Adaptive Optimization Energy Management System (AO-EMS) algorithm that significantly enhances the flexibility and reliability of power system dispatch in complex grid environments. Our algorithm adeptly manages multiple Points of Common Coupling (PoC) and Transformer Nodes (TNs), employing a priority-based capacity control mechanism alongside an integrated State of Charge (SoC) balancing strategy. These features ensure equitable utilization and extended longevity of storage resources. Adjustable priority settings enable precise attainment of control goals within defined constraints, optimizing resource allocation and bolstering system stability. Through theoretical analysis and practical implementation—including scenarios with intricate branch configurations—we demonstrate the algorithm’s effectiveness. Operational data from two real-world test sites and one transient simulation further demonstrate the stability and robustness of our adaptive optimization approach under diverse topologies, renewable variability, and load fluctuations.

可再生能源的快速发展增加了电网管理的复杂性，特别是在调度多个电池储能系统（BESS）时。针对这一挑战，我们提出了自适应优化能源管理系统（AO-EMS）算法，该算法显著提高了复杂电网环境下电力系统调度的灵活性和可靠性。我们的算法熟练地管理多个公共耦合点（PoC）和变压器节点（tn），采用基于优先级的容量控制机制以及集成的充电状态（SoC）平衡策略。这些特性确保了存储资源的合理利用和更长的使用寿命。可调整的优先级设置能够在定义的约束条件下精确实现控制目标，优化资源分配并增强系统稳定性。通过理论分析和实际实现，包括具有复杂分支配置的场景，我们证明了算法的有效性。来自两个实际测试站点和一个瞬态模拟的运行数据进一步证明了我们的自适应优化方法在不同拓扑结构、可再生可变性和负载波动下的稳定性和鲁棒性。

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引用次数: 0

Blockchain-based network-constrained peer-to-peer energy trading in a reconfigurable distribution network 可重构配电网络中基于区块链的网络约束点对点能源交易

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-08 DOI: 10.1016/j.apenergy.2025.127195

Jian Ping , Shiting Kong , Zheng Yan , Xiaoyuan Xu , Sijie Chen

Blockchain has emerged as a promising solution for enhancing transparency and trust in peer-to-peer (P2P) energy trading. Due to the difficulty of handling optimization tasks on blockchain, existing studies usually clear the P2P market on blockchain without considering network constraints, then a central entity adjusts the trading results to guarantee the network security. However, an untrusted entity could manipulate the trading results in such an adjustment, which may undermine optimality and trust. This paper proposes a blockchain-based network-constrained P2P energy trading method for a reconfigurable distribution network. To address the challenges of handling optimization tasks, an on-chain and off-chain collaborative architecture is proposed to solve the network-constrained P2P energy trading model in a blockchain environment. A Proof-of-Mixed-Integer-Programming (PoMIP) consensus algorithm is proposed to ensure the transparency and trust of the P2P trading results. A fault-tolerant feasible region splitting method is embedded into the PoMIP algorithm to further improve the off-chain computational efficiency. Theoretical analysis and simulation results demonstrate the robustness and computational efficiency of the proposed method.

区块链已经成为一个有前途的解决方案，用于提高点对点（P2P）能源交易的透明度和信任。由于区块链上的优化任务处理困难，现有研究通常在区块链上清除P2P市场，不考虑网络约束，然后由一个中心实体对交易结果进行调整，以保证网络安全。然而，在这种调整中，不受信任的实体可能会操纵交易结果，这可能会破坏最优性和信任。针对可重构配电网，提出了一种基于区块链的网络约束P2P能源交易方法。为了解决优化任务处理的挑战，提出了一种链上和链下协同架构来解决区块链环境下网络约束的P2P能源交易模型。为了保证P2P交易结果的透明性和可信性，提出了一种混合整数规划证明（PoMIP）共识算法。在PoMIP算法中嵌入了一种容错可行区域分割方法，进一步提高了离链计算效率。理论分析和仿真结果证明了该方法的鲁棒性和计算效率。

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引用次数: 0

Hydrogen-electricity hybrid-energy system with superconducting-battery energy storage for urban rail transit: design, case study, and techno-economic analysis 城市轨道交通用超导电池储能的氢-电混合能源系统：设计、案例研究和技术经济分析

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-08 DOI: 10.1016/j.apenergy.2025.127198

Lin Fu , Yu Chen , Shijuan Li , Mingshun Zhang , Shan Jiang , Xiaoyuan Chen , Boyang Shen

Urban rail transit networks are huge energy consumers. This paper proposes a novel hydrogen-electricity hybrid-energy system for urban rail transit, with liquid hydrogen and the superconducting magnetic energy storage (SMES) and battery energy storage (BES) hybrid energy storage system (HESS). The study shows that the proposed SMES-BES HESS technology can properly provide energy compensation and conversion for the frequent acceleration/braking of metros. The overall hydrogen-electricity hybrid-energy system for urban rail transit can utilize the surplus renewable energy and energy waste caused by regenerative braking of metros, and produce clean hydrogen for nearby infrastructures. The economic analysis shows that the payback period of the SMES-BES HESS is around 9 years and the payback period of the overall hydrogen-electricity hybrid-energy system for urban rail transit is 14.6 years (both with discount rates). In summary, the novel hydrogen-electricity hybrid-energy system with SMES-BES HESS technology can greatly enhance the energy utilization and coordination of urban rail transit system, and promote the deep integration of railway systems and clean energies.

城市轨道交通是巨大的能源消耗者。本文提出了一种新型的城市轨道交通氢电混合能源系统，该系统采用液态氢和超导磁储能（SMES）和电池储能（BES）混合储能系统（HESS）。研究表明，所提出的SMES-BES HESS技术可以很好地为地铁频繁的加速/制动提供能量补偿和转换。城市轨道交通整体氢电混合能源系统可以利用地铁再生制动产生的剩余可再生能源和能源浪费，为附近的基础设施生产清洁的氢。经济分析表明，中小企业-中小企业HESS的投资回收期为9年左右，城市轨道交通氢电混合能源系统整体投资回收期为14.6年（均含贴现率）。综上所述，采用SMES-BES HESS技术的新型氢电混合能源系统可以极大地提高城市轨道交通系统的能源利用和协调性，促进轨道交通系统与清洁能源的深度融合。

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引用次数: 0

Optimal operation strategy for building users considering asynchronous information release in multi-type demand response markets to mitigate building-grid interaction risks 多类型需求响应市场中考虑异步信息发布的建筑用户最优运行策略以降低建筑-电网交互风险

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-08 DOI: 10.1016/j.apenergy.2025.127213

Cheng Zhen , Zhe Tian , Jide Niu , Yakai Lu , Chuanzhi Liang

Building users, as ideal demand response participants, have been proven to effectively adapt to the time-of-use (TOU) pricing and provide multiple auxiliary services. The asynchronous release of TOU pricing and peak shaving ancillary service information by the grid operator leads to challenges in flexible resource allocation for building users. Research indicates that the lack of a flexibility resource allocation strategy not only significantly reduces the benefits for building users but also increases the risk of flexibility resource shortages in random demand response scenarios. To address this issue, this study explores multi-market demand response strategies for building users. First, a hybrid scenario theoretical optimal dispatch method is proposed to quantify the theoretically optimal economic benefits and load reduction capability when participating in both TOU pricing and peak shaving. Then, considering the unpredictability of peak shaving events, a reserved peak shaving capability optimal dispatch method is developed, which is particularly suitable for real-world market environments. This method introduces a power reserve coefficient to allocate the available capability of stationary energy storage devices across different market scenarios. Lastly, the proposed models are applied to the electricity market in Shenzhen, China. The results indicate that optimizing solely for economic efficiency in a single scenario leads to a theoretical economic loss of 5.6 % and a peak shaving capability loss of nearly 900 kW. The reserved peak shaving capability optimal dispatch method achieves an increase in declared response quantities ranging from 79 kW to 448 kW. The effectiveness of the method is further validated by adjusting the frequency of peak shaving events, achieving a maximum operating cost reduction of 5.66 %. Finds show that the proposed method can enhance the load reduction capability of building users in response to random peak shaving events and improve the economic benefits of hybrid scenarios.

建筑用户作为理想的需求响应参与者，已被证明能够有效地适应分时电价并提供多种辅助服务。电网运营商对分时电价和调峰辅助服务信息的异步发布给建筑用户的灵活资源分配带来了挑战。研究表明，在随机需求响应场景下，缺乏灵活性资源分配策略不仅会大大降低建筑用户的收益，还会增加灵活性资源短缺的风险。为了解决这个问题，本研究探讨了建筑用户的多市场需求响应策略。首先，提出了一种混合情景理论最优调度方法，量化参与分时电价和调峰时的理论最优经济效益和减载能力。然后，考虑到调峰事件的不可预测性，提出了一种特别适用于现实市场环境的预留调峰能力优化调度方法。该方法引入电力储备系数，在不同的市场情景下分配固定储能设备的可用容量。最后，将所提出的模型应用于深圳电力市场。结果表明，在单一情况下，仅针对经济效率进行优化会导致5.6%的理论经济损失和近900 kW的调峰能力损失。保留调峰能力优化调度方法实现了从79千瓦到448千瓦的声明响应量的增加。通过调整调峰事件的频率进一步验证了该方法的有效性，最大限度地降低了5.66%的运行成本。研究结果表明，该方法可以增强建筑用户应对随机调峰事件的减载能力，提高混合场景下的经济效益。

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引用次数: 0

Fuel cell electric long-haul truck evaluation for sustainable transport via a novel Pythagorean fuzzy sets-driven tool 基于新型毕达哥拉斯模糊集驱动工具的燃料电池电动长途卡车可持续运输评价

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-06 DOI: 10.1016/j.apenergy.2025.127175

Ömer Faruk Görçün , Pratibha Rani , Arunodaya Raj Mishra , Fatih Ecer

Fossil fuel-powered trucks and vehicles used in road freight transportation play a notable role in the emission of greenhouse gases. Although the road vehicle industry's use of renewable energy is promising in terms of sustainability, the vehicle manufacturing industry's initiatives are still in their infancy. Moreover, existing studies on using electric and renewable energies in transportation have primarily focused on electric automobiles. Considering these research and practice gaps, this work investigates the selection of the most proper fuel cell electric long-haul trucks (FCETs) to restructure the Turkish fleet of long-haul trucks operating nationwide concerning sustainability. However, assessing these vehicles is challenging, as they are produced based on new and advanced technology, with severe and highly complicated uncertainties. Thus, this paper suggests a Pythagorean fuzzy distance measure-based weighted integrated sum product (WISP) with the integration of the symmetry point of criteria (SPC) and relative closeness coefficient (RCC)-based weighting methods. Surprisingly, and unlike the findings of earlier works, the acquired conclusions indicate that refueling time (0.1161) is the most influential factor for FCET selection, followed by range (0.0837) and torque (0.0785) among the 14 criteria. Besides, the first alternative (R1) outperforms the other options, followed by R5 and R7. Finally, robustness and validity checks ensured the consistency, stability, and practicality of the conclusions. The research can guide manufacturers who produce FCETs and aim to enhance the quality and desirability of their products. Furthermore, practitioners and researchers can utilize the proposed model to solve challenging decision-making problems.

公路货物运输中使用的化石燃料卡车和车辆在温室气体排放中起着显著的作用。虽然道路车辆行业对可再生能源的使用在可持续性方面很有希望，但汽车制造业的举措仍处于起步阶段。此外，现有的关于在交通运输中使用电力和可再生能源的研究主要集中在电动汽车上。考虑到这些研究和实践差距，本工作调查了最合适的燃料电池电动长途卡车（FCETs）的选择，以重组土耳其在全国范围内运营的长途卡车车队。然而，评估这些车辆是具有挑战性的，因为它们是基于新的先进技术生产的，具有严重和高度复杂的不确定性。因此，本文提出了一种基于毕达哥拉斯模糊距离测度的加权积分和积（WISP）方法，该方法结合了准则对称点（SPC）和相对接近系数（RCC）的加权方法。令人惊讶的是，与之前的研究结果不同，所得结论表明，在14个标准中，加油时间（0.1161）是影响fceet选择的最重要因素，其次是范围（0.0837）和扭矩（0.0785）。此外，第一种选择（R1）的性能优于其他选择，其次是R5和R7。最后进行鲁棒性和效度检验，确保结论的一致性、稳定性和实用性。该研究可以指导生产fet的制造商提高其产品的质量和可取性。此外，从业者和研究人员可以利用所提出的模型来解决具有挑战性的决策问题。

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引用次数: 0

Flexible operation of virtual power plant enabled integrated electricity-heating system under multiple uncertainties via distributionally robust model predictive control 通过分布鲁棒模型预测控制，实现了多不确定条件下虚拟电厂综合供热系统的柔性运行

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-06 DOI: 10.1016/j.apenergy.2025.127177

Xiaobin Wang , Qi Li , Junyi Zhai , Yuning Jiang , Sheng Wang , Jianxiao Wang

Virtual power plant (VPP) can incorporate various electric infrastructures, e.g., data centers (DCs) and electric vehicles (EVs), creating multiple uncertainties and challenges for the operation of integrated electricity–heating system (IEHS). This paper focuses on the flexible operation problem of VPP-enabled IEHS under both static and dynamic uncertainties. First, the temporal shifting flexibility of workloads from DCs is modeled. Second, a novel metric-based distributionally robust model predictive control (DRMPC) framework is introduced to address both static uncertainties from renewable energy and dynamic uncertainties from EV charging behaviors. Third, the dynamic uncertainties are reformulated as ambiguity tubes, and distributionally robust bounds for both dynamic and static uncertainties are determined using DRMPC. Through ambiguity tubes and distributionally robust optimization, the stochastic MPC system is converted into a nominal one. Case studies validate the effectiveness of the proposed approach.

虚拟电厂（VPP）可以整合各种电力基础设施，例如数据中心（DCs）和电动汽车（ev），这给综合电热系统（IEHS）的运行带来了多重不确定性和挑战。研究了基于vpp的IEHS在静态和动态两种不确定性下的柔性运行问题。首先，对数据中心工作负载的时间转移灵活性进行了建模。其次，提出了一种基于度量的分布式鲁棒模型预测控制（DRMPC）框架，以解决来自可再生能源的静态不确定性和来自电动汽车充电行为的动态不确定性。第三，将动态不确定性重新表述为模糊管，并利用DRMPC确定了动态不确定性和静态不确定性的分布鲁棒界。通过模糊管和分布鲁棒优化，将随机MPC系统转化为标称系统。案例研究验证了所提出方法的有效性。

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引用次数: 0

Numerical investigation on a high-temperature data center cooled by combined liquid-cooling and free-cooling - a comprehensive case study 高温数据中心液体冷却与自然冷却联合冷却的数值研究——一个综合案例研究

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-06 DOI: 10.1016/j.apenergy.2025.127154

Zi-Xing Wang, Ke Xue, Jun-Yu Chen, Nan Li, Wen-Quan Tao

A high-performance cooling system is crucial for data center (DC) energy saving. This paper aims to model and analyze the cooling performance of a novel liquid-cooling and free-cooling combined cooling scheme proposed for high-temperature DC. The air-cooling and liquid-cooling combined servers (ALCS) and free-cooling air handling units (AHU) are combined in the DC. There is still no such study on the ALCS and AHU combined DC cooling performance at the complete DC system level. In this paper, the ANSYS Fluent and MATLAB SIMULINK are used to carry out the CFD simulation and the system-level simulation. For the studied DC in Xi'an, the effects of fans rotation speed (FRS) on IT room cooling performance are first investigated. When FRS is constant, under the inlet air/water temperature of 45 °C, the minimum FRS is 850 rad·s⁻¹ to ensure the safe server operation, while the total rack fans power is 83.68 W. By adjusting the FRS in different ALCSs, a modified FRS working condition is obtained with better cooling performance and lower rack fans power (81.36 W). Then, the free-cooling performance of twelve AHU designs is tested. All AHU designs can meet the cooling requirement when the environment temperature is 15 °C, and the highest COP of AHU (COP_AHU) reaches 28. But when the environment temperature is as high as 40 °C, the highest COP_AHU is only 3.94. The optimized AHU design with the highest ten-year averaged COP_AHU of 17.54 for the studied DC in Xi'an is obtained. The studied DC exhibits effective cooling and energy consumption performance. For the ten-year period, the PUE and COP of DC are respectively 1.18 and 7.64. The COP of the free-cooling part has reached 2.2 times of the COP of the water-cooling part, which indicates that the application of AHU can effectively improve the cooling efficiency.

高性能的冷却系统是数据中心节能的关键。本文旨在模拟和分析一种新型的液体冷却与自然冷却联合冷却方案在高温直流系统中的冷却性能。风冷/液冷联合服务器（ALCS）和自冷空气处理机组（AHU）在直流系统中组合。目前还没有对ALCS和AHU在整个直流系统层面上的联合直流制冷性能进行研究。本文采用ANSYS Fluent和MATLAB SIMULINK进行CFD仿真和系统级仿真。以西安某直流空调为研究对象，首先研究了风机转速（FRS）对IT机房制冷性能的影响。当FRS一定时，进风口温度为45℃时，为保证服务器的安全运行，FRS最小值为850 rad·s−1，机架风扇总功率为83.68 W。通过对不同alcs的FRS进行调节，得到了具有较好散热性能和较低机架风扇功率（81.36 W）的改进后的FRS工况。然后，对12种AHU设计的自冷性能进行了测试。所有AHU设计均能满足环境温度为15℃时的制冷要求，AHU的COP （COPAHU）最高可达28。但当环境温度高达40℃时，COPAHU的最高值仅为3.94。得到了西安直流空调10年平均COPAHU最高为17.54的优化设计。所研究的直流电具有良好的散热和节能性能。10年期间，DC的PUE和COP分别为1.18和7.64。自冷部分COP已达到水冷部分COP的2.2倍，说明采用AHU可有效提高冷却效率。

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引用次数: 0

Design of fair and interpretable electric vehicle charging policies through genetic programming 利用遗传规划设计公平可解释的电动汽车充电政策

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127176

Steffen Limmer , Angus Kenny , Tapabrata Ray , Felix Lanfermann , Hemant Kumar Singh , Andrea Castellani

Controlled charging of a group of electric vehicles (EVs) subject to power limits, such as those imposed by transformer capacity constraints, may result in inefficient and unfair energy distribution among EVs. The present work aims to learn fair and efficient charging policies on historical data using multi-objective genetic programming (GP). A formula, or model, is evolved through GP that takes features of connected EVs as input variables and computes scores to guide the distribution of available energy. Two variants of this approach are proposed and evaluated in simulation experiments, considering a residential charging scenario. In this setting, the dissatisfaction of EV users resulting from a certain charging control policy is quantified as the additional time they required for charging externally, compared to uncontrolled charging. The efficiency and fairness of a charging control policy are measured as the mean and maximum additional external charging time over all users. In the simulation experiments, the proposed approach is compared to several baseline methods, including different manually designed charging policies, such as equal distribution and first-come-first-served, as well as a recent approach for combining multiple fixed charging policies. The experimental results show that the proposed approach increases efficiency by at least 13 % and fairness by at least 12 %. An analysis of the automatically designed policies in terms of interpretability concludes that the best-performing policies are highly complex, containing more than 13 variables and more than 16 operators, on average. However, it is shown that it is possible to significantly reduce this complexity without substantial loss in the quality of the charging control, through appropriate control of the maximum GP tree size.

当一组电动汽车受到功率限制（如变压器容量限制）的控制充电时，可能会导致电动汽车之间的能量分配效率低下和不公平。本工作旨在利用多目标遗传规划（GP）学习公平有效的历史数据收费策略。通过GP，将联网电动汽车的特征作为输入变量，通过计算得分来指导可用能量的分配，从而形成一个公式或模型。本文提出了该方法的两种变体，并在模拟实验中进行了评估，考虑了住宅充电场景。在此设置中，电动汽车用户对充电控制策略的不满被量化为与不受控制的充电相比，用户需要额外的外部充电时间。收费控制策略的效率和公平性是用所有用户额外的外部收费时间的平均值和最大值来衡量的。在仿真实验中，将该方法与几种基线方法进行了比较，包括不同的人工设计的收费策略，如平均分配和先到先得，以及最近的一种组合多个固定收费策略的方法。实验结果表明，该方法的效率提高了至少13%，公平性提高了至少12%。根据可解释性对自动设计策略进行分析后得出结论，性能最佳的策略非常复杂，平均包含超过13个变量和超过16个操作符。然而，研究表明，通过适当控制最大GP树的大小，可以显著降低这种复杂性，而不会对收费控制的质量造成实质性损失。

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引用次数: 0

BattBee: Equivalent circuit modeling and early detection of thermal runaway triggered by internal short circuits for lithium-ion batteries BattBee：锂离子电池内部短路引发热失控的等效电路建模和早期检测

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127016

Sangwon Kang , Hao Tu , Huazhen Fang

Lithium-ion batteries are the enabling power source for transportation electrification. However, in real-world applications, they remain vulnerable to internal short circuits (ISCs) and the consequential risk of thermal runaway (TR). Toward addressing the challenge of ISCs and TR, we undertake a systematic study that extends from dynamic modeling to fault detection in this paper. First, we develop BattBee, the first equivalent circuit model to specifically describe the onset of ISCs and the evolution of subsequently induced TR. Drawing upon electrochemical modeling, the model can simulate ISCs at different severity levels and predict their impact on the initiation and progression of TR events. With the physics-inspired design, this model offers strong physical interpretability and predictive accuracy, while maintaining structural simplicity to allow fast computation. Then, building upon the BattBee model, we develop fault detection observers and derive detection criteria together with decision-making logic to identify the occurrence and emergence of ISC and TR events. This detection approach is principled in design and fast in computation, lending itself to practical applications. Validation based on simulations and experimental data demonstrates the effectiveness of both the BattBee model and the ISC/TR detection approach. The research outcomes underscore this study’s potential for real-world battery safety risk management.

锂离子电池是实现交通电气化的动力源。然而，在实际应用中，它们仍然容易受到内部短路（ISCs）和随之而来的热失控（TR）风险的影响。为了解决ISCs和TR的挑战，我们在本文中进行了从动态建模到故障检测的系统研究。首先，我们开发了BattBee，这是第一个专门描述ISCs发生和随后诱导的TR演变的等效电路模型。利用电化学建模，该模型可以模拟不同严重程度的ISCs，并预测它们对TR事件的发生和进展的影响。基于物理启发的设计，该模型提供了强大的物理可解释性和预测准确性，同时保持了结构的简单性以允许快速计算。然后，在batbee模型的基础上，我们开发了故障检测观测器，并导出了检测标准和决策逻辑，以识别ISC和TR事件的发生和出现。这种检测方法设计原理好，计算速度快，适合实际应用。基于仿真和实验数据的验证证明了BattBee模型和ISC/TR检测方法的有效性。研究结果强调了这项研究在现实世界电池安全风险管理方面的潜力。

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引用次数: 0