Applied Energy最新文献_第7页

Thermoelectric energy harvesting from day–night temperature swings with latent heat storage: Enhancing the efficiency by combining natural and Marangoni convection 利用潜热储存 从昼夜温度波动中收集热电能量：通过结合自然对流和马兰戈尼 对流来提高效率

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-16 DOI: 10.1016/j.apenergy.2025.125880

Santiago Madruga , Carolina Mendoza

Natural energy sources are a solution to power low-consumption electronic devices, such as sensors, in environments where batteries are impractical. Among these sources, thermoelectric conversion stands out for its ability to generate power from temperature fluctuations. However, its efficiency is severely constrained by the small temperature differences typically seen during natural day–night cycles, which limits its usability when relying on ambient thermal gradients. Through realistic physical modeling and 3D numerical simulations, we demonstrate that coupling a thermoelectric generator with a latent heat storage unit significantly enhances the conversion of natural day–night temperature swings into electricity. This enhancement is achieved by combining natural and Marangoni convective heat transfer. We utilize a standard thermoelectric module (Seebeck coefficient of

α = 0.027

) paired with a heat storage unit containing the phase change material hexadecane, which has a Prandtl number of 45.5 and configured with a Bond number of 8. Using temperature profiles representative of Western Europe, Eastern Europe, and Brazil, we illustrate the practical and broad application of these enhanced micro-energy harvesters to power environmental sensors. Over a 24-hour period, the combined effects of buoyancy and thermocapillarity in a

16 {cm}^{3}

heat storage unit yield harvested energies (average power densities) of 2.6 J (

29.7 μ W / {cm}^{2}

), 1.4 J (

16.4 μ W / {cm}^{2}

), and 2.4 J (

27.2 μ W / {cm}^{2}

) for the temperature profiles of Central Europe, Western Europe, and Brazil, respectively. Notably, even with weak thermocapillary effects at this Bond number, Marangoni convection doubles the harvested energy and average power density for the Central and Western Europe profiles compared to natural convection alone. The harvested energy is sufficient to uninterruptly power low-consumption sensors monitoring humidity, pressure, and ambient temperature, along with the necessary accompanying electronics. Importantly, this micro-energy harvester leverages fundamental physical properties of liquids: density variation with temperature (natural convection) and surface tension variation with temperature (Marangoni convection). The robustness of these results provides a foundation for further enhancements under more complex configurations.

自然能源是在电池不实用的环境中为传感器等低功耗电子设备供电的一种解决方案。在这些来源中，热电转换因其从温度波动中发电的能力而脱颖而出。然而，它的效率受到自然昼夜循环中通常看到的小温差的严重限制，这限制了它在依赖环境热梯度时的可用性。通过真实的物理建模和三维数值模拟，我们证明了热电发电机与潜热存储单元的耦合显著增强了自然昼夜温度波动转化为电能的能力。这种增强是通过结合自然和马兰戈尼对流传热来实现的。我们利用标准热电模块（塞贝克系数α=0.027）与含有相变材料十六烷的储热单元配对，十六烷的普朗特数为45.5，键数为8。以西欧、东欧和巴西为代表的温度分布为例，我们说明了这些增强型微能量采集器为环境传感器供电的实际和广泛应用。在24小时的时间里，在一个16cm3的蓄热装置中，浮力和热毛细作用的综合效应在中欧、西欧和巴西的温度分布中分别产生了2.6 J （29.7μW/cm2）、1.4 J （16.4μW/cm2）和2.4 J （27.2μW/cm2）的能量（平均功率密度）。值得注意的是，即使在这个Bond数下存在微弱的热毛细效应，与自然对流相比，Marangoni对流在中欧和西欧剖面上的能量和平均功率密度也增加了一倍。收集的能量足以不间断地为监测湿度、压力和环境温度的低功耗传感器以及必要的配套电子设备供电。重要的是，这种微能量收集器利用了液体的基本物理特性：密度随温度变化（自然对流）和表面张力随温度变化（马兰戈尼对流）。这些结果的健壮性为更复杂配置下的进一步增强奠定了基础。

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引用次数: 0

A bi-level optimization strategy for flexible and economic operation of the CHP units based on reinforcement learning and multi-objective MPC 基于强化学习和多目标MPC的热电联产机组灵活经济运行的双层优化策略

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-16 DOI: 10.1016/j.apenergy.2025.125850

Keyan Zhu , Guangming Zhang , Chen Zhu , Yuguang Niu , Jizhen Liu

Enhancing the comprehensive performance of the combined heat and power (CHP) units is crucial for accommodating renewable energy and achieving energy conservation. To this end, a bi-level optimization strategy based on reinforcement learning (RL) and multi-objective model predictive control (MOMPC) is proposed to enhance the CHP units flexibility and economic performance. Firstly, a CHP unit model is constructed, and its various parameters are incorporated into the rolling optimization of the MOMPC, serving as the lower-level follower to solve the fundamental control. Secondly, a bi-level optimization strategy integrating the twin delayed deep deterministic policy gradient (TD3) algorithm with MOMPC (TD3-MOMPC) is proposed. The TD3 agent is designated as the upper-level leader. By decomposing the complex flexibility requirements and the optimization control sequence of the CHP unit, tasks are assigned to both the upper-level leader and the lower-level follower for bi-level interactive optimization. Thirdly, with power flexibility, heating quality, and operational economy serving as leader guidance, a multi-criterion optimization reward function is designed for the upper-level. Then, the actions of the upper-level TD3 agent are designed as dynamic weights and time-varying prediction horizons for the rolling optimization of MOMPC, serving as a bridge to connect and guide the bi-level optimization. Finally, to verify the effectiveness of the bi-level optimization strategy, extensive tests on load variation and disturbance rejection were conducted on a 300 MW CHP unit. The results show that the proposed strategy enhances the unit's load flexibility, heating quality, and operational economy.

提高热电联产（CHP）机组的综合性能对于适应可再生能源和实现节能至关重要。为此，本文提出了一种基于强化学习（RL）和多目标模型预测控制（MOMPC）的双层优化策略，以提高热电联产机组的灵活性和经济性。首先，构建热电联产机组模型，并将其各种参数纳入 MOMPC 的滚动优化中，作为解决基本控制的下级随从。其次，提出了将双延迟深度确定性策略梯度（TD3）算法与 MOMPC（TD3-MOMPC）相结合的双层优化策略。TD3 代理被指定为上层领导。通过分解热电联产机组复杂的灵活性要求和优化控制顺序，将任务分配给上层领导者和下层追随者，实现双层互动优化。第三，以电力灵活性、供热质量和运行经济性为领导导向，为上层设计了多标准优化奖励函数。然后，将上层 TD3 代理的行动设计为 MOMPC 滚动优化的动态权重和时变预测视野，作为连接和指导双层优化的桥梁。最后，为了验证双级优化策略的有效性，在 300 MW 热电联产机组上进行了大量的负荷变化和干扰抑制测试。结果表明，建议的策略提高了机组的负荷灵活性、供热质量和运行经济性。

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引用次数: 0

Coupling time-scale reinforcement learning methods for building operational optimization with waste heat 基于余热的建筑运行优化耦合时间尺度强化学习方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125851

Zhe Chen , Tian Xing , Yu Wang , Yunlin Zhuang , Meng Zheng , Qianchuan Zhao , Qing-Shan Jia

This paper focuses on the joint optimization of fan coil units (FCUs) and heat pumps in HVAC systems for multi-zone building environments. The core problem involves balancing fast local control of FCUs with slower global control of heat pumps to ensure energy efficiency and indoor comfort. To tackle this, we propose a coupling time-scale reinforcement learning (RL) algorithm, specifically a Deep Q-Network (DQN)-based approach that employs a multi-task learning network to manage both FCU and heat pump control efficiently through the utilization of shared state information. Moreover, the agents are trained and make decisions collaboratively across different timescales. In addition, we develop a high-fidelity building simulation that incorporates detailed thermal models, dynamic loading, and waste heat modules to evaluate the performance of the system in real-world conditions. The experimental results show that the proposed coupling time-scale DQN algorithm improves the accuracy of temperature control by 35.4 % and 26.21 % compared to traditional DQN and the occupant-centric control (OCC) algorithms. Additionally, it reduces regional power fluctuations by 25.18 % and 56.74 % relative to these traditional algorithms. Simultaneously, the proposed algorithm achieves the lowest heat pump energy consumption (2964 W), outperforming traditional DQN (2977 W) and OCC (3051 W) respectively, while maintaining superior temperature control accuracy. These quantitative improvements collectively demonstrate the proposed algorithm’s capability to synergistically balance thermal comfort, power fluctuation, and energy consumption.

本文重点研究多区建筑环境下暖通空调系统中风机盘管机组（FCU）和热泵的联合优化问题。核心问题包括平衡 FCU 的快速局部控制和热泵的慢速全局控制，以确保能源效率和室内舒适度。为解决这一问题，我们提出了一种耦合时间尺度强化学习（RL）算法，特别是基于深度 Q 网络（DQN）的方法，该方法采用多任务学习网络，通过利用共享状态信息来有效管理 FCU 和热泵控制。此外，我们还对代理进行了培训，并在不同时间尺度上协同做出决策。此外，我们还开发了一种高保真建筑仿真，其中包含详细的热模型、动态负载和废热模块，以评估系统在实际条件下的性能。实验结果表明，与传统的 DQN 算法和以住户为中心的控制 (OCC) 算法相比，所提出的耦合时间尺度 DQN 算法分别提高了 35.4% 和 26.21% 的温度控制精确度。此外，与这些传统算法相比，它还将区域功率波动降低了 25.18% 和 56.74%。同时，所提出的算法实现了最低的热泵能耗（2964 瓦），分别优于传统的 DQN（2977 瓦）和 OCC（3051 瓦），同时保持了卓越的温度控制精确度。这些量化改进共同证明了所提出的算法能够协同平衡热舒适度、功率波动和能耗。

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引用次数: 0

Multi-objective optimization for energy-efficient management of electric Tractors via hybrid energy storage systems and scenario recognition 通过混合储能系统和场景识别实现电动拖拉机节能管理的多目标优化

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125898

Qiang Yu , Xionglin He , Yongji Chen , Zihong Jiang , Yilin Tan , Longze Liu , Bin Xie , Changkai Wen

The promotion of electric tractors faces significant challenges, including adapting powertrain systems to diverse operational conditions and optimizing energy efficiency and battery lifespan. This paper presents a hybrid energy storage system (HESS) architecture for electric tractors. And a multi-objective energy-efficient management strategy (EMS) based on plowing operation scenario recognition is proposed. The strategy involves developing an electric tractor model and a plowing operating condition (POC) cycle using real-world plowing data. Offline classification is performed using K-means clustering and Principal Component Analysis (PCA), while a Multilayer Perceptron Neural Network (MLPNN) is employed for online real-time scenario recognition. Additionally, a Multi-Strategy Improved Black-winged Kite Algorithm (MSIBKA) is developed to efficiently derive adaptive power allocation trajectories. Simulation and Hardware-in-the-Loop (HIL) experiments demonstrate that the proposed strategy effectively extends the lifespan of the HESS, smooths battery output, and reduces operating costs. Specifically, the supercapacitor supplies over 65 % of the peak power demand, reducing the battery C-rate by more than 10 %. Furthermore, the proposed system increases the state of charge (SOC) of the battery by at least 5 %, while reducing both operational costs and battery degradation costs by over 33.3 %. These results indicate that the proposed system and strategy provide substantial benefits in extending battery lifespan and enhancing energy efficiency.

电动拖拉机的推广面临着重大挑战，包括使动力系统适应不同的操作条件，优化能源效率和电池寿命。提出了一种电动拖拉机混合储能系统（HESS）体系结构。提出了一种基于耕作作业场景识别的多目标节能管理策略。该策略包括开发电动拖拉机模型和使用实际耕作数据的耕作工况（POC）循环。离线分类采用k均值聚类和主成分分析（PCA），在线实时场景识别采用多层感知器神经网络（MLPNN）。此外，提出了一种改进的多策略黑翼风筝算法（MSIBKA），有效地推导出自适应功率分配轨迹。仿真和硬件在环（HIL）实验表明，该策略有效地延长了HESS的使用寿命，平滑了电池输出，降低了运行成本。具体来说，超级电容器提供超过65%的峰值电力需求，将电池的c率降低了10%以上。此外，该系统将电池的荷电状态（SOC）提高了至少5%，同时将运行成本和电池退化成本降低了33.3%以上。这些结果表明，所提出的系统和策略在延长电池寿命和提高能源效率方面具有实质性的好处。

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引用次数: 0

Optimal management of coupled hydrogen-electricity energy systems at ports by multi-time scale scheduling 通过多时间尺度调度优化港口氢电耦合能源系统的管理

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125885

Daogui Tang , Pingxu Ge , Chengqing Yuan , Haidong Ren , Xiaohui Zhong , Mingwang Dong , Gibran David Agundis-Tinajero , Cesar Diaz-Londono , Josep M. Guerrero , Enrico Zio

This paper proposes a multi-time scale scheduling strategy for a practical port coupled hydrogen-electricity energy system (CHEES) to optimize the integration of renewable energy and manage the stochasticity of port power demand. An optimization framework based on day-ahead, intra-day and real-time scheduling is designed. The framework allows coordinating adjustable resources with different rates to reduce the impact of forecast errors and system disturbances, thus improving the flexibility and reliability of the system. The effectiveness of the proposed strategy is verified by a case study of the actual CHEES in the Ningbo Zhoushan Port, and the impact of equipment anomalies on the port power system operation is studied through simulation of different scenarios. The results show that compared with a scheduling scheme without energy management strategy, CHEES with multi-time scale scheduling can save 25.42 % of costs and reduce 14.78 % of CO₂ emissions. A sensitivity analysis is performed to highlight the impact of hydrogen price and soft open points (SOP) rated power on the system economy. This study not only provides a new perspective for the optimal scheduling of port energy systems, but also provides a practical framework for managing port energy systems to achieve green transformation and sustainable development.

针对实际港口氢-电耦合能源系统（CHEES），提出了一种多时间尺度的调度策略，以优化可再生能源的整合并管理港口电力需求的随机性。设计了基于日前调度、日内调度和实时调度的优化框架。该框架允许以不同的速率协调可调资源，以减少预测误差和系统干扰的影响，从而提高系统的灵活性和可靠性。通过宁波舟山港CHEES的实际案例研究，验证了所提策略的有效性，并通过不同场景的仿真研究了设备异常对港口电力系统运行的影响。结果表明，与不采用能源管理策略的调度方案相比，采用多时间尺度调度的CHEES可节约25.42%的成本，减少14.78%的CO2排放。通过敏感性分析，突出了氢价格和软开放点（SOP）额定功率对系统经济的影响。本研究不仅为港口能源系统优化调度提供了新的视角，也为港口能源系统管理实现绿色转型和可持续发展提供了实用框架。

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引用次数: 0

Optimizing hybrid energy systems for remote Australian communities: The role of tilt angle in cost-effective green hydrogen production 为澳大利亚偏远社区优化混合能源系统：倾斜角度在经济高效的绿色制氢中的作用

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125921

Tushar Kanti Roy , Sajeeb Saha , Amanullah Maung Than Oo

This study investigates hybrid energy systems (HESs) integrating photovoltaic (PV) panels, batteries, fuel cells (FCs), electrolyzers (ELs), and hydrogen tanks (HTs) to address the energy needs of remote Australian communities. Two configurations are analyzed: Type-A (PV/Batt/FC/EL/HT) and Type-B (PV/FC/EL/HT), focusing on cost-efficiency, energy reliability, and hydrogen production. Several optimization techniques, including the cuckoo search algorithm, non-dominated sorting genetic algorithm-II (NSGA-II), and sequential quadratic programming algorithm (SQPA), flower pollination algorithm, constrained PSO, and harmony search algorithm, are employed to determine optimal system configurations. Type-A emerges as the most cost-effective configuration when optimized with NSGA-II, achieving a net present cost (NPC) of $226,500, a levelized cost of electricity (LCOE) of $0.193/kWh, and a levelized cost of hydrogen (LCOH) of $4.88/kg. Battery integration in Type-A enhances both cost-efficiency and energy reliability. For hydrogen-focused applications, SQPA yields the highest hydrogen production at 4737 kg/year, supported by higher EL (14 kW) and FC (18.63 kW) capacities. System efficiency is found to be highly sensitive to PV tilt angle, with

30^{\circ}

identified as optimal. Increasing the tilt to

70^{\circ}

can raise system costs by up to 75 %. Sensitivity analyses reveal that improving component efficiencies dramatically impacts costs. For example, increasing fuel cell efficiency from 40 % to 60 % reduces NPC, LCOE, and LCOH by $40,000, $0.04/kWh, and $0.1/kg, respectively, especially in Type-A systems. Collectively, adjustments to PV tilt angles and component efficiencies can reduce overall costs by up to 40 %. These insights offer a strategic foundation for designing HESs that balance electricity and hydrogen generation, tailored for sustainable operation in off-grid and remote settings.

本研究调查了集成光伏（PV）面板、电池、燃料电池（fc）、电解槽（el）和氢罐（ht）的混合能源系统（HESs），以解决澳大利亚偏远社区的能源需求。分析了两种配置：a型（PV/ bat /FC/EL/HT）和b型（PV/FC/EL/HT），侧重于成本效益、能源可靠性和制氢。采用布谷鸟搜索算法、非支配排序遗传算法- ii （NSGA-II）、顺序二次规划算法（SQPA）、传粉算法、约束粒子群算法和和声搜索算法等优化技术确定系统的最优配置。当与NSGA-II进行优化后，a型成为最具成本效益的配置，实现净当前成本（NPC）为226,500美元，平准化电力成本（LCOE）为0.193美元/千瓦时，平准化氢成本（LCOH）为4.88美元/公斤。a型电池集成提高了成本效率和能源可靠性。对于以氢为重点的应用，SQPA的氢气产量最高，为4737 kg/年，并具有更高的EL（14 kW）和FC（18.63 kW）容量。系统效率对PV倾角高度敏感，30°被认为是最佳的。将倾斜度增加到70°会使系统成本增加高达75% %。敏感性分析表明，提高组件效率会显著影响成本。例如，将燃料电池效率从40% %提高到60% %，可使NPC、LCOE和LCOH分别降低40,000美元、0.04美元/千瓦时和0.1美元/千克，特别是在a型系统中。总的来说，调整光伏倾斜角度和组件效率可以降低高达40% %的总成本。这些见解为设计平衡电力和氢气生产的HESs提供了战略基础，为离网和偏远地区的可持续运行量身定制。

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引用次数: 0

A distributionally robust collaborative scheduling and benefit fallocation method for interconnected microgrids considering tail risk assessment 考虑尾部风险评估的互联微电网分布式稳健协同调度和收益分配方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125910

Jialin Du , Weihao Hu , Sen Zhang , Di Cao , Wen Liu , Zhenyuan Zhang , Daojuan Wang , Zhe Chen

The uncertainty of load and renewable energy poses a huge challenge to the optimal economic dispatch of interconnected microgrids. In this paper, a distributionally robust optimization (DRO) collaborative scheduling and cooperative benefit allocation method is proposed. First, an improved ambiguity set is constructed to characterize the uncertainty of load and renewable energy to reduce unnecessary conservatism of the scheduling strategy. Then, the day-ahead collaborative scheduling problem of interconnected microgrids is constructed as a DRO model based on the conditional value at risk (CVaR) to accurately assess the tail average risks of strategies. Furthermore, due to the difficulty of solving the double-layer definite integral optimization model, this paper equivalently transforms the original model into an easily solvable single-layer mixed-integer second-order cone programming (MISOCP) model through dual transformation and reformulation of interval constraints. Subsequently, a benefit allocation strategy based on the improved Shapley value is proposed, which considers energy supply and demand fluctuations to encourage microgrids to participate in energy sharing. Finally, the case study demonstrates that the day-ahead risks and actual costs of the microgrid cluster are reduced by 20.19 % and 15.07 %, respectively, and the proposed method can achieve more fair benefit allocation under source and load uncertainty.

负荷和可再生能源的不确定性对互联微电网的优化经济调度提出了巨大挑战。提出了一种分布式鲁棒优化（DRO）协同调度与协同效益分配方法。首先，构建改进的模糊集来表征负荷和可再生能源的不确定性，以减少调度策略中不必要的保守性；然后，将互联微电网日前协同调度问题构建为基于条件风险值（CVaR）的DRO模型，以准确评估策略尾部平均风险。进一步，针对双层定积分优化模型求解困难的问题，通过对偶变换和区间约束的重新表述，将原模型等效转化为易于求解的单层混合整数二阶锥规划（MISOCP）模型。随后，提出了一种基于改进Shapley值的利益分配策略，该策略考虑能源供需波动，鼓励微电网参与能源共享。最后，实例研究表明，该方法可使微网集群的日前风险和实际成本分别降低20.19%和15.07%，在源和负荷不确定的情况下，实现更公平的效益分配。

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引用次数: 0

Uncovering Sodiated HC dominated thermal runaway mechanism of NFPP/HC pouch battery 揭示了硫化HC主导的NFPP/HC袋式电池热失控机理

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125936

Wei Li , Shini Lin , Honghao Xie , Yuan Qin , Qilong Wu , Jing Zeng , Peng Zhang , Jinbao Zhao

Sodium-ion batteries (SIBs) are considered a promising technology for large-scale energy storage systems (LSESS) because of their rich resources and outstanding electrochemical performance. However, the safety of SIBs is rarely discussed, and the thermal stability is critical to the application of the battery, especially for LSESS. In this study, the thermal runaway mechanism of Na₃Fe₂(PO₄)(P₂O₇)||hard carbon (NFPP/HC) pouch batteries dominated by heat generation from the sodiated anode has been uncovered. The heat generation analysis based on battery and material levels shows that the exothermic reaction between HC and the electrolyte begins to occur at 100 °C (the exothermic reaction between NFPP and the electrolyte is near 230 °C), and the reaction between the anode and electrolyte releases a large amount of heat, while NFPP materials exhibit less and milder exothermic behavior. Meanwhile, the melting temperature of the separator is extremely close to the triggering temperature of thermal runaway. Therefore, the exothermic reaction between HC and the electrolyte can cause the separator to melt, thus triggering thermal runaway of the SIBs. More seriously, when sodium plating occurs, the safety of the battery will further deteriorate. Considering the characteristic of great heat generation in the early stage of thermal runaway of SIBs, the ceramic-coated separators with higher thermal stability and higher wettability are applied to SIBs, which significantly improve battery safety. This study reveals the mechanism of thermal runaway in SIBs (NFPP/HC), which is expected to provide guidance for the research of safer SIBs.

钠离子电池以其丰富的资源和优异的电化学性能被认为是一种很有前途的大规模储能技术。然而，sib的安全性很少被讨论，热稳定性对电池的应用至关重要，特别是对于less电池。本研究揭示了Na3Fe2(PO4)(P2O7)||硬碳（NFPP/HC）袋状电池的热失控机理。基于电池和材料水平的产热分析表明，HC与电解质之间的放热反应在100℃时开始发生（NFPP与电解质之间的放热反应在230℃附近），阳极与电解质之间的反应释放大量热量，而NFPP材料的放热行为较少且较温和。同时，分离器熔化温度与热失控触发温度极为接近。因此，HC与电解质之间的放热反应会导致分离器熔化，从而引发sib的热失控。更严重的是，当镀钠发生时，电池的安全性会进一步恶化。考虑到sib热失控初期产热较大的特点，将具有较高热稳定性和较高润湿性的陶瓷涂层隔膜应用于sib，显著提高了电池的安全性。本研究揭示了sib的热失控机理（NFPP/HC），有望为更安全sib的研究提供指导。

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引用次数: 0

A Li-O2 battery model coupled with LiO2 and Li2O2 reveals regulation mechanism of deposited product composition on mass transport and electron transfer 用LiO2和Li2O2耦合的锂氧电池模型揭示了沉积产物组成对质量传递和电子转移的调控机制

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125934

Yuanhui Wang , Linfeng Zang , Shaojun Dou , Liang Hao

To elucidate the regulation mechanism of deposited product composition on the coupling process of mass transport and electron transfer in lithium‑oxygen (Li-O₂) batteries, a novel multi-step discharge/charge model with solid lithium superoxide (LiO₂(s)) and lithium peroxide (Li₂O₂) as hybrid precipitation is proposed. This model couples the dynamic competitive growth mechanisms between LiO₂(s) and Li₂O₂, while incorporating the asymmetric deposition and decomposition behaviors of Li₂O₂. The electrode surface passivation caused by the sluggish kinetic rate of electron across the Li₂O₂ film is solely responsible for the deep discharge termination based on the reduced graphene oxide cathode. LiO₂(s) dominates the precipitation products with a limited capacity and a continuous decline in the LiO₂(s) percentage with discharge depth. Although promoting the LiO₂(s) formation is conducive to alleviating the electrode surface passivation, it aggravates the O₂ transport resistance due to occupying more electrode pores with the same charge contribution. Hence the discharge capacity demonstrates a three-stage variation with increasing lithium superoxide (LiO₂) formation rate, which rapidly grows by more than two times in stage 2 benefiting from the increased LiO₂(s) percentage and enhanced solution mechanism. Whereas a slow rise of 32 % in the discharge capacity in stage 3 is attributed to the conversion of LiO₂(s) to Li₂O₂ toroid for the discharge process controlled by O₂ transport. An increase in the thickness or specific surface area of the cathode improves the discharge capacity mainly by facilitating the production of Li₂O₂ toroid despite the decrease in the LiO₂(s) percentage, which is different from the regulatory mechanism of electrode porosity for accelerating the LiO₂(s) formation. Increasing LiO₂ solubility predominantly mitigates the electrode surface passivation through enhancement of the solution pathway, whereas the elevated O₂ solubility synergistically facilitates the co-formation of LiO₂(s) and Li₂O₂ toroid. In addition, the LiO₂(s) percentage declines for the amorphous Li₂O₂ film with low resistance and the electrode surface passivation mainly originates from the coverage of reactive sites by Li₂O₂.

为了阐明沉积产物组成对锂氧（Li-O2）电池中质量传递和电子转移耦合过程的调控机制，提出了固体超氧化物锂（LiO2(s)）和过氧化锂（Li2O2）作为杂化沉淀的多步放电/充电模型。该模型将LiO2(s)和Li2O2之间的动态竞争生长机制耦合在一起，同时考虑了Li2O2的不对称沉积和分解行为。电子在Li2O2薄膜上缓慢的运动速率导致的电极表面钝化是基于还原氧化石墨烯阴极的深度放电终止的唯一原因。降水产物以LiO2(s)为主，容量有限，且随放电深度的增加，LiO2(s)百分比持续下降。虽然促进LiO2(s)的形成有利于缓解电极表面钝化，但由于在相同电荷贡献的情况下占据了更多的电极孔，从而加剧了O2的传输阻力。因此，放电容量随LiO2形成速率的增加呈现出三个阶段的变化，在第2阶段，由于LiO2(s)百分比的增加和溶液机制的增强，放电容量快速增长两倍以上。而第三阶段的放电容量缓慢上升了32%，这是由于在O2输运控制的放电过程中，LiO2(s)转化为Li2O2环状体。阴极厚度或比表面积的增加主要通过促进Li2O2环体的生成来提高放电容量，尽管LiO2(s)的百分比会降低，这与电极孔隙率加速LiO2(s)形成的调节机制不同。增加的LiO 2溶解度主要通过增强溶液途径减轻电极表面钝化，而提高的O2溶解度则协同促进了LiO 2和Li2O2环状物的共形成。此外，低电阻非晶Li2O2膜的LiO2(s)百分比下降，电极表面钝化主要源于Li2O2覆盖了反应位点。

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引用次数: 0

India's residential space cooling transition: Decarbonization ambitions since the turn of millennium 印度住宅空间降温转型：千禧年以来的脱碳雄心

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-15 DOI: 10.1016/j.apenergy.2025.125929

Ran Yan , Nan Zhou , Minda Ma , Chao Mao

As an emerging emitter poised for significant growth in space cooling demand, India requires comprehensive insights into historical emission trends and decarbonization performance to shape future low-carbon cooling strategies. By integrating a bottom-up demand resource energy analysis model and a top-down decomposition method, this study is the first to conduct a state-level analysis of carbon emission trends and the corresponding decarbonization efforts for residential space cooling in urban and rural India from 2000 to 2022. The results indicate that (1) the carbon intensity of residential space cooling in India increased by 292.4 % from 2000 to 2022, reaching 513.8 kg of carbon dioxide per household. The net state domestic product per capita, representing income, emerged as the primary positive contributor. (2) The increase in carbon emissions from space cooling can be primarily attributed to the use of fans. While fan-based space cooling has nearly saturated Indian urban households, it is anticipated to persist as the primary cooling method in rural households for decades. (3) States with higher decarbonization potential are concentrated in two categories: those with high household income and substantial cooling appliance ownership and those with pronounced unmet cooling demand but low household income and hot climates. Furthermore, it is believed that promoting energy-efficient building designs can be prioritized to achieve affordable space cooling. Overall, this study serves as an effective foundation for formulating and promoting India's future cooling action plan, addressing the country's rising residential cooling demands and striving toward its net-zero goal by 2070.

作为一个新兴的排放国，印度的空间冷却需求将大幅增长，因此需要全面了解历史排放趋势和去碳化绩效，以制定未来的低碳冷却战略。通过整合自下而上的需求资源能源分析模型和自上而下的分解方法，本研究首次对 2000 年至 2022 年印度城市和农村地区住宅空间制冷的碳排放趋势和相应的去碳化努力进行了邦一级的分析。结果表明：(1) 从 2000 年到 2022 年，印度住宅空间制冷的碳强度增加了 292.4%，达到每户 513.8 千克二氧化碳。代表收入的人均国内净产值成为主要的正贡献者。(2）空间冷却碳排放量的增加主要归因于风扇的使用。虽然使用风扇的空间降温已接近印度城市家庭的饱和，但预计几十年内，风扇仍将是农村家庭的主要降温方式。(3）脱碳潜力较大的邦主要集中在两类：一类是家庭收入高、拥有大量制冷设备的邦，另一类是制冷需求明显得不到满足但家庭收入低、气候炎热的邦。此外，研究还认为，可优先推广节能建筑设计，以实现可负担得起的空间降温。总之，这项研究为制定和推广印度未来的降温行动计划奠定了有效基础，以解决该国不断增长的住宅降温需求，并努力实现到 2070 年的净零降温目标。

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引用次数: 0