Future Batteries最新文献

A reduced-order thermal model of battery thermal management system for online applications based on proper orthogonal decomposition and Galerkin projection method 基于适当正交分解和伽辽金投影法的在线电池热管理系统降阶热模型

Future Batteries

Pub Date : 2026-02-01 DOI: 10.1016/j.fub.2026.100149

Yankong Song , Lili Li , Chao Lyu , Xiao Liang , Wei Li

The temperature of lithium-ion batteries (LIBs) manifests significant hysteresis effects that substantially impede precise temperature regulation within battery thermal management systems (BTMS). The application of model predictive control (MPC) has been identified as a potentially effective strategy for mitigating thermal hysteresis. However, the existing thermal models for LIBs lack the requisite accuracy and computational efficiency for effective implementation in online MPC frameworks. In this paper, a reduced-order thermal model (ROTM) of BTMS is established based on the proper orthogonal decomposition (POD) and Galerkin projection. Firstly, a finite element model (FEM) of three parallel and eight series air-cooling battery module is constructed in aim of generating original data. The basis vectors of the flow and temperature fields of the battery module are subsequently extracted from the original data by the POD method. Finally, the Navier-Stokes equation and the Fourier's law of heat conduction are projected on the basis vectors previously described. The ROTM can thus be obtained. In comparison with the FEM, the ROTM exhibits a significantly reduced computational time and maintains adequate accuracy. The computational time for ROTM is merely one ten-thousandth of that required by FEM, whilst under 1.25 C constant-current conditions the maximum error between the two methods is less than 0.2°C.

锂离子电池（LIBs）的温度表现出明显的滞后效应，严重阻碍了电池热管理系统（BTMS）的精确温度调节。模型预测控制（MPC）的应用被认为是一种潜在有效的缓解热滞后的策略。然而，现有的lib热模型缺乏在在线MPC框架中有效实现所需的精度和计算效率。基于适当正交分解和伽辽金投影，建立了BTMS的降阶热模型（ROTM）。首先，为了生成原始数据，建立了3并联8串联风冷电池模块的有限元模型；然后利用POD方法从原始数据中提取电池模块流量场和温度场的基向量。最后，将Navier-Stokes方程和傅立叶热传导定律投影到前面描述的基向量上。这样就可以得到ROTM。与有限元法相比，ROTM的计算时间大大缩短，并保持了足够的精度。ROTM的计算时间仅为FEM的万分之一，而在1.25 ℃恒流条件下，两种方法的最大误差小于0.2℃。

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引用次数: 0

Smart magnesium batteries: Using AI to power greener and more reliable desalination systems 智能镁电池：利用人工智能为更环保、更可靠的海水淡化系统供电

Future Batteries

Pub Date : 2026-02-01 DOI: 10.1016/j.fub.2026.100151

Oluwafemi Babatunde Olasilola , Adeola Ajoke Oni , Rukayat Abisola Olawale , Adeyinka G. Ologun , Amirlahi Ademola Fajingbesi , Kemi K. Oladapo , Francis T. Omigbodun

This study develops a focused AI-based optimisation framework to improve the performance of magnesium alloy batteries for renewable-powered desalination systems. The objective is to enhance voltage stability, reduce internal resistance, and extend cycle life through coordinated optimisation of material and operating parameters. An analytical–simulation methodology is adopted, combining electrochemical degradation models with machine learning prediction and genetic algorithm optimisation. Key variables include alloy composition, electrolyte type, operating temperature, and current density. Neural networks were trained using a literature-anchored dataset and validated through cross-validation, while genetic algorithms were used to identify optimal multi-objective operating conditions. The optimised Mg–Al configurations demonstrated a 25 % reduction in voltage degradation, a 50 % decrease in internal resistance, and a 20 % increase in cycle life compared with baseline (non-optimised) conditions, achieving up to 220 stable cycles. The predictive models attained a 94.5 % accuracy with a root mean square error of 0.015 V, indicating low prediction uncertainty and robust generalisation within the studied domain. These quantified improvements translate into higher energy efficiency and reduced maintenance demand for desalination applications. Overall, the results confirm that AI-assisted optimisation provides a reliable, resource-efficient pathway for designing sustainable magnesium-based energy storage systems aligned with circular economy objectives.

本研究开发了一个基于人工智能的优化框架，以提高可再生能源海水淡化系统中镁合金电池的性能。目标是通过协调优化材料和操作参数来提高电压稳定性，降低内阻，延长循环寿命。采用分析模拟方法，将电化学降解模型与机器学习预测和遗传算法优化相结合。关键变量包括合金成分、电解质类型、工作温度和电流密度。神经网络使用文献锚定数据集进行训练，并通过交叉验证进行验证，而遗传算法则用于识别最佳的多目标操作条件。与基线（非优化）条件相比，优化后的Mg-Al配置显示电压退化降低了25% %，内阻降低了50% %，循环寿命增加了20% %，实现了高达220个稳定循环。预测模型的准确率为94.5 %，均方根误差为0.015 V，表明预测不确定性低，在研究领域内具有较强的通用性。这些量化的改进转化为更高的能源效率和减少对海水淡化应用的维护需求。总体而言，研究结果证实，人工智能辅助优化为设计符合循环经济目标的可持续镁基储能系统提供了一条可靠、资源高效的途径。

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引用次数: 0

Exploring trends in battery manufacturing: Comparative teardown and characterisation of high-performance cells 探索电池制造的趋势：高性能电池的比较拆卸和表征

Future Batteries

Pub Date : 2026-02-01 DOI: 10.1016/j.fub.2026.100150

Hamish T. Reid , Thomas Dore , Gaurav Singh , Yuhan Liu , Huw C.W. Parks , Charlie Kirchner-Burles , Francesco Iacoviello , Thomas S. Miller , Rhodri Jervis , James B. Robinson

As demand for higher energy and power density batteries continues to grow, both academia and industry continue to develop across multiple length scales. As academia often focusses on materials development, there is little publicly available information on how industry approaches the challenge of improving their energy storage devices. This is also an issue for the computational community, who require up-to-date baseline data on the latest cells to produce effective models. This work provides a detailed comparison of a range of parameters of two recent cells models from the same manufacturer, E-One Moli Energy’s P45B and new P50B, to give an insight into recent industrial developments. Non-destructive CT shows the difference in the internal architecture, particularly the increased number of windings in the cell jellyroll. Teardown analysis reveals thicker tabs in the P50B to accommodate a higher rated discharge current, and heavier calendering to improve mass loading and coating adhesion. EDX analysis confirms that both cells have a high-nickel NCA chemistry with a graphite/silicon negative electrode Micro-CT and subsequent image quantification show increased tortuosity in the electrodes. Electrochemical results show that the higher tortuosity may contribute to the increased resistance and poorer high-rate performance in the P50B relative to the P45B.

随着对更高能量和功率密度电池的需求不断增长，学术界和工业界都在不断发展跨多个长度尺度的电池。由于学术界经常关注材料开发，很少有关于工业如何应对改进其储能设备的挑战的公开信息。这也是计算界的一个问题，他们需要最新单元的最新基线数据来生成有效的模型。这项工作提供了来自同一制造商的两种最新电池模型的一系列参数的详细比较，E-One Moli Energy的P45B和新的P50B，以深入了解最近的工业发展。非破坏性CT显示了内部结构的差异，特别是细胞水母圈中线圈数量的增加。拆解分析显示，P50B中更厚的标签可以适应更高的额定放电电流，更重的压延可以改善质量负载和涂层附着力。EDX分析证实，两个电池都具有高镍NCA化学性质，石墨/硅负极Micro-CT和随后的图像量化显示电极扭曲度增加。电化学结果表明，相对于P45B，较高的弯曲度可能导致P50B的电阻增加，而高倍率性能较差。

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引用次数: 0

Advancing battery technology for wearable and implantable devices, the current challenges and future directions - A short review 推进可穿戴和植入式设备电池技术，当前的挑战和未来的方向-简短回顾

Future Batteries

Pub Date : 2026-01-20 DOI: 10.1016/j.fub.2026.100148

Darren John Haines , Mian Hammad Nazir

With the increasing demands in healthcare, wearable and implantable devices are now crucial in preventing and treating patients' conditions. However, the current battery technology used in these devices has become a significant barrier to further advancements. To tackle this, many research centres are now concentrating on key principles of human physiology and employing new, innovative materials and structural designs within battery cells to enhance factors such as size, biocompatibility, and overall cell efficiency. Although considerable momentum and significant breakthroughs are being achieved concerning greater flexibility and biocompatibility, battery cells remain imperfect, and enhancements are still required in several areas to develop a truly next-generational battery. To offer a current perspective on the situation, this research article seeks to present a concise overview of the current challenges and future prospects associated with next-generation batteries for wearable and implantable devices.

随着医疗保健需求的增加，可穿戴和可植入设备现在在预防和治疗患者疾病方面至关重要。然而，目前在这些设备中使用的电池技术已经成为进一步发展的重大障碍。为了解决这个问题，许多研究中心现在正专注于人类生理学的关键原理，并在电池中采用新的、创新的材料和结构设计，以提高诸如尺寸、生物相容性和整体电池效率等因素。尽管在更大的灵活性和生物相容性方面取得了相当大的进展和重大突破，但电池仍然不完美，要开发真正的下一代电池，还需要在几个领域进行改进。为了提供一个当前的观点，这篇研究文章试图对当前的挑战和与可穿戴和可植入设备的下一代电池相关的未来前景进行简要概述。

引用次数: 0

A holistic optimization framework for virtual power plants with physics-informed battery degradation and probabilistic stability constraints 具有物理通知电池退化和概率稳定性约束的虚拟电厂的整体优化框架

Future Batteries

Pub Date : 2026-01-14 DOI: 10.1016/j.fub.2026.100146

Vikram Kumar , Muhammad Ahsan Niazi , Usama Aslam , Nagham Saeed , Muhammad Aurangzeb , Syed Abid Ali Shah

The operation of Virtual Power Plants (VPPs) is impacted by both the uncertainty of markets and the limitations of physical assets, affecting the financial reliability and asset longevity of VPPs. This paper outlines a new two-stage stochastic optimization method for the co-optimization of the VPP's financial performance, its battery degradation, and its ability to provide primary frequency response. Key aspects of this method include: (1) a real-time, physics based electrochemical model to estimate the marginal cost of battery degradation in real time; (2) a multivariate ARIMA-GARCH model to forecast correlated market price and renewable power production forecasts; and (3) a Conditional Value at Risk (CVaR) probabilistic constraint to insure reliable frequency response. A detailed case study demonstrates that employing a degradation-aware strategy, rather than a traditional profit-maximizing approach, results in a 5.4 % increase in annual net profit alongside a significant extension of battery lifetime. The proposed method will provide utilities with a strategic decision-making tool to balance their short-term revenue requirements, their long-term asset health needs, and their obligation to maintain grid stability.

虚拟电厂的运行受到市场不确定性和实物资产局限性的双重影响，影响着虚拟电厂的财务可靠性和资产寿命。本文概述了一种新的两阶段随机优化方法，用于共同优化VPP的财务性能、电池退化和提供主频率响应的能力。该方法的关键方面包括：(1)基于物理的实时电化学模型，实时估计电池退化的边际成本；(2)利用多元ARIMA-GARCH模型预测市场价格与可再生能源发电量的相关预测；(3)条件风险值（CVaR）概率约束，以确保可靠的频率响应。一项详细的案例研究表明，采用退化感知策略，而不是传统的利润最大化方法，可使年净利润增加5.4% %，同时显著延长电池寿命。拟议的方法将为公用事业公司提供一种战略决策工具，以平衡其短期收入需求、长期资产健康需求和维护电网稳定的义务。

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引用次数: 0

Experimental analysis of the effects of aging on impedance dependencies 老化对阻抗依赖性影响的实验分析

Future Batteries

Pub Date : 2026-01-13 DOI: 10.1016/j.fub.2026.100145

Christian Rosenmüller , Julia Kowal , Oliver Bohlen

Impedance-based methods for battery diagnostics are becoming increasingly important as lithium-ion batteries continue to proliferate in mobility applications. While electrochemical impedance spectroscopy (EIS) offers powerful diagnostic capabilities, its practical implementation in battery management systems faces challenges due to complex inter-dependencies between temperature , State of Charge , DC-current offset , and State of Health . This study presents a comprehensive analysis of these dependencies through systematic EIS measurements across different aging stages of lithium-ion batteries subjected to combined cycling and fast-charging protocols. The research employs a three-pronged approach: conducting realistic aging series using standard cycles and fast charging, performing systematic characterization of complex impedance at discrete aging stages, and identifying individual parameter sensitivities through global sensitivity analysis. Our methodology aims to identify optimal frequency ranges for impedance-based state estimation and provide a framework for adaptive parameter tuning in practical applications. The results reveal distinct frequency-dependent behaviors and sensitivity patterns that can improve the development of more robust battery management systems, particularly for applications requiring accurate state estimation during fast charging operations.

随着锂离子电池在移动应用中的不断普及，基于阻抗的电池诊断方法正变得越来越重要。虽然电化学阻抗谱（EIS）提供了强大的诊断能力，但由于温度、充电状态、直流电流偏移和健康状态之间复杂的相互依赖关系，其在电池管理系统中的实际应用面临挑战。本研究通过系统的EIS测量，对联合循环和快速充电方案下锂离子电池不同老化阶段的依赖性进行了全面分析。该研究采用了三管齐下的方法：使用标准循环和快速充电进行真实老化系列，在离散老化阶段对复杂阻抗进行系统表征，并通过全局灵敏度分析确定单个参数的灵敏度。我们的方法旨在确定基于阻抗的状态估计的最佳频率范围，并为实际应用中的自适应参数调谐提供框架。研究结果揭示了不同的频率依赖行为和灵敏度模式，可以改善更强大的电池管理系统的开发，特别是对于在快速充电操作期间需要准确状态估计的应用。

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引用次数: 0

A proximity feature method for efficient capacity estimation of fast-charging lithium-ion batteries 一种快速充电锂离子电池有效容量估计的接近特征方法

Future Batteries

Pub Date : 2026-01-08 DOI: 10.1016/j.fub.2026.100144

Xiangjian Zeng, Zehao Yang, Yanqin Zhang

To address the challenge of insufficient capacity estimation accuracy in fast-charging lithium-ion batteries due to stage-dependent heterogeneous degradation, this study proposes a Long Short-Term Memory (LSTM) neural network model based on proximate feature inputs. This proposed method first employs the K-means clustering algorithm to quantitatively analyze capacity decay rates, dividing the battery degradation process into three consecutive stages: the slow decay stage, the transition stage, and the fast decay stage. To address the degradation characteristics across these stages, the model is trained using locally proximate data rather than all-historical data. This approach effectively mitigates the adverse impact of early-cycle data on capacity prediction during the middle and late degradation stages. The method is validated using aging data from 33 lithium iron phosphate cells subjected to six fast-charging protocols. Experimental results demonstrate that, during the transition and fast decay stages, the three-time proximity-based model achieves a 20 %–57 % improvement in estimation accuracy compared to a baseline model trained on all-historical data, while simultaneously reducing training time by 55 %–61 %. Furthermore, the proposed framework exhibits robust adaptability across diverse prediction windows, offering an efficient and accurate solution for capacity estimation in fast-charging lithium-ion batteries.

为了解决锂离子电池快速充电过程中由于阶段相关的异构退化导致容量估计精度不足的问题，本研究提出了一种基于近似特征输入的长短期记忆（LSTM）神经网络模型。该方法首先采用K-means聚类算法定量分析容量衰减率，将电池退化过程划分为三个连续阶段：慢衰减阶段、过渡阶段和快速衰减阶段。为了解决这些阶段的退化特征，使用局部近似数据而不是所有历史数据来训练模型。该方法有效地减轻了早期周期数据在中后期退化阶段对容量预测的不利影响。该方法使用33个磷酸铁锂电池经过6种快速充电方案的老化数据进行了验证。实验结果表明，在过渡和快速衰减阶段，与全历史数据训练的基线模型相比，基于三次接近度的模型的估计精度提高了20 % ~ 57 %，同时减少了55 % ~ 61 %的训练时间。此外，所提出的框架在不同的预测窗口中表现出强大的适应性，为快速充电锂离子电池的容量估计提供了高效、准确的解决方案。

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引用次数: 0

Effects of ionomer chemical degradation on low-Pt proton exchange membrane fuel cells 离子单体化学降解对低铂质子交换膜燃料电池的影响

Future Batteries

Pub Date : 2026-01-07 DOI: 10.1016/j.fub.2026.100143

Xiaohui Yan , Shiqing Liu , Yongjian Su , Jiabin You , Huiyuan Li , Xiaojing Cheng , Congfan Zhao , Yong Feng , Miaomiao He , Guoqiang Zhang , Junliang Zhang

Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.

自由基是质子交换膜燃料电池（pemfc）运行过程中产生的一类活性物质，对pemfc的耐久性有很大影响。以往对燃料电池降解机理的研究主要集中在高Pt负载pemfc中膜电极组件（MEA）的降解，特别是质子交换膜（PEM）的化学降解。然而，低Pt负载和高Pt负载的PEMFCs的特性和性能存在显著差异，特别是在高电流密度下，这主要是由于阴极催化剂层（ccl）中的氧传输过程造成的。目前，很少有相关研究探讨化学降解对低pt pemfc阴极氧传输的影响。因此，本研究通过物理化学表征、电化学测量和分子动力学模拟来研究自由基攻击对离子膜结构的影响、局部氧传输过程以及ccl中离子膜包覆Pt/C结构的演变。我们的研究发现，自由基的攻击降低了ccl的电化学活性面积，但也暂时提高了电池在高电流密度下的性能。此外，分子动力学模拟表明，该离聚体在降解后表现出更高的氧自扩散和更宽松的结构。

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引用次数: 0

Distribution of relaxation times in the diffusion response of the blocked-diffusion Warburg impedance with frequency dispersion 频率色散下阻塞扩散Warburg阻抗扩散响应的弛豫时间分布

Future Batteries

Pub Date : 2026-01-06 DOI: 10.1016/j.fub.2026.100141

Samuel Cruz-Manzo

The impedance response of the blocked-diffusion Warburg impedance with frequency dispersion (BDWf) is represented in the Nyquist plot by a finite diffusion response at high frequencies, followed by a constant phase element (CPE) response at low frequencies. In this study, a mathematical function to estimate the distribution of relaxation times (DRT) in the diffusion response of the BDWf impedance is derived. The analytical transfer function representing the BDWf impedance, reported in a previous study, is considered for the derivation of the DRT mathematical function. The impedance response of an electrical circuit comprising ZARC elements and the BDWf impedance is fitted to the measured impedance response of a lithium-ion battery. The resulting parameters of the BDWf impedance, estimated from the fitting process, are considered in the DRT function. This study demonstrates that it is possible to simulate the impedance responses of the BDWf impedance and the electrical circuit through DRT functions and the Fredholm integral equation. This study also demonstrates the application of the DRT function of the diffusion response of the BDWf impedance, with parameters estimated from EIS measurements carried out on a solar cell. The new DRT function of the diffusion response of the BDWf impedance could allow the estimation of the distribution of the diffusion processes of charge carriers represented in the low-frequency impedance response of electrochemical systems.

带频散的阻塞扩散Warburg阻抗（BDWf）的阻抗响应在Nyquist图中由高频有限扩散响应和低频恒相元（CPE）响应表示。在本研究中，导出了一个数学函数来估计BDWf阻抗扩散响应中的松弛时间（DRT）分布。在先前的研究中，考虑了表示BDWf阻抗的解析传递函数来推导DRT数学函数。将由ZARC元件和BDWf组成的电路的阻抗响应拟合到锂离子电池的测量阻抗响应中。在DRT函数中考虑了从拟合过程中估计的BDWf阻抗的结果参数。本研究表明，通过DRT函数和Fredholm积分方程可以模拟BDWf阻抗和电路的阻抗响应。本研究还演示了BDWf阻抗扩散响应的DRT函数的应用，其参数由在太阳能电池上进行的EIS测量估计。BDWf阻抗扩散响应的新DRT函数可以估计电化学系统低频阻抗响应中载流子扩散过程的分布。

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引用次数: 0

Design of air cooled lithium battery thermal management system with Z-step type flow pattern based on electrochemical-thermal coupled model 基于电化学-热耦合模型的z阶流型风冷锂电池热管理系统设计

Future Batteries

Pub Date : 2026-01-05 DOI: 10.1016/j.fub.2026.100142

Yongtong Li , Tao Ding , Wenshuang Cui , Xiaogang Xu

Air-cooled battery management system (BTMS) is widely used in electric vehicle to regulate temperature of the battery packs, in which the flow pattern dramatically impacts the system cooling performance. In this study, a novel Z-step air-cooled BTMS is proposed to regulate airflow distribution pattern and enhance thermal management efficiency. The heat generation of individual cells was characterized using an electrochemical-thermal coupled model, and a validated computational fluid dynamics (CFD) approach was applied to numerically investigate the effects of the number of steps, battery spacing, and inlet air velocity on system performance. The results indicate that the Z-step configuration provides superior cooling performance, reducing the maximum temperature (T_max), average temperature (T_avg) and maximum temperature differences (ΔT_max) by 2 K, 1 K and 1.6 K, respectively, compared with the conventional Z-type BTMS. Further analysis shows that the optimal cooling performance occurs with step-8 configuration, where the T_max, ΔT_max and T_avg are reduced by 1.61–6.08 K, 1.61–6.26 K and 0.79–1.37 K, respectively. The most efficient cooling is achieved with a battery spacing of 6 mm, resulting in reductions of 2.66–10.73 K in T_max, 0.23–5.84 K in T_avg and 13.54–22.61 K in ΔT_max compared with other spacings. Within the inlet air velocity range of 1.5–7 m/s, the pressure increase remains moderate between 1.5 and 4 m/s, with an optimal airflow velocity of 4 m/s identified. Additionally, as the discharge rates rise, both the maximum and average temperature differences increase significantly, particularly at higher rates. This study provides a valuable guidance for optimizing air-cooled BTMS design.

空气冷却电池管理系统（BTMS）广泛应用于电动汽车的电池组温度调节，其流动模式对系统的冷却性能有很大影响。本研究提出了一种新型的z阶风冷BTMS，以调节气流分布模式，提高热管理效率。采用电化学-热耦合模型对单个电池的产热进行了表征，并应用计算流体动力学（CFD）方法对台阶数、电池间距和入口空气速度对系统性能的影响进行了数值研究。结果表明，与传统的z型BTMS相比，z阶跃结构的最高温度（Tmax）、平均温度（Tavg）和最大温差（ΔTmax）分别降低了2 K、1 K和1.6 K，具有较好的冷却性能。进一步分析表明，采用步进8时冷却性能最佳，Tmax、ΔTmax和Tavg分别降低1.61 ~ 6.08 K、1.61 ~ 6.26 K和0.79 ~ 1.37 K。当电池间距为6 mm时，达到最有效的冷却效果，与其他间距相比，Tmax降低2.66-10.73 K， Tavg降低0.23-5.84 K， ΔTmax降低13.54-22.61 K。在进气速度1.5 ~ 7 m/s范围内，压力增幅在1.5 ~ 4 m/s范围内保持适中，确定最佳气流速度为4 m/s。此外，随着放电速率的增加，最大和平均温差都会显著增加，特别是在较高的放电速率下。该研究为优化风冷式BTMS的设计提供了有价值的指导。

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引用次数: 0