Journal of Power Sources最新文献_第5页

Predictive modeling of lithium battery capacity loss using electrolyte-cathode parameters and machine learning approaches 使用电解质阴极参数和机器学习方法的锂电池容量损失预测建模

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-28 DOI: 10.1016/j.jpowsour.2026.239355

Anupam Yadav , Mustafa Abdullah , Vivek V , Ibrahim Khersan , Nora rashid najem , Prabhat Kumar Sahu , Joshila Grace , Vikas Wasoom , Abdolali Yarahmadi Kandahari

Lithium-ion battery degradation is strongly influenced by electrolyte–cathode interactions and operational intensity, yet quantitative prediction of its capacity loss remains limited by nonlinear coupling among chemical and physical parameters. This study aims to develop reliable predictive models correlating electrolyte type, temperature (°C), charge rate (C), cathode composition (wt%), and cycle number (n) with measured capacity loss (%), using diverse machine learning (ML) architectures. A compiled dataset of 1200 experimental observations from published studies was standardized, filtered for outliers through leverage analysis, and partitioned with 5-fold cross-validation for robust evaluation. Models including Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP-ANN) were optimized through systematic hyperparameter tuning to minimize rooted mean squared error (RMSE) and improve coefficient of determination (

R^{2}

). CNN achieved the highest test performance (

R^{2} = 0.93

; RMSE ≈ 8; AARE% ≈ 50), followed by SVR and MLP-ANN, confirming their aptitude for nonlinear relation recognition. SHAP analysis revealed that cycle number and temperature exert dominant influence on degradation, while cathode composition, charge rate, and electrolyte type show secondary impacts. These findings establish a mechanistically interpretable, data-driven framework for accurately forecasting electrochemical aging behavior, highlighting the synergistic role of operational stress and material composition in determining lithium battery longevity.

锂离子电池的退化受到电解-阴极相互作用和操作强度的强烈影响，但其容量损失的定量预测仍然受到化学和物理参数之间非线性耦合的限制。本研究旨在开发可靠的预测模型，将电解质类型、温度（°C）、充电速率(C)、阴极成分（wt%）和循环次数(n)与测量的容量损失（%）相关联，使用不同的机器学习（ML）架构。对已发表研究的1200个实验观察数据进行了标准化，通过杠杆分析过滤了异常值，并通过5倍交叉验证进行了分区，以进行稳健评估。对决策树（DT）、AdaBoost （AB）、随机森林（RF）、k近邻（KNN）、支持向量回归（SVR）、卷积神经网络（CNN）和多层感知器（MLP-ANN）等模型进行了系统超参数调优，以最小化均方根误差（RMSE）和提高决定系数（R2）。CNN获得了最高的测试性能（R2=0.93; RMSE≈8;AARE%≈50），其次是SVR和MLP-ANN，证实了它们对非线性关系识别的能力。SHAP分析表明，循环次数和温度是影响降解的主要因素，阴极成分、充电速率和电解质类型次之。这些发现为准确预测电化学老化行为建立了一个机制可解释的数据驱动框架，强调了操作应力和材料成分在决定锂电池寿命方面的协同作用。

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引用次数: 0

Experimental and numerical study of a multi-channel solid oxide fuel cell stack: performance enhancement and compact design 多通道固体氧化物燃料电池堆的实验与数值研究：性能增强与紧凑设计

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-28 DOI: 10.1016/j.jpowsour.2026.239397

Feifan Huang, Xinglong Li, Bin Wang, Tao Li

Achieving high volumetric power density while maintaining thermal stability remains a critical challenge for the commercialization of portable solid oxide fuel cells (SOFCs). In this study, a high-performance multi-channel micro-tubular SOFC (MT-SOFC) stack is developed and systematically optimized through a combined experimental and numerical approach. A robust four-channel anode support with a hierarchical microstructure was fabricated using a phase inversion-assisted extrusion technique. The resulting 4-cell series-connected stack exhibited exceptional low-temperature performance, achieving a peak power density of 1.16 W/cm² at 600 °C and demonstrating stable operation for over 50 h. To address the thermal management issues inherent in high-power density stacks, a validated multi-physics model was employed to optimize the operating parameters. By adopting a co-flow configuration and optimizing reactant flow rates, the average temperature gradient in the active region was reduced by 61.44 % for the single cell and 53.14 % for the stack assembly, significantly mitigating thermal stress risks. Furthermore, a scaling-up analysis performed on a 16-cell stack module revealed that through rational compact design—specifically optimizing the cell spacing to 1.5 times the tube diameter and the cathode length to 3.7 cm—a theoretical volumetric power density of 3.89 W/cm³ can be achieved. This work provides quantitative design guidelines for next-generation compact and thermally robust SOFC stacks.

在保持热稳定性的同时实现高体积功率密度仍然是便携式固体氧化物燃料电池（sofc）商业化的关键挑战。在本研究中，通过实验和数值相结合的方法，开发了高性能多通道微管SOFC （MT-SOFC）堆栈，并对其进行了系统优化。采用相变辅助挤压技术制备了具有分层结构的坚固的四通道阳极支架。由此产生的4电池串联堆叠表现出优异的低温性能，在600°C时达到1.16 W/cm2的峰值功率密度，并表现出超过50小时的稳定运行。为了解决高功率密度堆叠固有的热管理问题，采用了经过验证的多物理场模型来优化工作参数。通过采用共流配置和优化反应物流速，单个电池的平均温度梯度降低了61.44%，堆叠组件的平均温度梯度降低了53.14%，显著降低了热应力风险。此外，对16个电池堆叠模块进行的放大分析表明，通过合理的紧凑设计，特别是优化电池间距到管直径的1.5倍，阴极长度到3.7厘米，可以实现3.89 W/cm3的理论体积功率密度。这项工作为下一代紧凑和热坚固的SOFC堆栈提供了定量设计指南。

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引用次数: 0

The mechanical properties of solid sulfur electrolytes: a local approach for a multiscale design 固体硫电解质的机械性能：多尺度设计的局部方法

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-27 DOI: 10.1016/j.jpowsour.2026.239292

Kethsovann Var , Etienne Barthel , Sofiane Maiza , David Sicsic , Damien Bregiroux , Christel Laberty-Robert

Optimizing solid-state cathodes and electrolytes requires meeting stringent mechanical constraints, which demands precise knowledge of individual component properties. We show that fast, low-load nanoindentation effectively characterizes single particles in air-sensitive solid electrolytes, overcoming atmospheric control challenges. As a case study, argyrodite (Li₆PS₅Cl) pellets were fabricated under varying compaction pressures and particle sizes. Local-scale measurements reveal that smaller or softer particles achieve higher density and smoother surfaces at lower pressures, linking particle-scale mechanics to bulk behavior. These results rationalize macroscopic pellet density and surface quality, providing practical guidance for compaction optimization. Overall, this accessible methodology offers a multiscale framework for designing high-density, high-performance solid electrolytes, enabling informed choices of particle size, compaction pressure, and material properties to enhance the mechanical and electrochemical performance of solid-state batteries.

优化固态阴极和电解质需要满足严格的机械约束，这需要对单个组件的特性有精确的了解。我们展示了快速、低负载的纳米压痕有效地表征了空气敏感固体电解质中的单个颗粒，克服了大气控制的挑战。作为案例研究，在不同的压实压力和粒径下制备了银柱石（Li6PS5Cl）球团。局部尺度的测量表明，更小或更软的颗粒在更低的压力下可以实现更高的密度和更光滑的表面，将颗粒尺度的力学与体行为联系起来。研究结果使颗粒宏观密度和表面质量趋于合理，为压实优化提供了实践指导。总的来说，这种可行的方法为设计高密度、高性能的固体电解质提供了一个多尺度框架，能够明智地选择颗粒尺寸、压实压力和材料特性，从而提高固态电池的机械和电化学性能。

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引用次数: 0

A pressure control algorithm for the hydrogen subsystem of fuel cells based on High Speed on-off Valves 基于高速开关阀的燃料电池氢分系统压力控制算法

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-27 DOI: 10.1016/j.jpowsour.2026.239426

Ziliang Zhao , Yifan Fu , Bin Guo , Jiaming Zhou , Duo Ma , Zhangu Wang , Ji Pu , Jiaping Xie

In order to reduce the production cost of fuel cells, some manufacturers are trying to replace proportional control valves (PCV) with high speed on-off valves (HSV) for hydrogen supply, but it is difficult to achieve precise hydrogen pressure control. This study established a hydrogen pressure control circuit based on HSV, analyzed the working characteristics of the system, introduced current disturbance as a global gain into the state observer and feedback controller, and proposed a global dynamic coordinated control (GDCC) algorithm. Experiments under step and dynamic conditions have shown that GDCC has a faster response speed than traditional Automatic Disturbance Rejection Control (ADRC), and the anti-interference ability of the proposed algorithm has been verified through experiments on external disturbances such as hydrogen discharge valves, hydrogen circulation pumps, temperature, and sensor errors.

为了降低燃料电池的生产成本，一些厂商试图用高速开关阀（HSV）代替比例控制阀（PCV）供氢，但难以实现精确的氢气压力控制。建立了基于HSV的氢气压力控制电路，分析了系统的工作特性，将电流扰动作为全局增益引入状态观测器和反馈控制器，提出了全局动态协调控制（GDCC）算法。阶跃和动态条件下的实验表明，GDCC比传统的自抗扰控制（ADRC）具有更快的响应速度，并通过对氢气排放阀、氢气循环泵、温度和传感器误差等外部干扰的实验验证了该算法的抗干扰能力。

引用次数: 0

Comprehensive feature extraction for battery health prognostics: Identifying predictive indicators of state of health 用于电池健康预测的综合特征提取：识别健康状态的预测指标

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-25 DOI: 10.1016/j.jpowsour.2026.239389

Giovane Ronei Sylvestrin , Joylan Nunes Maciel , Oswaldo Hideo Ando Junior

Accurate estimation of battery state of health (SOH) is crucial for ensuring the safety, reliability, and operational efficiency of energy storage systems in electric vehicles, consumer electronics, and grid applications. Traditional approaches often rely on a limited set of handcrafted features derived from electrochemical analyses, such as incremental capacity, differential voltage, and constant-current/constant-voltage (CC-CV) phases, which restrict their predictive power and generalizability. This study introduces a comprehensive machine learning pipeline for large-scale feature engineering and SOH modeling using only standard sensor data: current, voltage, temperature, and time. Using a public dataset, we generate over 40,000 features across seven domain-informed groups that capture both charge and discharge dynamics. Feature relevance is assessed through univariate analyses (Spearman correlation, Predictive Power Score, and single-feature models) and multivariate modeling within a unified selection pipeline. Prediction targets include remaining useful life (RUL) and future discharge capacity at 10, 50, 100, and 250 cycles ahead. In total, we develop 40 final LightGBM (Light Gradient Boosting Machine) models, spanning the complete feature space and individual feature groups. Model optimization employs a hybrid selection strategy combining SHAP (SHapley Additive exPlanations)-based importance ranking, forward feature selection, and recovery techniques using BorutaShap and minimum redundancy maximum relevance (MRMR). Across all models, 773 unique features are retained, forming a compact yet highly informative subset. The best RUL models achieve a mean absolute percentage error of approximately 10 %, while capacity-forecasting errors remain below 1 % across all prediction horizons. Notably, sliding-window descriptors are frequently retained by the multistage selection pipeline and recurrently appear among the top SHAP contributors in the final models, suggesting that short-term temporal aggregation provides complementary information to single-cycle descriptors. These findings demonstrate that broad and systematic feature exploration, integrated with robust univariate-multivariate selection and interpretable modeling, substantially improves SOH prediction accuracy and generalizability. The proposed framework is scalable and adaptable for data-driven SOH estimation, offering a strong basis for advancing battery diagnostics and prognostics.

准确估计电池健康状态（SOH）对于确保电动汽车、消费电子产品和电网应用中储能系统的安全性、可靠性和运行效率至关重要。传统方法通常依赖于从电化学分析中获得的一组有限的手工特征，如增量容量、差分电压和恒流/恒压（CC-CV）相位，这限制了它们的预测能力和通用性。本研究介绍了一个全面的机器学习管道，用于大规模特征工程和SOH建模，仅使用标准传感器数据：电流、电压、温度和时间。使用公共数据集，我们在七个领域信息组中生成超过40,000个特征，这些特征捕获了充电和放电动态。特征相关性通过单变量分析（Spearman相关性、预测能力评分和单特征模型）和统一选择管道内的多变量建模来评估。预测目标包括剩余使用寿命（RUL）和未来10、50、100和250次循环的放电容量。我们总共开发了40个最终的LightGBM（光梯度增强机）模型，涵盖了完整的特征空间和单个特征组。模型优化采用混合选择策略，结合基于SHapley加性解释（SHapley Additive exPlanations）的重要性排序、前向特征选择以及使用BorutaShap和最小冗余最大相关性（MRMR）的恢复技术。在所有模型中，保留了773个独特的功能，形成了一个紧凑但信息丰富的子集。最佳RUL模型的平均绝对百分比误差约为10%，而在所有预测范围内，能力预测误差保持在1%以下。值得注意的是，滑动窗口描述符经常被多级选择管道保留，并在最终模型中反复出现在SHAP贡献最大的模型中，这表明短期时间聚合为单周期描述符提供了补充信息。这些发现表明，广泛而系统的特征探索，结合稳健的单变量-多变量选择和可解释建模，大大提高了SOH预测的准确性和普遍性。所提出的框架具有可扩展性和适应性，可用于数据驱动的SOH估计，为推进电池诊断和预测提供了坚实的基础。

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引用次数: 0

Inhibiting the shuttle effect in sodium-sulfur batteries using Mo2CT2 (T = S, O) MXenes: A DFT investigation Mo2CT2 (T = S， O) MXenes抑制钠硫电池中穿梭效应的DFT研究

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-25 DOI: 10.1016/j.jpowsour.2026.239407

Anan Udomkijmongkol , Piyaphat Ruttanapunt , Sirinee Thasitha , Iyarat Ounrit , Satchakorn Khammuang , Thanayut Kaewmaraya , Tanveer Hussain , Ralph H. Scheicher , Komsilp Kotmool

The rising demand for electrification has highlighted sodium–sulfur (Na–S) batteries as a promising energy-storage technology due to their high theoretical capacity, abundant materials, and low cost. However, their performance is limited by polysulfide dissolution, or the shuttle effect, which slows redox kinetics and accelerates capacity fading. This study employs the DFT method to investigate Mo₂CT₂ (T = S, O) MXenes in 1T and 2H phases as potential anchoring materials for sulfur cathodes. All Mo₂CT₂ structures effectively adsorb sodium polysulfides (Na₂S_n), demonstrating higher adsorption strength than commercial electrolytes and effectively suppressing the shuttle effect. Structural phase notably affects Na₂S_n adsorption on Mo₂CS₂, while its influence is minor for Mo₂CO₂. Higher Na₂S_n-Mo₂CO₂ interaction arises from greater charge transfer from Na to O atom driven by higher electronegativity difference. Among the candidates, 2H-Mo₂CS₂ and 1T-Mo₂CO₂ exhibit higher binding energies than its counterpart and maintain metallic conductivity after Na₂S_n adsorption, benefiting electron transport. Gibbs free energy calculations indicate more favorable sulfur reduction pathways on Mo₂CT₂ surfaces, along with reduced energy barriers for Na₂S oxidation. Overall, Mo₂CT₂ MXenes exhibit strong anchoring capability and catalytic activity, making them promising materials for mitigating the shuttle effect and enhancing electrochemical performance in Na–S batteries.

随着电气化需求的不断增长，钠硫电池（Na-S）因其理论容量高、材料丰富、成本低而成为一种有前途的储能技术。然而，它们的性能受到多硫化物溶解或穿梭效应的限制，这会减慢氧化还原动力学并加速容量褪色。本研究采用DFT方法研究了1T和2H相的Mo2CT2 (T = S， O) MXenes作为硫阴极的潜在锚定材料。所有Mo2CT2结构均能有效吸附多硫化钠（Na2Sn），表现出比商用电解质更高的吸附强度，并能有效抑制穿梭效应。结构相对Mo2CS2吸附Na2Sn的影响较大，对Mo2CO2的影响较小。Na2Sn-Mo2CO2相互作用的增强是由于电负性差的增大导致Na原子向O原子的电荷转移增大。其中，2H-Mo2CS2和1T-Mo2CO2具有较高的结合能，吸附Na2Sn后保持金属导电性，有利于电子传递。Gibbs自由能计算表明Mo2CT2表面更有利的硫还原途径，以及Na2S氧化的能量垒降低。总体而言，Mo2CT2 MXenes具有较强的锚定能力和催化活性，是一种很有前景的材料，可以减轻Na-S电池的穿梭效应，提高电化学性能。

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引用次数: 0

Designing boron and hydroxyl-functionalized 3D crosslinked binder for enhancing high-energy-density silicon anode of lithium-ion batteries 设计硼羟基功能化三维交联粘结剂增强锂离子电池高能量密度硅负极

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-24 DOI: 10.1016/j.jpowsour.2026.239360

Shinyoung Lee , Ki-Yong Yoon , Jongha Hwang , Haeyoung Lee , Jeonghun Kim , Chang-Min Yoon , Woo-Jin Song

Silicon (Si) is a candidate anode for high-energy-density lithium-ion batteries due to its higher theoretical capacity (4200 mAh g⁻¹) than graphite (372 mAh g⁻¹). However, Si suffers rapid capacity fading owing to large volume changes and excessive solid electrolyte interphase (SEI) layer during repeated lithiation/delithiation. The design of the binder plays an important role in preventing such volume changes over a long cycle life. This study uses a crosslinked polymer composed of poly(acrylic acid) (PAA) and boric acid (BA) with the electron-deficient boron with the remaining hydroxyl groups (PBH) as a Si-based anode binder. The crosslinked PBH binder provides a robust electrode with improved adhesive strength at an optimized ratio, which effectively fixes Si particles and forms a LiF-rich SEI that can promote rigidity and ion conduction while suppressing the volume expansion of Si. The Si anode with the PBH binder achieves a high capacity of 2275 mAh g⁻¹ after 150 cycles, with an average Coulombic efficiency of 98.8 % and high-capacity retention of 88.85 %. A LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622)||Si cell is shown to withstand 100 cycles and exhibit improved retention. The crosslinked polymer derived from electron-deficient components is a promising Si binder for high-energy and high-stability lithium-ion batteries.

硅（Si）是高能量密度锂离子电池的候选阳极，因为它的理论容量（4200 mAh g - 1）比石墨（372 mAh g - 1）更高。然而，在重复锂化/去锂化过程中，由于体积变化大和固体电解质间相（SEI）层过多，硅的容量衰减迅速。粘合剂的设计在长循环寿命期间防止这种体积变化方面起着重要作用。本研究采用一种由聚丙烯酸（PAA）和硼酸（BA）组成的交联聚合物，外加缺电子的硼和剩余的羟基（PBH）作为硅基阳极粘合剂。交联PBH粘结剂提供了一个坚固的电极，以优化的比例提高了粘合强度，有效地固定了Si颗粒，形成了富liff的SEI，可以提高刚度和离子传导，同时抑制Si的体积膨胀。采用PBH粘结剂的硅阳极在循环150次后获得2275 mAh g−1的高容量，平均库仑效率为98.8%，高容量保持率为88.85%。LiNi0.6Co0.2Mn0.2O2 (NCM622)||硅电池可承受100次循环，并具有更好的保留性能。这种由缺电子组分衍生的交联聚合物是一种很有前途的高能高稳定性锂离子电池硅粘合剂。

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引用次数: 0

Exploring the impact of dispersing BiVO4 microcubes in a chitosan-blended dendritic polymer for sustainable electrochemical energy conversion and storage applications 探索分散BiVO4微立方体在壳聚糖混合枝状聚合物中对可持续电化学能量转换和存储应用的影响

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-24 DOI: 10.1016/j.jpowsour.2026.239436

Egambaram Murugan , Kesava Munusamy , Arumugam Poongan , Vinitha Annachi , Kaviya Nissi Darwin

Cost-effective polymer composite membranes (PCMs) with high electrochemical performance are critical for energy conversion and storage technologies. Herein, chitosan–dendritic polyamidoamine (Cs–DPA) composite membranes incorporating BiVO₄ microcubes (MCs) (1–7 wt%) were fabricated via solvent casting and evaluated for high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) and supercapacitors. The phosphoric-acid-doped Cs–DPA membrane containing 5 wt% BiVO₄ MCs exhibited the highest proton conductivity (7.86 × 10⁻² S cm⁻¹), significantly exceeding that of pristine Cs–DPA (5.62 × 10⁻² S cm⁻¹). Moreover, the corresponding composite electrode delivered a high specific capacitance of 895.65 F g⁻¹ at 1 A g⁻¹, significantly outperforming the bare Cs–DPA (325 F g⁻¹). The enhanced performance is attributed to improved filler dispersion and interfacial Lewis acid–base and hydrogen-bonding interactions that facilitate proton and charge transport. These results demonstrate the potential of BiVO₄ MCs-incorporated Cs–DPA PCMs for HT-PEMFC and supercapacitor applications.

具有高电化学性能的高性价比聚合物复合膜是能量转换和存储技术的重要组成部分。本文采用溶剂浇铸法制备了含有BiVO4微立方（MCs）（1-7 wt%）的壳聚糖-树突状聚酰胺胺（Cs-DPA）复合膜，并对其用于高温聚合物电解质膜燃料电池（ht - pemfc）和超级电容器进行了研究。含有5 wt% BiVO4 MCs的磷酸掺杂Cs-DPA膜具有最高的质子电导率（7.86 × 10−2 S cm−1），显著超过原始Cs-DPA膜的质子电导率（5.62 × 10−2 S cm−1）。此外，相应的复合电极在1 a g−1时提供了895.65 F g−1的高比电容，显著优于裸Cs-DPA （325 F g−1）。增强的性能归因于填料分散性和界面路易斯酸碱和氢键相互作用的改善，促进了质子和电荷的传输。这些结果证明了BiVO4 mc - Cs-DPA pcm在HT-PEMFC和超级电容器应用中的潜力。

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引用次数: 0

Battery-aging-aware co-optimization of adaptive cruise control and hybrid electric vehicle energy management: A constrained hybrid-action reinforcement learning approach 电池老化感知自适应巡航控制和混合动力汽车能量管理的协同优化：一种约束混合行动强化学习方法

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-24 DOI: 10.1016/j.jpowsour.2026.239432

Jinming Xu , Nasser Lashgarian Azad , Zheng Chen , Yuan Lin

Integrating adaptive cruise control (ACC) and hybrid electric vehicle (HEV) energy management is vital for enhancing fuel efficiency. However, existing co-optimization strategies often neglect battery degradation and struggle to enforce critical safety and operational constraints. This paper introduces a novel constrained hybrid-action reinforcement learning algorithm, named parameterized proximal policy optimization with Lagrangian method (PAPPOLag), to address these challenges. The proposed method co-optimizes the ACC and HEV energy management system (EMS) by incorporating a battery aging model to extend battery life. It employs a Lagrangian multiplier to explicitly handle constraints such as safe following distance, powertrain limits, and battery state of charge. The policy also manages a hybrid-action space, concurrently optimizing discrete gear shifts and continuous acceleration and torque commands. Comparative analysis demonstrates that PAPPOLag achieves fuel economy within 9.9% of the near-optimal offline benchmark combining a model predictive control-based ACC with a dynamic programming-based EMS while operating nearly three orders of magnitude faster. The algorithm demonstrates superior safety, maintaining a 100% safety rate in critical cut-in scenarios where its unconstrained counterpart failed over 31% of the time. The results confirm a trade-off wherein a 4.91% increase in fuel consumption corresponds to a 33.48% reduction in battery aging.

将自适应巡航控制（ACC）与混合动力汽车（HEV）的能量管理相结合是提高燃油效率的关键。然而，现有的协同优化策略往往忽略了电池的退化，并且难以执行关键的安全和操作约束。为了解决这些问题，本文提出了一种新的约束混合动作强化学习算法——参数化拉格朗日方法近端策略优化算法（PAPPOLag）。该方法通过引入电池老化模型，对ACC和HEV能量管理系统（EMS）进行协同优化，延长电池寿命。它采用拉格朗日乘法器来明确处理诸如安全跟随距离、动力系统限制和电池充电状态等约束。该策略还管理混合动力空间，同时优化离散换挡和连续加速和扭矩命令。对比分析表明，结合基于模型预测控制的ACC和基于动态规划的EMS， PAPPOLag在接近最优离线基准的9.9%范围内实现了燃油经济性，同时运行速度快了近三个数量级。该算法表现出卓越的安全性，在无约束算法失败率超过31%的关键切入场景中保持100%的安全性。结果证实了一种权衡，即燃料消耗增加4.91%对应于电池老化减少33.48%。

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引用次数: 0

Hybrid semi-IPN ion-solvating membranes combining high hydroxide conductivity and structural stability for alkaline water electrolysis 混合半ipn离子溶剂化膜，具有高氢氧化物导电性和结构稳定性，用于碱性水电解

IF 7.9 2区工程技术 Q1 CHEMISTRY, PHYSICAL

Journal of Power Sources

Pub Date : 2026-01-24 DOI: 10.1016/j.jpowsour.2026.239425

Loubna Ahsaini, Mina Jellab, Mustapha Matrouf, Fatima-Zahra Semlali, Fouad Ghamouss

This study reports hybrid semi-interpenetrating polymer network (semi-IPN) membranes based on poly (vinyl alcohol) (PVA) crosslinked with glutaraldehyde (GA), physically entangled with poly (vinylpyrrolidone) (PVP), and reinforced with silica derived from tetraethyl orthosilicate (TEOS). The membranes were fabricated by solvent casting, followed by thermal treatment and alkaline activation with KOH to enable hydroxide ion solvation. The GA content (0–0.5 mL) was systematically varied to control the crosslink density and membrane microstructure. Low GA contents produced loosely organized networks, whereas excessive GA led to heterogeneous structures. An optimal composition at 0.4 mL GA yielded a dense semi-IPN structure with low porosity (3.98 %) and excellent mechanical properties (+1972 % rigidity and +2117 % hardness vs pristine PVA). The optimized membrane exhibited good oxidative stability (82.35 % mass retention), strong alkaline resistance (1–2 wt.% mass loss after exposure to 4–6 M KOH), and negligible methanol permeability, indicating a compact and highly selective architecture. A high hydroxide conductivity of 195.9 mS cm⁻¹ was achieved at 80 °C under ion-solvating conditions, exceeding values typically reported for many polymeric alkaline membranes. Single-cell alkaline water electrolysis tests further demonstrated reduced operating voltages and stable performance compared with a commercial reference membrane, confirming the effective translation of material-level advantages into practical device operation.

本研究报道了基于聚乙烯醇（PVA）与戊二醛（GA）交联、聚乙烯基吡罗烷酮（PVP）物理缠结、正硅酸四乙酯（TEOS）衍生二氧化硅增强的杂化半互穿聚合物网络（semi-IPN）膜。采用溶剂铸造法制备膜，然后进行热处理和KOH碱性活化，使氢氧根离子溶剂化。系统地改变GA含量（0-0.5 mL），以控制交联密度和膜微观结构。低GA含量产生松散组织的网络，而过多的GA导致异质结构。在0.4 mL GA条件下，得到了致密的半ipn结构，具有低孔隙率（3.98%）和优异的力学性能（与原始PVA相比，刚性+1972 %，硬度+ 2117%）。优化后的膜具有良好的氧化稳定性（82.35%的质量保持率），较强的耐碱性（暴露于4-6 M KOH后质量损失1-2 wt.%），以及可忽略的甲醇渗透性，表明其结构紧凑且具有高选择性。在离子溶剂化条件下，在80°C下获得了195.9 mS cm−1的高氢氧化物电导率，超过了许多聚合物碱性膜通常报道的值。与商业参考膜相比，单细胞碱性电解测试进一步证明了工作电压降低和性能稳定，证实了材料级优势有效转化为实际设备操作。

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