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Journal of Wuhan University(Natural Science Edition)

Journal of Wuhan University(Natural Science Edition) Journal of Wuhan University(Natural Science Edition)

Impact Factor:1.821(CNKI)

ISSN:1671-8836

CN:42-1674/N

Publication frequency:bimonthly

Tel.:027-68756952

E-mail:whdz@whu.edu.cn

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Volume 71 期 6，2025 2025年第71卷第6期
    “在人工智能领域，专家建立了深度学习体系，为智能技术发展提供新方向。”

    Wang Shunli

    DOI：10.14188/j.1671-8836.2025.0180
      
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    “在新型储能技术领域，我国研究聚焦工程实践和政策导向，国际研究注重材料机理与理论创新，电池储能研究热度最高，我国正迈向系统协同与智能化发展阶段。”

    WANG Shunli, CHENG Liangwei, ZHOU Lei, ZHANG Liya, HUO Yuchen

    DOI：10.14188/j.1671-8836.2025.0142
    摘要：To reveal the research landscape and development trajectories of typical emerging energy storage technologies in China, this study focuses on five representative types: battery energy storage, molten salt energy storage, compressed air energy storage, flywheel energy storage, and hydrogen energy storage. A comparative bibliometric analysis was conducted based on core literature from the CNKI and Web of Science (WOS) databases during 2015—2025. The results indicate that, overall, domestic research is characterized by engineering practice and policy orientation, whereas international studies emphasize material mechanisms and theoretical innovation, reflecting a differentiated evolution of research pathways. Among them, battery energy storage exhibits the highest research intensity: domestic studies focus on state-of-charge (SOC) estimation, safety management, and system operation, while international research concentrates on solid electrolytes and post-lithium material systems. Molten salt energy storage has evolved from solar-thermal coupling toward multi-temperature thermochemical applications, emphasizing heat transfer efficiency and thermal performance improvement. Compressed air energy storage research centers on thermo-hydro-mechanical modeling and heat recovery optimization, showing strong potential for long-duration peak regulation. Flywheel energy storage has shifted from structural and control optimization to system integration and inertia support, and hydrogen energy storage demonstrates multi-energy coupling among wind, solar, and hydrogen systems, highlighting progress in hydrogen production efficiency. Overall, China’s research on emerging energy storage technologies is transitioning from single-technology breakthroughs toward system-level synergy and intelligent development. The complementary relationship between short-term power-type and long-duration capacity-type storage technologies is becoming clearer, with future progress expected to move toward higher safety, lower cost, longer lifespan, and multi-energy integration-providing critical support for the construction of a new-type power system.  
    关键词：emerging energy storage;battery energy storage;molten salt energy storage;compressed air energy storage;flywheel energy storage;hydrogen energy storage;bibliometric analysis   
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    “最新研究提出一种结合物理模型和深度学习的锂离子电池荷电状态估计方法，有效提升模型在复杂工况下的建模能力，实验结果表明具有高预测精度和鲁棒性。”

    CHEN Yuan, LI Jiale, LIU Yanzhong, HE Yigang

    DOI：10.14188/j.1671-8836.2025.0143
    摘要：Accurate estimation of the State of Charge (SOC) is fundamental to ensure the reliable operation of lithium-ion batteries. To address the issue of insufficient input features in existing deep learning methods, this paper proposes an SOC estimation method based on a combination of a physical model and a deep learning algorithm. This method exploits the local feature extraction capability of the Convolutional Neural Network (CNN) and the temporal sequence processing ability of the Bi-directional Gated Recurrent Unit (BiGRU). By introducing the terminal voltage output from a first-order Resistor-Capacitor （RC） model as an input feature, which is combined with the measured voltage and current to form the neural network input, the modeling capability of the CNN-BiGRU under complex dynamic operating conditions is enhanced. Experimental results demonstrate the good SOC estimation performance of the CNN-BiGRU model. For the Center for Advanced Life Cycle Engineering （CALCE) dataset of University of Maryland, the root mean square error (RMSE) is 0.16% and the mean absolute error (MAE) is 0.12% at room temperature (25 ℃). Furthermore, the proposed model exhibits high prediction accuracy and robustness for lithium-ion batteries under varying ambient temperatures and different degradation levels.  
    关键词：lithium-ion battery;SOC estimation;physical model;CNN-BiGRU model   
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    “在电池管理领域，研究人员提出了一种基于CL-NARX神经网络的SOC估计模型，有效提高了估计精度和鲁棒性，为锂离子电池安全运行提供技术支持。”

    WANG Shunli, WANG Junkai, ZHANG Liya, LI Huan

    DOI：10.14188/j.1671-8836.2025.0123
    摘要：To address the problems of insufficient estimation accuracy, error accumulation, and poor robustness of traditional State of Charge (SOC) estimation models under actual operating conditions, this paper proposes a Closed-loop Nonlinear Autoregressive eXogenous (CL-NARX) neural network model to improve SOC estimation accuracy. The model enhances the fitting capability for complex battery processes by introducing a closed-loop feedback mechanism, effectively suppressing error accumulation, and strengthening robustness by optimizing key hyperparameters. The experimental results show that the model achieves optimal performance, when the training iteration number is 150, the number of neurons in the hidden layer is 10, the input delay layers are 5, and the output delay layers are 2, with estimation errors significantly superior to other neural network models. The maximum error, RMSE, MAE, and MAPE are reduced to 2.58%, 1.41%, 1.36%, and 4.57%, respectively. The model demonstrates high accuracy, effective error handling, and strong robustness, providing reliable technical support for the safe operation of lithium-ion batteries.  
    关键词：lithium-ion battery;State of Charge (SOC);Closed-Loop Nonlinear Autoregressive eXogenous (CL-NARX);backpropagation (BP) neural network   
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