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学者姓名：欧凯
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Abstract ：
The main contribution of this study is to introduce deep reinforcement learning (DRL) within the model prediction control (MPC) framework, and consider comprehensive economic objectives including fuel cell degradation costs, lithium battery aging costs, hydrogen consumption costs, etc. This approach successfully mitigated the inherent shortcomings of deep reinforcement learning, namely poor generalization and lack of adaptability, thereby significantly enhancing the robustness of economic driving decision in unknown scenarios. In this study, an MPC framework was developed for the energy management problem of fuel cell vehicles, and Bi-directional Long Short-Term Memory (Bi-LSTM) neural network was used to construct a vehicle speed predictor The accuracy of its prediction was verified through comparative analysis, and then it was regarded as a DRL model. Different from the overall strategy of the entire driving cycle, the model based DRL agent can learn the optimal action for each vehicle state. The simulation evaluated the impact of different predictors and prediction ranges on hydrogen economy, and verified the adaptability of the proposed strategy in different driving environments, the stability of battery state maintenance, and the advantages of delaying energy system degradation through comprehensive comparative analysis. © 2024
Keyword ：
Automobile drivers Automobile drivers Brain Brain Costs Costs Energy management Energy management Forecasting Forecasting Fuel cells Fuel cells Fuel economy Fuel economy Hybrid vehicles Hybrid vehicles Hydrogen fuels Hydrogen fuels Learning systems Learning systems Lithium batteries Lithium batteries Long short-term memory Long short-term memory Model predictive control Model predictive control Predictive control systems Predictive control systems Reinforcement learning Reinforcement learning
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| GB/T 7714 | Huang, Xuejin , Zhang, Jingyi , Ou, Kai et al. Deep reinforcement learning-based health-conscious energy management for fuel cell hybrid electric vehicles in model predictive control framework [J]. | Energy , 2024 , 304 . |
| MLA | Huang, Xuejin et al. "Deep reinforcement learning-based health-conscious energy management for fuel cell hybrid electric vehicles in model predictive control framework" . | Energy 304 (2024) . |
| APA | Huang, Xuejin , Zhang, Jingyi , Ou, Kai , Huang, Yin , Kang, Zehao , Mao, Xuping et al. Deep reinforcement learning-based health-conscious energy management for fuel cell hybrid electric vehicles in model predictive control framework . | Energy , 2024 , 304 . |
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The optimal capacity of energy storage facilities is a cornerstone for the investment and low-carbon operation of integrated energy systems (IESs). However, the intermittence of renewable energy and the different operating characteristics of facilities present challenges to IES configuration. Therefore, a two-stage decision-making framework is developed to optimize the capacity of facilities for six schemes comprised of battery energy storage systems and hydrogen energy storage systems. The objectives considered are to minimize the levelized cost of electricity (LCOE), power abandonment rate (PAR) and maximize self-sufficiency rate (SSR) simultaneously. In the first stage, each scheme is solved using NSGA-II. In the second stage, the weights of objective function are determined by entropy weight method, while the optimal individual is selected from the Pareto solutions by the technique for order preference by similarity to ideal solution approach. Life models of battery, fuel cell, and electrolyzer are introduced to quantify device replacement costs. Meanwhile, carbon trading mechanisms and time-of-use tariffs are considered to assess environmental and economic benefits. The results show that the hydrogen-electric coupling scheme demonstrated superior performance, with LCOE, SSR, and PAR of 0.6416 ¥/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR. © 2024 Elsevier Ltd
Keyword ：
Battery storage Battery storage Carbon Carbon Decision making Decision making Entropy Entropy Fuel cells Fuel cells Genetic algorithms Genetic algorithms Hydrogen storage Hydrogen storage Investments Investments
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| GB/T 7714 | Lin, Liangguang , Ou, Kai , Lin, Qiongbin et al. Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system [J]. | Journal of Energy Storage , 2024 , 97 . |
| MLA | Lin, Liangguang et al. "Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system" . | Journal of Energy Storage 97 (2024) . |
| APA | Lin, Liangguang , Ou, Kai , Lin, Qiongbin , Xing, Jianwu , Wang, Ya-Xiong . Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system . | Journal of Energy Storage , 2024 , 97 . |
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The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial for health management and diagnosis. However, most data-driven estimation methods heavily rely on scarce labeled data, while traditional transfer learning faces challenges in handling domain shifts across various battery types. This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries. A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention. Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains, providing a superior starting point for fine-tuning the target domain model. Subsequently, the abundant aging data of the same type as the target battery are labeled through semi-supervised learning, compensating for the source model's limitations in capturing target battery aging characteristics. Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model. In particular, across the experimental groups 13–15 for different types of batteries, the root mean square error of SOH estimation was less than 0.66 %, and the mean relative error of RUL estimation was 3.86 %. Leveraging extensive unlabeled aging data, the proposed method could achieve accurate estimation of SOH and RUL. © 2024 The Authors
Keyword ：
Depth-wise separable convolutional vision-transformer Depth-wise separable convolutional vision-transformer Maximum mean discrepancy Maximum mean discrepancy Remaining useful life (RUL) Remaining useful life (RUL) Semi-supervised learning Semi-supervised learning State of health (SOH) State of health (SOH) Transfer learning Transfer learning
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| GB/T 7714 | Wang, Y.-X. , Zhao, S. , Wang, S. et al. Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries [J]. | Energy and AI , 2024 , 17 . |
| MLA | Wang, Y.-X. et al. "Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries" . | Energy and AI 17 (2024) . |
| APA | Wang, Y.-X. , Zhao, S. , Wang, S. , Ou, K. , Zhang, J. . Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries . | Energy and AI , 2024 , 17 . |
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在"双碳"背景下,电化学储能成本低,商业化应用日渐成熟,其技术优势愈发明显,发展前景广阔.立足福建省自身储能电池产业基础和发展格局,分析福建省储能电池产业发展存在的问题,针对性地提出福建省锂离子电池及钠离子电池产业技术路线和创新路径,以期进一步巩固储能电池产业基础,助力福建省储能电池产业高质量发展.
Keyword ：
储能电池产业 储能电池产业 创新发展 创新发展 技术路线 技术路线 福建 福建
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| GB/T 7714 | 欧凯 , 李雨 , 吴川 et al. 福建省储能电池产业技术路线与创新路径 [J]. | 学会 , 2023 , (4) : 28-33 . |
| MLA | 欧凯 et al. "福建省储能电池产业技术路线与创新路径" . | 学会 4 (2023) : 28-33 . |
| APA | 欧凯 , 李雨 , 吴川 , 王亚雄 . 福建省储能电池产业技术路线与创新路径 . | 学会 , 2023 , (4) , 28-33 . |
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随着全球新能源大规模装机及对能源消纳和电力供需要求的不断提高,电化学储能应用日益增多.该文通过梳理分析福建省储能电池产业基础和发展现状,分析了福建省储能电池产业发展中存在的问题,针对性地提出福建省储能电池产业的发展建议,旨在进一步巩固储能电池产业基础,助力福建省储能电池产业高质量发展.
Keyword ：
储能电池产业 储能电池产业 现状 现状 问题 问题 高质量发展 高质量发展
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| GB/T 7714 | 吴锋 , 吴川 , 王亚雄 et al. 推动福建省储能电池产业创新发展的对策建议 [J]. | 学会 , 2023 , (6) : 32-35 . |
| MLA | 吴锋 et al. "推动福建省储能电池产业创新发展的对策建议" . | 学会 6 (2023) : 32-35 . |
| APA | 吴锋 , 吴川 , 王亚雄 , 李雨 , 欧凯 . 推动福建省储能电池产业创新发展的对策建议 . | 学会 , 2023 , (6) , 32-35 . |
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Regulating the air supply is crucial for high efficiency and reliable operation of proton exchange membrane fuel cell systems (PEMFCs). In this study, a fused multi-model predictive control (FM-MPC) with an adaptive compensation is proposed for the oxygen excess ratio (OER) regulation of the air supply system. The FM-MPC is designed based on the linearized PEMFC model at low and high power phases, with two linear MPCs imple-mented and combined using adaptive featured weights. An adaptive compensation strategy is created to address the imbalance of the two MPCs and external load disturbances. The stability of the proposed control is analyzed using Lyapunov's second law. Simulation results demonstrate that the proposed method exhibits less overshoot and faster response than conventional MPCs, with the OER total sum-of-squares error (TSSE) reduced by 59.4% and 87.7% for New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) con-ditions, respectively. Finally, a Hardware-In-the-Loop (HIL) experiment verifies the real-time application po-tential of the proposed controller, with a mean relative error (MRE) of 1.12% between experiment and simulation.
Keyword ：
Adaptive multivariable compensation strategy Adaptive multivariable compensation strategy Air supply system Air supply system Lyapunov stability analysis Lyapunov stability analysis Model predictive control Model predictive control Oxygen excess ratio Oxygen excess ratio Proton exchange membrane fuel cell (PEMFC) Proton exchange membrane fuel cell (PEMFC)
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| GB/T 7714 | Hu, Haowen , Ou, Kai , Yuan, Wei-Wei . Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system [J]. | ENERGY , 2023 , 284 . |
| MLA | Hu, Haowen et al. "Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system" . | ENERGY 284 (2023) . |
| APA | Hu, Haowen , Ou, Kai , Yuan, Wei-Wei . Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system . | ENERGY , 2023 , 284 . |
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An effective energy management strategy (EMS) is crucial for the reliable operation of fuel cell hybrid electric vehicles (FCHEVs). This study proposes a power distribution optimization strategy for FCHEVs that leverages deep reinforcement learning (DRL) and Pontryagin's minimum principle (PMP). The DRL algorithm effectively balances fuel economy, battery durability, and fuel cell durability objectives. The degradation mechanisms of battery and fuel cell under extreme working conditions are considered in the PMP optimization. A comprehensive evaluation framework is established with degradation and energy consumption models to serve as a reward for deep reinforcement learning to balance fuel economy and power sources' lifetime. Simulation results show that the proposed EMS framework reduces FC degradation by 18.4% and battery degradation by 71.1% compared to traditional PMP-based EMS under the NEDC driving condition. Hardware-in-the-loop (HIL) testing demonstrates that the proposed EMS framework has the potential for real-time application, with an average relative error between experiment and simulation of approximately 0.0203. This research highlights the significance of the proposed EMS framework in ensuring the reliable operation of FCHEVs with enhanced performance and reduced cost.
Keyword ：
Battery degradation Battery degradation Deep reinforcement learning Deep reinforcement learning Energy management strategy (EMS) Energy management strategy (EMS) Fuel cell degradation Fuel cell degradation Fuel economy Fuel economy Overall cost Overall cost
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| GB/T 7714 | Hu, Haowen , Yuan, Wei-Wei , Su, Minghang et al. Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems [J]. | ENERGY CONVERSION AND MANAGEMENT , 2023 , 291 . |
| MLA | Hu, Haowen et al. "Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems" . | ENERGY CONVERSION AND MANAGEMENT 291 (2023) . |
| APA | Hu, Haowen , Yuan, Wei-Wei , Su, Minghang , Ou, Kai . Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems . | ENERGY CONVERSION AND MANAGEMENT , 2023 , 291 . |
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本实用新型涉及一种燃料电池空压机级间冷却结构，包括中空的壳体，壳体的两相对端面上同轴开设有用以级间管路穿设的通口，壳体的下方一端设置有进水口，上方另一端设置有出水口，进水口与出水口之间的壳体内部沿级间管路轴向分布有若干片上半圆环形折流板与下半圆环形折流板，若干片上半圆环形折流板与下半圆环形折流板间隔交错设置。半圆环形折流板的设置便于安装在级间管路上，提高了壳程内冷却液的流速并增加湍动程度，减少结垢，提高传热效率，增大壳程冷却液的传热系数，使级间管路内气体得到充分冷却，提高压缩效率，同时起到支撑壳体的作用，适用于市面上已经存在的两级增压空压机，可以对已经存在的空压机进行安装此装置，普适性强。
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| GB/T 7714 | 王亚雄 , 张志宏 , 欧凯 et al. 一种燃料电池空压机级间冷却结构 : CN202321309673.6[P]. | 2023-05-27 00:00:00 . |
| MLA | 王亚雄 et al. "一种燃料电池空压机级间冷却结构" : CN202321309673.6. | 2023-05-27 00:00:00 . |
| APA | 王亚雄 , 张志宏 , 欧凯 , 蔡泽南 . 一种燃料电池空压机级间冷却结构 : CN202321309673.6. | 2023-05-27 00:00:00 . |
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本实用新型涉及一种用于快拆式便携电源的电池包结构，包括电池组模块、电池包外壳、电池包上盖、散热风扇、接线端子、第一散热翅片、第二散热翅片和集成电路板；第一散热翅片设置在电源组模块上方，第二散热翅片设置在集成电路板的下方，集成电路板安装在电池包外壳内底部，电池组模块放置在电池包外壳内且位于集成电路板上侧，电源组模块与集成电路板电连接；电池包外壳上具有上部敞口，电池包上盖与上部敞口配合连接；接线端子安装在电池包外壳上，并与电池组模块电连接；电池包外壳上设有进风口和出风口，进风口和出风口上安装散热风扇。该电池包结构采用模块化设计，可方便快捷地安装在快拆式便携电源上及从其上拆下更换，且散热效果好。
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| GB/T 7714 | 欧凯 , 许宏伟 , 刘锦航 et al. 一种用于快拆式便携电源的电池包结构 : CN202321597098.4[P]. | 2023-06-21 00:00:00 . |
| MLA | 欧凯 et al. "一种用于快拆式便携电源的电池包结构" : CN202321597098.4. | 2023-06-21 00:00:00 . |
| APA | 欧凯 , 许宏伟 , 刘锦航 , 王淞任 . 一种用于快拆式便携电源的电池包结构 : CN202321597098.4. | 2023-06-21 00:00:00 . |
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本实用新型涉及一种快拆式便携电源，包括铝合金外壳、抽拉式更替电池箱、上下隔板、逆变器及控制电路板集成模块、取电公头、电源插座、铝合金镂空侧板、显示器控制端前面板和提手；抽拉式更替电池箱、上下隔板、集成模块自下而上安装在铝合金外壳内；铝合金外壳的右侧具有取出抽拉式更替电池箱的侧向敞口，铝合金镂空侧板与侧向敞口配合连接；取电公头安装在铝合金外壳内的左侧，抽拉式更替电池箱的左侧嵌设有供电母头，供电母头与取电公头配合连接，取电公头的输出端与集成模块电连接；电源插座安装在铝合金外壳的左侧，并与集成模块电连接。该快拆式便携电源结构简单，设计合理，可以方便快捷地进行内部电源的拆装更换，使用效果好。
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| GB/T 7714 | 欧凯 , 王淞任 , 刘锦航 et al. 一种快拆式便携电源 : CN202321597126.2[P]. | 2023-06-21 00:00:00 . |
| MLA | 欧凯 et al. "一种快拆式便携电源" : CN202321597126.2. | 2023-06-21 00:00:00 . |
| APA | 欧凯 , 王淞任 , 刘锦航 , 许宏伟 . 一种快拆式便携电源 : CN202321597126.2. | 2023-06-21 00:00:00 . |
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