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学者姓名：欧凯
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Abstract ：
从性能需求与技术现状等角度,综述了载运工具用燃料电池空气压缩机的研究进展,总结了离心式空气压缩机的关键部件参数优化设计、机电耦合控制、加工制造和性能测试等技术,并展望了燃料电池空气压缩机技术未来的发展方向.研究结果表明:燃料电池空气压缩机需满足大流量与快速响应等要求;当前,两级离心式空气压缩机流量与压力等特性可满足5～350 kW燃料电池系统供氧需求,最高转速可达1.0×10 5 r·min-1,零转速到怠速的响应时间为秒级;叶轮、扩压器、箔片气体动压轴承等关键部件的参数可采用优化算法进行设计以提高空气压缩机气动性能;为降低驱动电机的转速与转矩波动,离心式空气压缩机机电耦合控制可采用电流环解耦控制和无传感控制等方法以提高空气压缩机的动态响应能力;为保证离心式空气压缩机高速运转下的气动性能和系统稳定性,高精度三元叶轮加工主要通过五轴数控机床铣削实现,箔片气体动压轴承的涂层常采用固体润滑与等离子喷射工艺;燃料电池空气压缩机还需开展流量、压比、效率等特性与启停、寿命等耐久性的指标测试以综合评价其性能;目前,空气压缩机气动性能测试标准与试验方法较为完备,但耐久性相关的测试和评价方法还需进一步完善;未来,随着对可持续交通解决方案需求的不断增长,载运工具用燃料电池空气压缩机技术将朝着集成轻量化与智能化等方向发展.
Keyword ：
优化设计 优化设计 机电耦合控制 机电耦合控制 燃料电池 燃料电池 空气压缩机 空气压缩机 载运工具 载运工具 需求分析 需求分析
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| GB/T 7714 | 欧凯 , 胡皓文 , 吴雨衡 et al. 载运工具用燃料电池空气压缩机技术综述 [J]. | 交通运输工程学报 , 2025 , 25 (1) : 66-93 . |
| MLA | 欧凯 et al. "载运工具用燃料电池空气压缩机技术综述" . | 交通运输工程学报 25 . 1 (2025) : 66-93 . |
| APA | 欧凯 , 胡皓文 , 吴雨衡 , 郭轩 , 杨新荣 , 张前 et al. 载运工具用燃料电池空气压缩机技术综述 . | 交通运输工程学报 , 2025 , 25 (1) , 66-93 . |
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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 state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries affect their operating performance and safety. The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging. This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health. The battery model is formulated across temperatures and aging, which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information. The open-circuit voltages (OCVs) are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows. Arrhenius equation is combined with estimated SOH for temperature-aging migration. A novel transformer model is introduced, which integrates multiscale attention with the transformer’s encoder to incorporate SOC-voltage differential derived from battery model. This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution. By leveraging multi-head attention, the model establishes information dependency relationships across different aging levels, enabling rapid and precise SOH estimation. Specifically, the root mean square error for SOC and SOH under conditions of 15 °C dynamic stress test and 25 °C constant current cycling was less than 0.9% and 0.8%, respectively. Notably, the proposed method exhibits excellent adaptability to varying temperature and aging conditions, accurately estimating SOC and SOH. Graphical Abstract: (Figure presented.) © Youke Publishing Co.,Ltd 2024.
Keyword ：
Aging migration Aging migration Global correction Global correction Multiscale attention Multiscale attention State-of-charge (SOC) State-of-charge (SOC) State-of-health (SOH) State-of-health (SOH) Temperature Temperature Transformer Transformer
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| GB/T 7714 | Zhao, S.-Y. , Ou, K. , Gu, X.-X. et al. A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health [J]. | Rare Metals , 2024 , 43 (11) : 5637-5651 . |
| MLA | Zhao, S.-Y. et al. "A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health" . | Rare Metals 43 . 11 (2024) : 5637-5651 . |
| APA | Zhao, S.-Y. , Ou, K. , Gu, X.-X. , Dan, Z.-M. , Zhang, J.-J. , Wang, Y.-X. . A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health . | Rare Metals , 2024 , 43 (11) , 5637-5651 . |
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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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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.
Keyword ：
Bi-directional long short-term memory network Bi-directional long short-term memory network Energy management strategy Energy management strategy Energy source aging Energy source aging Fuel cell hybrid electric vehicle Fuel cell hybrid electric vehicle Model-based deep reinforcement learning Model-based deep reinforcement learning Model predictive control framework Model predictive control framework
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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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Abstract ：
Accurate estimations of the state of charge (SOC) and state of health (SOH) are crucial for improving battery management techniques. However, batteries are affected by temperature and aging, leading to nonlinear relationships that are more difficult to be characterized. This article proposes an SOC-SOH joint estimation method of lithium-ion battery based on temperature-dependent extended Kalman filter (EKF) and deep learning. First, the battery model state, control, and observation matrices with temperature and capacity variables are created for real-time SOC estimation by using EKF at the local end. Second, battery aging features are extracted and weighted using convolutional neural networks (CNNs) and attention mechanisms and are combined with a gated unit to solve long time series memory problem for SOH estimation at remote computing platform. Finally, the dual time-scale joint model is realized by real-time SOC estimation on the local controller, and the SOH can be calculated on the remote computing platform to correct the available capacity to further update SOC at the end of the discharge. Through 1C discharge rate cycle experimental validation, the root mean square errors of SOC and SOH estimation were within 1%. Therefore, the proposed joint SOC-SOH estimation method can be achieved with local and remote computation.
Keyword ：
Aging Aging Discharges (electric) Discharges (electric) Estimation Estimation Hybrid neural networks Hybrid neural networks joint estimation joint estimation Lithium-ion batteries Lithium-ion batteries lithium-ion battery lithium-ion battery local and remote computing platforms local and remote computing platforms state of charge (SOC) state of charge (SOC) state of health (SOH) state of health (SOH) Temperature measurement Temperature measurement Voltage measurement Voltage measurement
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| GB/T 7714 | Wang, Shiquan , Ou, Kai , Zhang, Wei et al. An SOC and SOH Joint Estimation Method of Lithium-Ion Battery Based on Temperature-Dependent EKF and Deep Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 , 72 (1) : 570-579 . |
| MLA | Wang, Shiquan et al. "An SOC and SOH Joint Estimation Method of Lithium-Ion Battery Based on Temperature-Dependent EKF and Deep Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 72 . 1 (2024) : 570-579 . |
| APA | Wang, Shiquan , Ou, Kai , Zhang, Wei , Wang, Ya-Xiong . An SOC and SOH Joint Estimation Method of Lithium-Ion Battery Based on Temperature-Dependent EKF and Deep Learning . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 , 72 (1) , 570-579 . |
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Abstract ：
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 & YEN;/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR.
Keyword ：
Battery energy storage system Battery energy storage system Configuration optimization Configuration optimization Entropy weight method Entropy weight method Hydrogen energy storage system Hydrogen energy storage system ideal solution ideal solution Non-dominated sorting genetic algorithm-II Non-dominated sorting genetic algorithm-II Technique for order preference by similarity to Technique for order preference by similarity to
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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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Abstract ：
The state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries affect their operating performance and safety. The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging. This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health. The battery model is formulated across temperatures and aging, which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information. The open-circuit voltages (OCVs) are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows. Arrhenius equation is combined with estimated SOH for temperature-aging migration. A novel transformer model is introduced, which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model. This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution. By leveraging multi-head attention, the model establishes information dependency relationships across different aging levels, enabling rapid and precise SOH estimation. Specifically, the root mean square error for SOC and SOH under conditions of 15 degrees C dynamic stress test and 25 degrees C constant current cycling was less than 0.9% and 0.8%, respectively. Notably, the proposed method exhibits excellent adaptability to varying temperature and aging conditions, accurately estimating SOC and SOH.
Keyword ：
Aging migration Aging migration Global correction Global correction Multiscale attention Multiscale attention State-of-charge (SOC) State-of-charge (SOC) State-of-health (SOH) State-of-health (SOH) Temperature Temperature Transformer Transformer
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| GB/T 7714 | Zhao, Shang-Yu , Ou, Kai , Gu, Xing-Xing et al. A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health [J]. | RARE METALS , 2024 , 43 (11) : 5637-5651 . |
| MLA | Zhao, Shang-Yu et al. "A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health" . | RARE METALS 43 . 11 (2024) : 5637-5651 . |
| APA | Zhao, Shang-Yu , Ou, Kai , Gu, Xing-Xing , Dan, Zhi-Min , Zhang, Jiu-Jun , Wang, Ya-Xiong . A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health . | RARE METALS , 2024 , 43 (11) , 5637-5651 . |
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The control of the compressor in fuel cell systems (FCS) plays a crucial role in ensuring stable and efficient operation. To further enhance the efficiency and durability of FCS, considering the impact of power allocation on the air supply control of the system, this work analyzes the coupling relationship between the control variables of the compressor, the state of the air supply system in fuel cells, and the output of the FCS. A coordinated control model for fuel cell hybrid electric vehicle (FCHEV) considering the compressor is reconstructed. A system coordination control strategy based on a cascade feedback structure is proposed, and simulation and experimental verification of system performance are conducted under different reference values of Oxygen Excess Ratio (OER). © 2024 IEEE.
Keyword ：
Cascade control systems Cascade control systems Feedback control Feedback control Gas compressors Gas compressors Hybrid vehicles Hybrid vehicles
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| GB/T 7714 | Hu, Haowen , Guo, Xuan , Yuan, Weiwei et al. Cascade Coordinated Feedback Control for Fuel Cell Air Compressor with System Power Distribution Considered [C] . 2024 : 65-69 . |
| MLA | Hu, Haowen et al. "Cascade Coordinated Feedback Control for Fuel Cell Air Compressor with System Power Distribution Considered" . (2024) : 65-69 . |
| APA | Hu, Haowen , Guo, Xuan , Yuan, Weiwei , Dai, Yuchao , Ou, Kai . Cascade Coordinated Feedback Control for Fuel Cell Air Compressor with System Power Distribution Considered . (2024) : 65-69 . |
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