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学者姓名：王亚雄
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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 efficiency and dynamic response of air compressors are crucial for stability and lifespan of hydrogen fuel cells. A predictive control scheme with surge- and choke-constrained awareness is proposed to ensure safe and efficient operation of air compressors in this study. The proposed scheme consists of an efficiency enhancement model predictive control (EE-MPC), and an improved active disturbance rejection control (IADRC). Surge- and choke-constrained awareness is achieved by comparing predicted air flow with surge and choke limitations. Simultaneously, the EE-MPC is constrained with oxygen excess ratio (OER) and obtains optimal solution by searching active set. The reference flow and supply manifold pressure trajectories for IADRC are generated by EE-MPC. A designed piecewise differentiable nonlinear smoothing function is embedded in IADRC. The disturbances are estimated for coordinating flow and pressure control. Under China heavy-duty commercial vehicle test cycle for bus conditions, root-mean-squared errors (RMSEs) of flow and pressure are 3.27 g s-1 and 1.88 x 103 Pa, respectively, and the mean efficiency can be enhanced by 13.4% compared to the MPC with fixed OER. Finally, a controller hardware-in-the-loop test is conducted, with flow and pressure RMSEs of 2.48 g s-1 and 4.28 x 103 Pa between the test and simulation, respectively. This study proposes a predictive control scheme with surge- and choke-constrained awareness to guarantee safety and efficiency of air compressors. The reference flow and pressure trajectories are formulated by efficiency enhancement model predictive control, and further tracked by improved active disturbance rejection control. The proposed scheme can efficiently improve fuel cell air compressor isentropic efficiency and avoid surge and choke. image
Keyword ：
air compressor predictive control air compressor predictive control compressor isentropic efficiency enhancement compressor isentropic efficiency enhancement coordinated control coordinated control fuel cell fuel cell surge- and choke-constrained awareness surge- and choke-constrained awareness
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| GB/T 7714 | Ye, Wangcheng , Zhong, Shunbin , Shen, Ying et al. Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness [J]. | ADVANCED THEORY AND SIMULATIONS , 2024 , 7 (6) . |
| MLA | Ye, Wangcheng et al. "Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness" . | ADVANCED THEORY AND SIMULATIONS 7 . 6 (2024) . |
| APA | Ye, Wangcheng , Zhong, Shunbin , Shen, Ying , Zhang, Xuezhi , Wang, Ya-Xiong . Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness . | ADVANCED THEORY AND SIMULATIONS , 2024 , 7 (6) . |
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Given the serious net power decline and excessive mass of the system in heavy power fuel cells (FCs) operating at variable altitudes, optimizing and matching the appropriate air compressor of FC emerged as a prominent area of research. This study aims to perform multi-objective and multi-parameter optimization of the FC air loop to improve the performance of the FC system for heavy power under a variable altitude environment. Based on the experimental test data, combined with semi-empirical and semi-mechanism equations, five air compressor models with different power levels were developed, and their performance covered the altitude from 0 to 4000 m. Pareto theory is introduced to evaluate the three-dimensional objectives of cathode system mass, isentropic efficiency, and system net power under different air supply parameters and different power levels of air compressors. The Pareto front is solved by a multi-objective particle swarm optimization (MOPSO) algorithm under different altitudes. The results show that compared with the single-objective PSO with customized weight summation (PSO1 and PSO2), MOPSO improves 2.38% and 8.89% for net power, respectively. The three objectives for the optimized configuration are −12.20% (0.61%), 15.87% (27.40%), and 23.96% (−2.74%) improved than baseline1 (baseline2) for the 4000 m altitude. © 2024
Keyword ：
Air loop system Air loop system Fuel cell (FC) Fuel cell (FC) High altitude High altitude Multiple objectives optimization Multiple objectives optimization
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| GB/T 7714 | Chen, J. , He, H. , Zhang, Z. et al. Optimization and matching of the air loop system in a fuel cell for high-altitude application [J]. | International Journal of Hydrogen Energy , 2024 . |
| MLA | Chen, J. et al. "Optimization and matching of the air loop system in a fuel cell for high-altitude application" . | International Journal of Hydrogen Energy (2024) . |
| APA | Chen, J. , He, H. , Zhang, Z. , Wu, J. , Wang, Y.-X. . Optimization and matching of the air loop system in a fuel cell for high-altitude application . | International Journal of Hydrogen Energy , 2024 . |
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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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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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Pressure and mass flow control in fuel cell air supply systems highly affect the dynamic performance, reliability, and efficiency of proton exchange membrane fuel cell vehicles (FCVs). However, the coupling effect between pressure and mass flow makes their control difficult and can seriously compromise the performance of proton exchange membrane fuel cells (PEMFCs). In this article, the optimization diagonal matrix decoupling (DMD) is proposed to avoid the occurrence of detrimental operating conditions and improve performance. This study includes data-driven modeling of the air supply system with transfer functions and the analysis of the coupling mechanisms between pressure and mass flow. The simulation results show that the proposed strategy has good disturbance rejection and low coupling between flow and pressure. Compared with conventional DMD, the standard deviation (std) of the relative control error of flow and pressure can be reduced by 9.7% and 14.4% in the proposed strategy. The new contribution of this article is to reveal the coupling mechanism, which can be used to guide the design of decoupling control strategies designed for air supply systems of fuel cell engines in FCV.
Keyword ：
Coupling mechanisms Coupling mechanisms Couplings Couplings data driven data driven diagonal matrix decoupling (DMD) diagonal matrix decoupling (DMD) Fuel cells Fuel cells Fuel cell vehicles Fuel cell vehicles proton exchange membrane fuel cell (PEMFC) proton exchange membrane fuel cell (PEMFC) Protons Protons Steady-state Steady-state Transfer functions Transfer functions Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Qiu, Yuqi , Zhang, Caizhi , Hametner, Christoph et al. Coupling Mechanism Analysis and Decoupling Control of the Air Supply System for Fuel Cell Engine in Fuel Cell Vehicle [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2024 , 10 (4) : 10059-10072 . |
| MLA | Qiu, Yuqi et al. "Coupling Mechanism Analysis and Decoupling Control of the Air Supply System for Fuel Cell Engine in Fuel Cell Vehicle" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 10 . 4 (2024) : 10059-10072 . |
| APA | Qiu, Yuqi , Zhang, Caizhi , Hametner, Christoph , Zeng, Tao , Ferrara, Alessandro , Wang, Yaxiong et al. Coupling Mechanism Analysis and Decoupling Control of the Air Supply System for Fuel Cell Engine in Fuel Cell Vehicle . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2024 , 10 (4) , 10059-10072 . |
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It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm.
Keyword ：
Air supply system Air supply system Cost function Cost function Dynamic programming (DP) Dynamic programming (DP) Energy management strategy (EMS) Energy management strategy (EMS) Fuel cell hybrid electric vehicle (FCHEV) Fuel cell hybrid electric vehicle (FCHEV)
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| GB/T 7714 | Chen, Jinzhou , He, Hongwen , Wang, Ya-Xiong et al. Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization [J]. | ENERGY , 2024 , 300 . |
| MLA | Chen, Jinzhou et al. "Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization" . | ENERGY 300 (2024) . |
| APA | Chen, Jinzhou , He, Hongwen , Wang, Ya-Xiong , Quan, Shengwei , Zhang, Zhendong , Wei, Zhongbao et al. Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization . | ENERGY , 2024 , 300 . |
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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 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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Deep reinforcement learning (DRL) has been widely used in the field of automotive energy management. However, DRL is computationally inefficient and less robust, making it difficult to be applied to practical systems. In this article, a customized energy management strategy based on the deep reinforcement learning-model predictive control (DRL-MPC) self-regulation framework is proposed for fuel cell electric vehicles. The soft actor critic (SAC) algorithm is used to train the energy management strategy offline, which minimizes system comprehensive consumption and lifetime degradation. The trained SAC policy outputs the sequence of fuel cell actions at different states in the prediction horizon as the initial value of the nonlinear MPC solution. Under the MPC framework, iterative computation is carried out for nonlinear optimization problems to optimize action sequences based on SAC policy. In addition, the vehicle's usual operation dataset is collected to customize the update package for further improvement of the energy management effect. The DRL-MPC can optimize the SAC policy action at the state boundary to reduce system lifetime degradation. The proposed strategy also shows better optimization robustness than SAC strategy under different vehicle loads. Moreover, after the update package application, the total cost is reduced by 5.93% compared with SAC strategy, which has better optimization under comprehensive condition with different vehicle loads.
Keyword ：
Batteries Batteries Costs Costs Customized energy management Customized energy management Degradation Degradation Energy management Energy management fuel cell and battery degradation fuel cell and battery degradation fuel cell electric vehicle fuel cell electric vehicle Fuel cells Fuel cells model predictive control model predictive control Optimization Optimization reinforcement learning reinforcement learning State of charge State of charge
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| GB/T 7714 | Quan, Shengwei , He, Hongwen , Wei, Zhongbao et al. Customized Energy Management for Fuel Cell Electric Vehicle Based on Deep Reinforcement Learning-Model Predictive Control Self-Regulation Framework [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) : 13776-13785 . |
| MLA | Quan, Shengwei et al. "Customized Energy Management for Fuel Cell Electric Vehicle Based on Deep Reinforcement Learning-Model Predictive Control Self-Regulation Framework" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 12 (2024) : 13776-13785 . |
| APA | Quan, Shengwei , He, Hongwen , Wei, Zhongbao , Chen, Jinzhou , Zhang, Zhendong , Wang, Ya-Xiong . Customized Energy Management for Fuel Cell Electric Vehicle Based on Deep Reinforcement Learning-Model Predictive Control Self-Regulation Framework . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) , 13776-13785 . |
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能量管理决定燃料电池汽车(fuel cell vehicles,FCV)动力系统的功率分配,影响FCV的经济性与耐久性等.汽车运行工况复杂多变,能量管理可通过融合交通信息提升FCV动力系统的输出性能.本文总结了FCV能量管理的优化目标,分析了传统的规则式与优化式的能量管理策略;以车速、交通状况等交通信息的分析及预测为重点,综述马尔可夫、人工智能等预测方法,总结融合交通信息的FCV能量管理策略的研究进展;最后,提出融合交通信息的FCV能量管理发展方向.
Keyword ：
交通信息 交通信息 燃料电池汽车 燃料电池汽车 能量管理 能量管理 预测方法 预测方法
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| GB/T 7714 | 王亚雄 , 范依莹 , 欧凯 et al. 融合交通信息的燃料电池汽车能量管理研究进展 [J]. | 汽车工程 , 2024 , 46 (12) : 2314-2328,2338 . |
| MLA | 王亚雄 et al. "融合交通信息的燃料电池汽车能量管理研究进展" . | 汽车工程 46 . 12 (2024) : 2314-2328,2338 . |
| APA | 王亚雄 , 范依莹 , 欧凯 , 魏中宝 , 张久俊 . 融合交通信息的燃料电池汽车能量管理研究进展 . | 汽车工程 , 2024 , 46 (12) , 2314-2328,2338 . |
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