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学者姓名：王亚雄
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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 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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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 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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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 (FCV). 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. In this paper, 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 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 paper 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. IEEE
Keyword ：
Coupling mechanisms Coupling mechanisms Couplings Couplings Data-driven Data-driven Diagonal matrix decoupling Diagonal matrix decoupling Fuel cells Fuel cells Fuel cell vehicles Fuel cell vehicles Proton exchange membrane fuel cell Proton exchange membrane fuel cell Protons Protons Steady-state Steady-state Transfer functions Transfer functions Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Qiu, Y. , Zhang, C. , Hametner, C. 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) : 1-1 . |
| MLA | Qiu, Y. 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) : 1-1 . |
| APA | Qiu, Y. , Zhang, C. , Hametner, C. , Zeng, T. , Ferrara, A. , Wang, Y. 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) , 1-1 . |
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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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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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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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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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