Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insight...Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insights into battery degradation analysis and modeling.However,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH)estimation.Motivated by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis.By utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input features.Subsequently,the extreme gradient boosting(XGBoost)modeling framework is constructed for estimating the battery degradation trajectories.The XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization performance.Experimental validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.展开更多
Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indis...Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.展开更多
Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles.Reinforcement learning algorithms offer various...Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles.Reinforcement learning algorithms offer various advantages,including fast convergence,broad applicability,stability,and robustness,particularly with the integration of deep and transfer learning.This paper provides a comprehensive understanding of reinforcement learning principles and a critical review of various reinforcement learning methods,states,actions,and rewards used to optimize the energy management performance of hybrid electric vehicles.Furthermore,the advantages and limitations of these algorithms are also discussed.This review reveals that deep reinforcement learning techniques show superior performance in handling complex energy management tasks thanks to their ability to learn from high-dimensional state spaces.Nevertheless,their implementation faces notable obstacles,including computational complexity and generalization across diverse driving conditions.Finally,key research directions for future work and challenges are highlighted in the domain of reinforcement-learning-based hybrid electric vehicle energy management.展开更多
A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the...A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.展开更多
The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability.Therefore, energy storage resources are...The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability.Therefore, energy storage resources are used to deal with the challenges imposed by power variability and demand-supply balance.The main focus of this paper is to investigate the appropriate storage technologies and the capacity needed for a successful tidal power integration.Therefore, a simplified sizing method, integrating an energy management strategy, is proposed.This method allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics.The optimal solutions given by the multi-objective evolutionary algorithm are presented and analyzed.展开更多
Due to the harsh and changeable marine environment,one low speed stator-permanent magnet machine named doubly salient permanent magnet machine with toothed pole is applied for marine current energy conversion system.I...Due to the harsh and changeable marine environment,one low speed stator-permanent magnet machine named doubly salient permanent magnet machine with toothed pole is applied for marine current energy conversion system.Indeed,this machine has simple structure,intriguing fault tolerance,and higher power density,which could adequately satisfy the different complicated operation conditions.However,its permanent magnet flux-linkage has the same variation period as the inductance which leads to a strong nonlinear coupling system.Moreover,the torque ripple caused by this special characteristics,uncertainty of system parameters and disturbance of load greatly increases the difficulty of control in this strongly coupling system.Consequently,the classical linear PI controller is difficult to meet the system requirement.In this paper,the high-order sliding mode control strategy based on the super-twisting algorithm for this system is creatively utilized for the first time.The stability of the system within a limited time is also proved with a quadratic Lyapunov function.The relative simulation results demonstrate convincingly that,the high-order sliding mode control has little chattering,high control accuracy and strong robustness.展开更多
In this paper, observer design for an induction motor has been investigated. The peculiarity of this paper is the synthesis of a mono-Luenberger observer for highly coupled system. To transform the nonlinear error dyn...In this paper, observer design for an induction motor has been investigated. The peculiarity of this paper is the synthesis of a mono-Luenberger observer for highly coupled system. To transform the nonlinear error dynamics for the induction motor into the linear parametric varying （LPV） system, the differential mean value theorem combined with the sector nonlinearity transformation has been used. Stability conditions based on the Lyapunov function lead to solvability of a set of linear matrix inequalities. The proposed observer guarantees the global exponential convergence to zero of the estimation error. Finally, the simulation results are given to show the performance of the observer design.展开更多
As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which ...As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change.As a result,introducing a policy that promotes renewable energy(RE)generation and integration is inevitable for sustainable development.In this endeavor,electrification of the transport sector rises as key point in reducing the accelerating environment degradation,by the deployment of new type of vehicles referred to as PHEV(plug-in hybrid electric vehicle).Besides being able to use two kinds of drives(the conventional internal combustion engine and the electric one)to increase the total efficiency,they come with a grid connection and interaction capability known as the vehicle-to-grid(V2G)that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer.Unfortunately,all these advantages do not come alone.The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid.In this work the large scale integration impact of EVs will be investigated in details.The results of power flow analysis and the dynamic response of the grid parameters variation are presented,taking the IEEE 14 bus system as a test grid system.展开更多
Proton exchange membrane(PEM)fuel cell has been regarded as a promising approach to the decarbonization and diversification of energy sources.In recent years,durability and cost issues of PEM fuel cells are increasing...Proton exchange membrane(PEM)fuel cell has been regarded as a promising approach to the decarbonization and diversification of energy sources.In recent years,durability and cost issues of PEM fuel cells are increasingly significant with the rapid increase of power density.However,the failure to maintain the cell consistency,as one major cause of the above issue,has attracted little attention.Therefore,this study intends to figure out the underlying cause of cell inconsistency and provide solutions to it from the perspective of multi-physics transport coupled with electrochemical reactions.The PEM fuel cells with electrodes under two compression modes are firstly discussed to fully explain the relationship of cell performance and consistency to electrode structure and multi-physics transport.The result indicates that one main cause of cell inconsistency is the intrinsic conflict between the separated transport and cooperated consumption of oxygen and electron throughout the active area.Then,a mixed-pathway electrode design is proposed to reduce the cell inconsistency by enhancing the mixed transport of oxygen and electron in the electrode.It is found that the mixing of pathways in electrodes at under-rib region is more effective than that at the under-channel region,and can achieve an up to 40%reduction of the cell inconsistency with little(3.3%)sacrificed performance.In addition,all the investigations are implemented based on a self-developed digitalization platform that reconstructs the complex physical–chemical system of PEM fuel cells.The fully observable physical information of the digitalized cells provides strong support to the related analysis.展开更多
Existing research on fault diagnosis for polymer electrolyte membrane fuel cells(PEMFC)has advanced significantly,yet performance is hindered by variations in data distributions and the requirement for extensive fault...Existing research on fault diagnosis for polymer electrolyte membrane fuel cells(PEMFC)has advanced significantly,yet performance is hindered by variations in data distributions and the requirement for extensive fault data.In this study,a cross-domain adaptive health diagnosis method for PEMFC is proposed,integrating the digital twin model and transfer convolutional diagnosis model.A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method.To extract long-term time series features from the data,a temporal convolutional network(TCN)is proposed as a pre-trained diagnosis model for the source domain,with feature extraction layers that can be reused to the transfer learning network.It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults,including pressure,drying,flow,and flooding faults,with 99.92%accuracy,through the effective capture of the long-term dependencies in time series data.Finally,a domain adaptive transfer convolutional network(DATCN)is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features.The results show that the DATCN model,tested on three different target domain devices with adversarial training using only 10%normal data,can achieve an average accuracy of 98.5%(30%improved over traditional diagnosis models).This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices,significantly reducing the reliance on extensive fault data.展开更多
This paper deals with a design methodology of permanent magnets(PM)generators used for fixed-pitch tidal turbines in a marine renewable energy context.In the case of underwater turbines,fixed-pitch tidal turbines coul...This paper deals with a design methodology of permanent magnets(PM)generators used for fixed-pitch tidal turbines in a marine renewable energy context.In the case of underwater turbines,fixed-pitch tidal turbines could be very attractive and interesting to reduce maintenance operation by avoiding using such a complex electromechanical system for blade-pitching.In this technological case,one of the main control challenges is to ensure power limitation at high tidal current velocities.This control mode can be achieved using the generator flux-weakening.In this context,this paper proposes an original and systemic design methodology to optimize the generator design taking into account the tidal turbine power limitation for high tidal currents velocities.展开更多
文摘Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insights into battery degradation analysis and modeling.However,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH)estimation.Motivated by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis.By utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input features.Subsequently,the extreme gradient boosting(XGBoost)modeling framework is constructed for estimating the battery degradation trajectories.The XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization performance.Experimental validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.
文摘Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.
文摘Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles.Reinforcement learning algorithms offer various advantages,including fast convergence,broad applicability,stability,and robustness,particularly with the integration of deep and transfer learning.This paper provides a comprehensive understanding of reinforcement learning principles and a critical review of various reinforcement learning methods,states,actions,and rewards used to optimize the energy management performance of hybrid electric vehicles.Furthermore,the advantages and limitations of these algorithms are also discussed.This review reveals that deep reinforcement learning techniques show superior performance in handling complex energy management tasks thanks to their ability to learn from high-dimensional state spaces.Nevertheless,their implementation faces notable obstacles,including computational complexity and generalization across diverse driving conditions.Finally,key research directions for future work and challenges are highlighted in the domain of reinforcement-learning-based hybrid electric vehicle energy management.
基金supported in part by the National Natural Science Foundation(51977181,52077180)Natural Science Foundation of Sichuan Province(2022NSFSC0027)+2 种基金Fok Ying-Tong Education Foundation of China(171104)14th Five-year Major Science and Technology Research Project of CRRC(2021CXZ021-2)Key research and development project of China National Railway Group Co.,Ltd(N2022J016-B).
文摘A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.
文摘The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability.Therefore, energy storage resources are used to deal with the challenges imposed by power variability and demand-supply balance.The main focus of this paper is to investigate the appropriate storage technologies and the capacity needed for a successful tidal power integration.Therefore, a simplified sizing method, integrating an energy management strategy, is proposed.This method allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics.The optimal solutions given by the multi-objective evolutionary algorithm are presented and analyzed.
基金This work was supported by National Natural Science Foundation of China,China(Grant No:61503242)Nat-ural Science Foundation of Shanghai,China(15ZR1419800).
文摘Due to the harsh and changeable marine environment,one low speed stator-permanent magnet machine named doubly salient permanent magnet machine with toothed pole is applied for marine current energy conversion system.Indeed,this machine has simple structure,intriguing fault tolerance,and higher power density,which could adequately satisfy the different complicated operation conditions.However,its permanent magnet flux-linkage has the same variation period as the inductance which leads to a strong nonlinear coupling system.Moreover,the torque ripple caused by this special characteristics,uncertainty of system parameters and disturbance of load greatly increases the difficulty of control in this strongly coupling system.Consequently,the classical linear PI controller is difficult to meet the system requirement.In this paper,the high-order sliding mode control strategy based on the super-twisting algorithm for this system is creatively utilized for the first time.The stability of the system within a limited time is also proved with a quadratic Lyapunov function.The relative simulation results demonstrate convincingly that,the high-order sliding mode control has little chattering,high control accuracy and strong robustness.
文摘In this paper, observer design for an induction motor has been investigated. The peculiarity of this paper is the synthesis of a mono-Luenberger observer for highly coupled system. To transform the nonlinear error dynamics for the induction motor into the linear parametric varying （LPV） system, the differential mean value theorem combined with the sector nonlinearity transformation has been used. Stability conditions based on the Lyapunov function lead to solvability of a set of linear matrix inequalities. The proposed observer guarantees the global exponential convergence to zero of the estimation error. Finally, the simulation results are given to show the performance of the observer design.
文摘As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change.As a result,introducing a policy that promotes renewable energy(RE)generation and integration is inevitable for sustainable development.In this endeavor,electrification of the transport sector rises as key point in reducing the accelerating environment degradation,by the deployment of new type of vehicles referred to as PHEV(plug-in hybrid electric vehicle).Besides being able to use two kinds of drives(the conventional internal combustion engine and the electric one)to increase the total efficiency,they come with a grid connection and interaction capability known as the vehicle-to-grid(V2G)that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer.Unfortunately,all these advantages do not come alone.The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid.In this work the large scale integration impact of EVs will be investigated in details.The results of power flow analysis and the dynamic response of the grid parameters variation are presented,taking the IEEE 14 bus system as a test grid system.
基金supported by the National Natural Science Foundation of China(52176196)the Natural Science Foundation of Tianjin(China)for Distinguished Young Scholars(18JCJQJC46700).
文摘Proton exchange membrane(PEM)fuel cell has been regarded as a promising approach to the decarbonization and diversification of energy sources.In recent years,durability and cost issues of PEM fuel cells are increasingly significant with the rapid increase of power density.However,the failure to maintain the cell consistency,as one major cause of the above issue,has attracted little attention.Therefore,this study intends to figure out the underlying cause of cell inconsistency and provide solutions to it from the perspective of multi-physics transport coupled with electrochemical reactions.The PEM fuel cells with electrodes under two compression modes are firstly discussed to fully explain the relationship of cell performance and consistency to electrode structure and multi-physics transport.The result indicates that one main cause of cell inconsistency is the intrinsic conflict between the separated transport and cooperated consumption of oxygen and electron throughout the active area.Then,a mixed-pathway electrode design is proposed to reduce the cell inconsistency by enhancing the mixed transport of oxygen and electron in the electrode.It is found that the mixing of pathways in electrodes at under-rib region is more effective than that at the under-channel region,and can achieve an up to 40%reduction of the cell inconsistency with little(3.3%)sacrificed performance.In addition,all the investigations are implemented based on a self-developed digitalization platform that reconstructs the complex physical–chemical system of PEM fuel cells.The fully observable physical information of the digitalized cells provides strong support to the related analysis.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB4005800)National Natural Science Foundation of China(grant No.52241702).
文摘Existing research on fault diagnosis for polymer electrolyte membrane fuel cells(PEMFC)has advanced significantly,yet performance is hindered by variations in data distributions and the requirement for extensive fault data.In this study,a cross-domain adaptive health diagnosis method for PEMFC is proposed,integrating the digital twin model and transfer convolutional diagnosis model.A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method.To extract long-term time series features from the data,a temporal convolutional network(TCN)is proposed as a pre-trained diagnosis model for the source domain,with feature extraction layers that can be reused to the transfer learning network.It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults,including pressure,drying,flow,and flooding faults,with 99.92%accuracy,through the effective capture of the long-term dependencies in time series data.Finally,a domain adaptive transfer convolutional network(DATCN)is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features.The results show that the DATCN model,tested on three different target domain devices with adversarial training using only 10%normal data,can achieve an average accuracy of 98.5%(30%improved over traditional diagnosis models).This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices,significantly reducing the reliance on extensive fault data.
文摘This paper deals with a design methodology of permanent magnets(PM)generators used for fixed-pitch tidal turbines in a marine renewable energy context.In the case of underwater turbines,fixed-pitch tidal turbines could be very attractive and interesting to reduce maintenance operation by avoiding using such a complex electromechanical system for blade-pitching.In this technological case,one of the main control challenges is to ensure power limitation at high tidal current velocities.This control mode can be achieved using the generator flux-weakening.In this context,this paper proposes an original and systemic design methodology to optimize the generator design taking into account the tidal turbine power limitation for high tidal currents velocities.
