The world's energy system is changing dramatically.Li-ion battery,as a powerful and highly effective energy storage technique,is crucial to the new energy revolution for its continuously expanding application in e...The world's energy system is changing dramatically.Li-ion battery,as a powerful and highly effective energy storage technique,is crucial to the new energy revolution for its continuously expanding application in electric vehicles and grids.Over the entire lifetime of these power batteries,it is essential to monitor their state of health not only for the predicted mileage and safety management of the running electric vehicles,but also for an"end-of-life"evaluation for their repurpose.Electrochemical impedance spectroscopy(EIS)has been widely used to diagnose the health state of batteries quickly and nondestructively.In this review,we have outlined the working principles of several electrochemical impedance techniques and further evaluated their application prospects to achieve the goal of nondestructive testing of battery health.EIS can scientifically and reasonably perform real-time monitoring and evaluation of electric vehicle power batteries in the future and play an important role in vehicle safety and battery gradient utilization.展开更多
Flexoelectricity refers to the mechanical-electro coupling between strain gradient and electric polarization, and conversely, the electro-mechanical coupling between electric field gradient and mechanical stress. This...Flexoelectricity refers to the mechanical-electro coupling between strain gradient and electric polarization, and conversely, the electro-mechanical coupling between electric field gradient and mechanical stress. This unique effect shows a promising size effect which is usually large as the material dimension is shrunk down. Moreover, it could break the limitation of centrosymmetry, and has been found in numerous kinds of materials which cover insulators, liquid crystals, biological materials, and semiconductors. In this review, we will give a brief report about the recent discoveries in flexoelectricity, focusing on the flexoelectric materials and their applications. The theoretical developments in this field are also addressed. In the end, the perspective of flexoelectricity and some open questions which still remain unsolved are commented upon.展开更多
The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for S...The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise.Subsequently, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.展开更多
In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery operation.In the EMS,it is proposed to...In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery operation.In the EMS,it is proposed to approximate the EMS policy function with fuzzy inference system(FIS)and learn the policy parameters through policy gradient reinforcement learning(PGRL).Thus,a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the paper.Fuzzy REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment,which makes it independent of model accuracy,prior knowledge,and expert experience.Meanwhile,to stabilize the training process,a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient direction.More-over,the drawbacks of traditional reinforcement learning such as high computation burden,long convergence time,can also be overcome.The effectiveness of the proposed methods were verified by Hardware-in-Loop ex-periments.The adaptability of the proposed method to the changes of driving conditions and system states is also verified.展开更多
The impurity iron in silicon material will seriously affect the photoelectric conversion efficiency of silicon solar cells.However,the traditional silicon purification method has the disadvantages of long cycle,high e...The impurity iron in silicon material will seriously affect the photoelectric conversion efficiency of silicon solar cells.However,the traditional silicon purification method has the disadvantages of long cycle,high energy consumption and serious pollution.In this study,an efficient and green pulsed electric current purification technology is proposed.The electromigration effect of iron elements,the current density gradient driving of iron phase,and the gravity of iron phase all affect the migration behavior of iron phase in silicon melt under pulsed electric current.Regardless of the depth of electrode insertion into the silicon melt,the solubility of iron in silicon decreases under the pulsed electric current,which helps to form the iron phase.At the same time,the iron phase tends to sink toward the bottom under the influence of gravity.When the electrode is shallowly inserted,a non-uniform electric field is formed in the silicon melt,and the iron phase is mainly driven by the current density gradient to accelerate sink toward the bottom.When the electrode is fully inserted,an approximately uniform electric field is formed in the silicon melt,and iron elements are preferentially migrated to the cathode by electromigration,forming iron phase sinking at the cathode.The study of impurity iron migration behavior in silicon melt under pulsed electric current provides a new approach for the purification of polycrystalline silicon.展开更多
基金financially supported by the State Grid Corporation Science and Technology Project of China(No.520940180017)。
文摘The world's energy system is changing dramatically.Li-ion battery,as a powerful and highly effective energy storage technique,is crucial to the new energy revolution for its continuously expanding application in electric vehicles and grids.Over the entire lifetime of these power batteries,it is essential to monitor their state of health not only for the predicted mileage and safety management of the running electric vehicles,but also for an"end-of-life"evaluation for their repurpose.Electrochemical impedance spectroscopy(EIS)has been widely used to diagnose the health state of batteries quickly and nondestructively.In this review,we have outlined the working principles of several electrochemical impedance techniques and further evaluated their application prospects to achieve the goal of nondestructive testing of battery health.EIS can scientifically and reasonably perform real-time monitoring and evaluation of electric vehicle power batteries in the future and play an important role in vehicle safety and battery gradient utilization.
基金supported by the National Natural Science Foundation of China under Grant Nos. 11574126 and 11604135the Natural Science Foundation of Jiangxi Province (No. 20161BAB216110)+1 种基金China Postdoctoral Science Foundation (No. 2017M612162)Postdoctoral Science Foundation of Jiangxi Province (No. 2017KY02)
文摘Flexoelectricity refers to the mechanical-electro coupling between strain gradient and electric polarization, and conversely, the electro-mechanical coupling between electric field gradient and mechanical stress. This unique effect shows a promising size effect which is usually large as the material dimension is shrunk down. Moreover, it could break the limitation of centrosymmetry, and has been found in numerous kinds of materials which cover insulators, liquid crystals, biological materials, and semiconductors. In this review, we will give a brief report about the recent discoveries in flexoelectricity, focusing on the flexoelectric materials and their applications. The theoretical developments in this field are also addressed. In the end, the perspective of flexoelectricity and some open questions which still remain unsolved are commented upon.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61903114 and 62203423)the Anhui Provincial Natural Science Foundation (Grant No. 2008085QF301)+2 种基金the Youth Science and Technology Talents Support Program (2020) by Anhui Association for Science and Technology (Grant No. RCTJ202008)the Fundamental Research Funds for the Central Universities (Grant No. JZ2021HGTB0076)the Education and Scientific Research Project for Young and Middleaged Teachers in Fujian Province (Grant No. JAT201276)。
文摘The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise.Subsequently, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.
基金This work has been supported by the ANR DEAL(contract ANR-20-CE05-0016-01)This work has also been partially funded by Region Sud Provence-Alpes-Cote d’Azur via project AMULTI(2021_02918).
文摘In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery operation.In the EMS,it is proposed to approximate the EMS policy function with fuzzy inference system(FIS)and learn the policy parameters through policy gradient reinforcement learning(PGRL).Thus,a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the paper.Fuzzy REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment,which makes it independent of model accuracy,prior knowledge,and expert experience.Meanwhile,to stabilize the training process,a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient direction.More-over,the drawbacks of traditional reinforcement learning such as high computation burden,long convergence time,can also be overcome.The effectiveness of the proposed methods were verified by Hardware-in-Loop ex-periments.The adaptability of the proposed method to the changes of driving conditions and system states is also verified.
基金financially supported by the National Natural Science Foundation of China(No.U21B2082)Natural Science Foundation of Beijing Municipality(No.2222065)and Fundamental Research Funds for the Central Universities(No.FRF-TP-22-02C2).
文摘The impurity iron in silicon material will seriously affect the photoelectric conversion efficiency of silicon solar cells.However,the traditional silicon purification method has the disadvantages of long cycle,high energy consumption and serious pollution.In this study,an efficient and green pulsed electric current purification technology is proposed.The electromigration effect of iron elements,the current density gradient driving of iron phase,and the gravity of iron phase all affect the migration behavior of iron phase in silicon melt under pulsed electric current.Regardless of the depth of electrode insertion into the silicon melt,the solubility of iron in silicon decreases under the pulsed electric current,which helps to form the iron phase.At the same time,the iron phase tends to sink toward the bottom under the influence of gravity.When the electrode is shallowly inserted,a non-uniform electric field is formed in the silicon melt,and the iron phase is mainly driven by the current density gradient to accelerate sink toward the bottom.When the electrode is fully inserted,an approximately uniform electric field is formed in the silicon melt,and iron elements are preferentially migrated to the cathode by electromigration,forming iron phase sinking at the cathode.The study of impurity iron migration behavior in silicon melt under pulsed electric current provides a new approach for the purification of polycrystalline silicon.
