Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,batter...Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.展开更多
A unified charge-based model for fully depleted silicon-on-insulator （SOI） metal oxide semiconductor field-effect transistors （MOSFETs） is presented. The proposed model is accurate and applicable from intrinsic to...A unified charge-based model for fully depleted silicon-on-insulator （SOI） metal oxide semiconductor field-effect transistors （MOSFETs） is presented. The proposed model is accurate and applicable from intrinsic to heavily doped channels with various structure parameters. The framework starts from the one-dimensional Poisson Boltzmann equa- tion, and based on the full depletion approximation, an accurate inversion charge density equation is obtained. With the inversion charge density solution, the unified drain current expression is derived, and a unified terminal charge and intrinsic capacitance model is also derived in the quasi-static case. The validity and accuracy of the presented analytic model is proved by numerical simulations.展开更多
A two-dimensional analytical model of double-gate（DG） tunneling field-effect transistors（TFETs） with interface trapped charges is proposed in this paper. The influence of the channel mobile charges on the potentia...A two-dimensional analytical model of double-gate（DG） tunneling field-effect transistors（TFETs） with interface trapped charges is proposed in this paper. The influence of the channel mobile charges on the potential profile is also taken into account in order to improve the accuracy of the models. On the basis of potential profile,the electric field is derived and the expression for the drain current is obtained by integrating the BTBT generation rate. The model can be used to study the impact of interface trapped charges on the surface potential, the shortest tunneling length, the drain current and the threshold voltage for varying interface trapped charge densities, length of damaged region as well as the structural parameters of the DG TFET and can also be utilized to design the charge trapped memory devices based on TFET. The biggest advantage of this model is that it is more accurate,and in its expression there are no fitting parameters with small calculating amount. Very good agreements for both the potential, drain current and threshold voltage are observed between the model calculations and the simulated results.展开更多
文章介绍了槟松紫外全帧背照式面阵CCD(S7171-0909)的结构和工作特点,分析了该芯片驱动时序要求;采用可编程逻辑器件EP2C8作为硬件平台,在Quartus II 9.1软件环境下,用基于状态机的算法对时序电路进行了描述,设计产生了芯片正常工作所...文章介绍了槟松紫外全帧背照式面阵CCD(S7171-0909)的结构和工作特点,分析了该芯片驱动时序要求;采用可编程逻辑器件EP2C8作为硬件平台,在Quartus II 9.1软件环境下,用基于状态机的算法对时序电路进行了描述,设计产生了芯片正常工作所需的时序脉冲信号,并选用EL7202作为CCD驱动器对时钟脉冲进行功率放大。调用第三方软件进行仿真,并给出实际工作输出波形,结果表明,设计的时序电路满足CCD对各驱动信号的要求。展开更多
基金supported by the BK21 FOUR project funded by the Ministry of Education,Korea(4199990113966).
文摘Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.
基金supported by the National Natural Science Foundation of China (Grant No. 60876027)the State Key Program of the National Natural Science Foundation of China (Grant No. 61036004)+2 种基金the Shenzhen Science and Technology Foundation, China (Grant No. CXB201005250031A)the Fundamental Research Project of Shenzhen Science and Technology Foundation, China (Grant No. JC201005280670A)the International Collaboration Project of Shenzhen Science & Technology Foundation, China (Grant No. ZYA2010006030006A)
文摘A unified charge-based model for fully depleted silicon-on-insulator （SOI） metal oxide semiconductor field-effect transistors （MOSFETs） is presented. The proposed model is accurate and applicable from intrinsic to heavily doped channels with various structure parameters. The framework starts from the one-dimensional Poisson Boltzmann equa- tion, and based on the full depletion approximation, an accurate inversion charge density equation is obtained. With the inversion charge density solution, the unified drain current expression is derived, and a unified terminal charge and intrinsic capacitance model is also derived in the quasi-static case. The validity and accuracy of the presented analytic model is proved by numerical simulations.
基金Project supported by the National Natural Science Foundation of China(No.61376106)the University Natural Science Research Key Project of Anhui Province(No.KJ2016A169)the Introduced Talents Project of Anhui Science and Technology University
文摘A two-dimensional analytical model of double-gate（DG） tunneling field-effect transistors（TFETs） with interface trapped charges is proposed in this paper. The influence of the channel mobile charges on the potential profile is also taken into account in order to improve the accuracy of the models. On the basis of potential profile,the electric field is derived and the expression for the drain current is obtained by integrating the BTBT generation rate. The model can be used to study the impact of interface trapped charges on the surface potential, the shortest tunneling length, the drain current and the threshold voltage for varying interface trapped charge densities, length of damaged region as well as the structural parameters of the DG TFET and can also be utilized to design the charge trapped memory devices based on TFET. The biggest advantage of this model is that it is more accurate,and in its expression there are no fitting parameters with small calculating amount. Very good agreements for both the potential, drain current and threshold voltage are observed between the model calculations and the simulated results.
文摘文章介绍了槟松紫外全帧背照式面阵CCD(S7171-0909)的结构和工作特点,分析了该芯片驱动时序要求;采用可编程逻辑器件EP2C8作为硬件平台,在Quartus II 9.1软件环境下,用基于状态机的算法对时序电路进行了描述,设计产生了芯片正常工作所需的时序脉冲信号,并选用EL7202作为CCD驱动器对时钟脉冲进行功率放大。调用第三方软件进行仿真,并给出实际工作输出波形,结果表明,设计的时序电路满足CCD对各驱动信号的要求。
