A Novel Simulation Method for Analyzing Diode Electrical Characteristics Based on Neural Networks

Based on the equivalent circuit model and physical model, a new method for analyzing diode electrical characteristics based on a neural network model is proposed in this paper. Although the equivalent circuit model is widely used, it cannot effectively reflect the working state of diode circuits under the conditions of large injection and high frequency. The analysis method based on physical models developed in recent years can effectively resolve the above shortcomings, but it faces the problem of a low simulation efficiency. Therefore, the physical model method based on neural network acceleration is used to improve the traditional, equivalent circuit model. The results obtained from the equivalent circuit model and the physical model are analyzed using the finite-difference time-domain method. The diode model based on a neural network is fitted with training data obtained from the results of the physical model, then it is summarized into a voltage–current equation and used to improve the traditional, equivalent circuit method. In this way, the improved equivalent circuit method can be used to analyze the working state of a diode circuit under large injection and high frequency conditions. The effectiveness of the proposed model is verified by some examples.

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Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

Applied Sciences ◽

10.3390/app11104631 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4631

Author(s):

Yu Chen ◽

Xiaoqing Ji ◽

Zhongyong Zhao

Keyword(s):

Equivalent Circuit ◽

High Frequency ◽

Equivalent Circuit Model ◽

Black Box ◽

Synchronous Machine ◽

Circuit Model ◽

Modeling Method ◽

Experimental Results ◽

Box Model ◽

Impedance Curve

The accurate establishment of the equivalent circuit model of the synchronous machine windings’ broadband characteristics is the basis for the study of high-frequency machine problems, such as winding fault diagnosis and electromagnetic interference prediction. Therefore, this paper proposes a modeling method for synchronous machine winding based on broadband characteristics. Firstly, the single-phase high-frequency lumped parameter circuit model of synchronous machine winding is introduced, then the broadband characteristics of the port are analyzed by using the state space model, and then the equivalent circuit parameters are identified by using an optimization algorithm combined with the measured broadband impedance characteristics of port. Finally, experimental verification and comparison experiments are carried out on a 5-kW synchronous machine. The experimental results show that the proposed modeling method identifies the impedance curve of the circuit parameters with a high degree of agreement with the measured impedance curve, which indicates that the modeling method is feasible. In addition, the comparative experimental results show that, compared with the engineering exploratory calculation method, the proposed parameter identification method has stronger adaptability to the measured data and a certain robustness. Compared with the black box model, the parameters of the proposed model have a certain physical meaning, and the agreement with the actual impedance characteristic curve is higher than that of the black box model.

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A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach

Energy ◽

10.1016/j.energy.2017.07.035 ◽

2017 ◽

Vol 138 ◽

pp. 118-132 ◽

Cited By ~ 35

Author(s):

Qian-Kun Wang ◽

Yi-Jun He ◽

Jia-Ni Shen ◽

Zi-Feng Ma ◽

Guo-Bin Zhong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Modeling Framework ◽

Unified Modeling ◽

Model Approach

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Transfer of diclofenac sodium across excised guinea pig skin on high-frequency pulse iontophoresis. I. Equivalent circuit model.

Chemical and Pharmaceutical Bulletin ◽

10.1248/cpb.38.1019 ◽

1990 ◽

Vol 38 (4) ◽

pp. 1019-1021 ◽

Cited By ~ 7

Author(s):

Tamotsu KOIZUMI ◽

Masawa KAKEMI ◽

Kazunori KATAYAMA ◽

Hirohiko INADA ◽

Kazuyoshi SUDEJI ◽

...

Keyword(s):

Guinea Pig ◽

Equivalent Circuit ◽

High Frequency ◽

Diclofenac Sodium ◽

Equivalent Circuit Model ◽

Circuit Model ◽

Pig Skin ◽

Frequency Pulse

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SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4052274 ◽

2021 ◽

pp. 1-28

Author(s):

Yanbo Che ◽

Yibin Cai ◽

Hongfeng Li ◽

Yushu Liu ◽

Mingda Jiang ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Variable Order ◽

Nonlinear Correlation ◽

Nonlinear Autoregressive ◽

Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

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