scholarly journals Soybean and Soybean Oil Price Forecasting through the Nonlinear Autoregressive Neural Network (NARNN) and NARNN with Exogenous Inputs (NARNN–X)

2022 ◽  
pp. 200061
Author(s):  
Xiaojie Xu ◽  
Yun Zhang
Keyword(s):  
Neural Network ◽  
Soybean Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

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

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.

Sign in / Sign up

Export Citation Format

Share Document