Online State of Health Estimation Method for Lithium-ion Battery Based on CEEMDAN for Feature Analysis and RBF Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Ling Mao ◽  
Huizhong Hu ◽  
Jiajun Chen ◽  
Jinbin Zhao ◽  
Keqing Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Rbf Neural Network ◽  
Estimation Method ◽  
Lithium Ion ◽  
Feature Analysis ◽  
State Of Health

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

scholarly journals Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95333-95344
Author(s):  
Hang Yao ◽  
Xiang Jia ◽  
Qian Zhao ◽  
Zhi-Jun Cheng ◽  
Bo Guo
Keyword(s):  
Genetic Programming ◽  
Lithium Ion Battery ◽  
Programming Model ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Health

scholarly journals A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Rbf Neural Networks ◽  
Battery Packs ◽  
Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

scholarly journals State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 156
Author(s):  
Sihan Zhang ◽  
Md Sazzad Hosen ◽  
Theodoros Kalogiannis ◽  
Joeri Van Mierlo ◽  
Maitane Berecibar
Keyword(s):  
Neural Network ◽  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Lithium Ion Battery ◽  
Electrochemical Impedance ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
State Of Health ◽  
Characteristic Parameters ◽  
Backpropagation Neural Network

The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.

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