A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

2019 ◽  
Vol 66 (10) ◽  
pp. 7706-7716 ◽  
Author(s):  
Houde Dai ◽  
Guangcai Zhao ◽  
Mingqiang Lin ◽  
Ji Wu ◽  
Gengfeng Zheng
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Lithium Ion Battery ◽  
Prior Knowledge ◽  
Estimation Method ◽  
Lithium Ion ◽  
The State ◽  
State Of Health ◽  
Knowledge Based

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 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.

Prognostics of the state of health for lithium-ion battery packs in energy storage applications

Energy ◽  
2021 ◽  
pp. 122189
Author(s):  
Chun Chang ◽  
Yutong Wu ◽  
Jiuchun Jiang ◽  
Yan Jiang ◽  
Aina Tian ◽  
...  
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
The State ◽  
State Of Health ◽  
Battery Packs ◽  
Energy Storage Applications
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