Diagnostics and Prognostics of Lithium-Ion Batteries

2015 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Cheol Lee
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Data Driven ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Battery State Of Charge ◽  
Li Ion ◽  
Useful Life

This paper investigates recent research on battery diagnostics and prognostics especially for Lithium-ion (Li-ion) batteries. Battery diagnostics focuses on battery models and diagnosis algorithms for battery state of charge (SOC) and state of health (SOH) estimation. Battery prognostics elaborates data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH. Readers will learn not only basics but also very recent research developments on battery diagnostics and prognostics.

scholarly journals Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7836
Author(s):  
Cher Ming Tan ◽  
Preetpal Singh ◽  
Che Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Lithium Ion ◽  
Threshold Level ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
State Of Health ◽  
On Line ◽  
Useful Life

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

scholarly journals Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

2018 ◽  
Vol 8 (11) ◽  
pp. 2078 ◽  
Author(s):  
Cunsong Wang ◽  
Ningyun Lu ◽  
Senlin Wang ◽  
Yuehua Cheng ◽  
Bin Jiang
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Li Ion Batteries ◽  
Short Term ◽  
Term Memory ◽  
Li Ion ◽  
Useful Life ◽  
Long Short Term Memory

On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

2013 ◽  
Vol 239 ◽  
pp. 680-688 ◽  
Author(s):  
Adnan Nuhic ◽  
Tarik Terzimehic ◽  
Thomas Soczka-Guth ◽  
Michael Buchholz ◽  
Klaus Dietmayer
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Useful Life ◽  
Using Data ◽  
Health Diagnosis

Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life

2013 ◽  
Vol 717 ◽  
pp. 390-395 ◽  
Author(s):  
Lin Jiang ◽  
Wei Ming Xian ◽  
Bin Long ◽  
Hou Jun Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Linear Trend ◽  
Lithium Ion ◽  
Single Step ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Data Set ◽  
Useful Life

As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i.e., Support Vector Machine (SVM), Autoregressive Moving Average (ARMA), and Particle Filtering (PF) are presented for RUL prediction. The lithium-ion battery aging experiment data set has been trained to implement simulation. Based on the RUL prediction result, we can conclude that: (1) ARMA model achieved better result than SVM, however, the result shows a linear trend, which fail to properly reflect the degradation trend of the battery; (2) SVM often suffers from over fitting problem and is more suitable for single-step prediction; and (3) PF approach achieved a better prediction and reflected the trends of degradation of the battery owing to its combined with specific model.

scholarly journals Positron Annihilation Spectroscopy as a Diagnostic Tool for the Study of LiCoO2 Cathode of Lithium-Ion Batteries

2021 ◽  
Vol 6 (3) ◽  
pp. 28
Author(s):  
Gioele Pagot ◽  
Valerio Toso ◽  
Bernardo Barbiellini ◽  
Rafael Ferragut ◽  
Vito Di Noto
Keyword(s):  
Lithium Ion Batteries ◽  
Positron Annihilation ◽  
Positron Lifetime ◽  
Lithium Ion ◽  
Positron Annihilation Spectroscopy ◽  
State Of Charge ◽  
Doppler Broadening ◽  
Li Ion Batteries ◽  
Li Ion

Positron annihilation spectroscopy using lifetime and Doppler broadening allows the characterization of the lithiation state in LiCoO2 thin film used in cathode of lithium-ion batteries. The lifetime results reflect positron spillover because of the presence of graphite in between the oxide grains in real cathode Li-ion batteries. This spillover produces an effect in the measured positron parameters which are sensitive to delocalized electrons from lithium atoms as in Compton scattering results. The first component of the positron lifetime corresponds to a bulk-like state and can be used to characterize the state of charge of the cathode while the second component represents a surface state at the grain-graphite interface.

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