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.

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 Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1685 ◽  
Author(s):  
Xiaodong Xu ◽  
Chuanqiang Yu ◽  
Shengjin Tang ◽  
Xiaoyan Sun ◽  
Xiaosheng Si ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Engineering Application ◽  
Prediction Method ◽  
Lithium Ion ◽  
Relaxation Effect ◽  
Remaining Useful Life ◽  
Parameters Estimation ◽  
Wiener Processes ◽  
Useful Life

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

scholarly journals State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4858
Author(s):  
Zhonghua Yun ◽  
Wenhu Qin ◽  
Weipeng Shi ◽  
Peng Ping
Keyword(s):  
Lithium Ion Batteries ◽  
Hybrid Approach ◽  
Lithium Ion ◽  
The State ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Data Series ◽  
State Of Health ◽  
Useful Life ◽  
Item Data

Generally, the State-of-Health (SOH) monitoring and Remaining Useful Life (RUL) prediction and assessment of lithium-ion (Li-ion) batteries need to use sensors to obtain the degradation test data of the same type of batteries and establish the degradation model for reference. However, when the battery type is unknown, a usable reference model cannot be obtained, so its prediction and evaluation may be relatively inconvenient. In this paper, the State of-Health prediction for lithium-ion batteries based on a novel hybrid scheme is proposed. Firstly, historical charge/discharge time series and capacity series are extracted to analyze and construct Health Indicators, then using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the Health Indicator series into the trend and non-trend terms. Among them, the relatively smooth trend item data series uses the Autoregressive Integrated Moving Average model (ARIMA) for prediction; when dealing with the data series of non-trend items which are obviously non-smooth and seemingly random, the residuals predicted by ARIMA and the non-trend items obtained by CEEMDAN decomposition are combined into new non-trend items; then the least square support vector machine (LSSVM) is introduced to build a nonlinear prediction model and make predictions. Finally, combining the prediction results of the trend item data series and the non-trend item data series as a reference for the assessment of the state of health and remaining useful life. The 13 experimental results of 3 batteries verify the effectiveness of the scheme.

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