Residual life prediction of lithium-ion batteries by non-equidistance grey forecasting model

2014 ◽  
Author(s):  
Zhao Liang-liang ◽  
Xiao Ming-qing ◽  
Sheng Sheng ◽  
Tingting Ren
Keyword(s):  
Lithium Ion Batteries ◽  
Life Prediction ◽  
Residual Life ◽  
Lithium Ion ◽  
Forecasting Model ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Residual Life Prediction

scholarly journals Reliability Evaluation and In-Orbit Residual Life Prediction for Satellite Lithium-Ion Batteries

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Bin Yu ◽  
Tao Zhang ◽  
Tianyu Liu ◽  
Lei Yao
Keyword(s):  
Lithium Ion Batteries ◽  
Life Prediction ◽  
Residual Life ◽  
Interval Estimation ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Scale Transformation ◽  
Capacity Degradation ◽  
Battery Capacity

As a new type of secondary battery, lithium-ion battery is widely used in the aerospace industry with the advantages of long lifetime, high energy density and low pollution, etc. In this paper, we focus on the problem of offline and online life prediction for satellite lithium-ion batteries. Firstly, based on the NASA laboratory battery dataset, a Wiener process with time-scale transformation is used to capture battery capacity fading, and then the battery reliability point and interval estimation equation are derived, respectively. Secondly, by analyzing the charge and discharge profiles of the batteries in orbit environments, an accurate capacity prediction model is proposed based on the partial charging curves. Finally, the Bayesian framework is used to perform capacity degradation model online updating, and the analytical expression of residual life distribution is derived to achieve RL prediction for in-orbit satellite lithium-ion batteries.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

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