A New Battery Life Prediction Method for Geosynchronous Spacecraft

1987 ◽  
Author(s):  
J. W. Lyons ◽  
H. W. Earl ◽  
J. D. Dunlop
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Battery Life

scholarly journals A battery life prediction method for hybrid power applications

1997 ◽  
Author(s):  
Stephen Drouilhet ◽  
Bertrand Johnson ◽  
Stephen Drouilhet ◽  
Bertrand Johnson
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Battery Life ◽  
Hybrid Power

scholarly journals Life Prediction Method of Power Battery for New Energy Vehicle

CONVERTER ◽  
2021 ◽  
pp. 635-642
Author(s):  
Guanqiang Ruan, Jinliang Cao, Xing Hu
Keyword(s):  
Life Prediction ◽  
Evaluation Method ◽  
Prediction Method ◽  
Battery Life ◽  
Degradation Data ◽  
Power Battery ◽  
New Energy ◽  
Life Model ◽  
New Energy Vehicle ◽  
Failure Life

Battery life is a key factor restricting the development of new energy vehicle manufacturing industry.Taking a LiMn2O4 power battery as an example, the life prediction method of power battery based on performance degradation data was studied. It is found that the capacity of the battery basically conforms to the decline trajectory of the power function, and the pseudo failure life of the battery is extrapolated by using the decline model. The distribution of pseudo failure life is studied, and the results are consistent with the fitting effect of Weibull distribution. Finally, the reliability of the battery is evaluated based on Weibull life distribution. This paper analyzes the influence of the actual cycle data on the prediction accuracy. The test results show that the life model and reliability evaluation method have high accuracy, and solve the problems of long life evaluation cycle and high cost of power battery.

scholarly journals Battery life prediction method based on DE-GWO-LSTM

2021 ◽  
Vol 2076 (1) ◽  
pp. 012105
Author(s):  
Yongsheng Shi ◽  
Jiarui Ren ◽  
Mengzhuo Shi ◽  
Jin Li ◽  
Kai Zhang
Keyword(s):  
Lithium Ion Battery ◽  
Life Prediction ◽  
Differential Evolution Algorithm ◽  
Prediction Method ◽  
Lithium Ion ◽  
Population Diversity ◽  
Battery Life ◽  
Grey Wolf ◽  
Evolution Algorithm ◽  
Life Prediction Model

Abstract Aiming at the problem of inaccurate prediction results of lithium-ion battery life, a lithium-ion battery life prediction model based on hybrid algorithm is designed. The position of grey wolf algorithm is updated by differential evolution algorithm, which improves the population diversity and avoids premature stagnation of the algorithm. The GWO-LSTM model and DE-GWO-LSTM model are compared and analyzed by using NASA data. The proposed DE-GWO-LSTM can well conduct global search and local search, and improve the prediction performance to a certain extent.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

Remaining useful life prediction method for machine tools based on meta-action theory

2021 ◽  
pp. 1748006X2110025
Author(s):  
Zongyi Mu ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hongwei Wang ◽  
Xin Yang
Keyword(s):  
Machine Tools ◽  
Life Prediction ◽  
Action Theory ◽  
Prediction Method ◽  
Operational Reliability ◽  
Remaining Useful Life ◽  
Practical Application ◽  
Dynamic Prediction ◽  
Degradation Model ◽  
Useful Life

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

scholarly journals Novel Practical Life Cycle Prediction Method by Entropy Estimation of Li-Ion Battery

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 487
Author(s):  
Tae-Kue Kim ◽  
Sung-Chun Moon
Keyword(s):  
Lithium Ion Batteries ◽  
Prediction Method ◽  
Lithium Ion ◽  
Estimation Accuracy ◽  
Battery Life ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Basic Law ◽  
Life Problems ◽  
Li Ion

The growth of the lithium-ion battery market is accelerating. Although they are widely used in various fields, ranging from mobile devices to large-capacity energy storage devices, stability has always been a problem, which is a critical disadvantage of lithium-ion batteries. If the battery is unstable, which usually occurs at the end of its life, problems such as overheating and overcurrent during charge-discharge increase. In this paper, we propose a method to accurately predict battery life in order to secure battery stability. Unlike the existing methods, we propose a method of assessing the life of a battery by estimating the irreversible energy from the basic law of entropy using voltage, current, and time in a realistic dimension. The life estimation accuracy using the proposed method was at least 91.6%, and the accuracy was higher than 94% when considering the actual used range. The experimental results proved that the proposed method is a practical and effective method for estimating the life of lithium-ion batteries.

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