Battery lifetime prediction by pattern recognition. Application to lead-acid battery life-cycling test data

Perone, S. P.; Spindler, W.C.

doi:10.1016/0378-7753(84)80051-8

Cited by 12 publications

(1 citation statement)

References 7 publications

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“…e capacity of a battery is an important indicator of battery performance, and its lifespan is an important indicator of its health. When the battery capacity decreases to a certain critical point (typically 80% of the rated capacity, but different battery types have different thresholds), the battery is considered to be invalid [10]. Remaining service life refers to the remaining service life of a battery after it has been used for a period of time [11][12][13][14][15][16].…”

Section: Battery Life Predictionmentioning

confidence: 99%

Residual Life Prediction of Lithium Batteries Based on Data Mining

Qin

2022

Computational Intelligence and Neuroscience

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Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life.

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