Data-driven prediction of battery cycle life
Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.
Second, the external and internal factors affecting the cycle life of lithium-ion batteries are investigated in detail, including temperature, charge/discharge multiplier, charge/discharge cut-off voltage, cell performance inconsistency, solid electrolyte interphase (SEI) film, and copper foil.
Therefore, the experiment data showed that power lithium-ion batteries directly affected the cycle life of the battery pack and that the battery pack cycle life could not reach the cycle life of a single cell (as elaborated in Fig. 14, Fig. 15). Fig. 14. Assessment of battery inconsistencies for different cycle counts . Fig. 15.
Among all power batteries, lithium-ion power batteries are widely used in the field of new energy vehicles due to their unique advantages such as high energy density, no memory effect, small self-discharge, and a long cycle life [,, ]. Lithium-ion battery capacity is considered as an important indicator of the life of a battery.
Mentioning the service life of power lithium-ion batteries, developing the high-property cathode/anode materials, high-security electrolytes, separator with superior safety properties is very vital. The corresponding measurements aim to increase the charge storage capacity, furtherly the service life.
Chen et al., in their verification of the factors influencing the life of hybrid batteries, found that after 12,000 cycles, the capacity of batteries with depths of discharge (DODs) of 1 and 0.8 decreased significantly, while the life of batteries with a DOD of 0.5 was more stable (as described in Fig. 12).
Comparison of lithium-ion battery life prediction methods. The data-driven method establishes a prediction model based on the statistical laws of historical data, without considering the physical and chemical reactions inside the battery, and can quickly predict the state and life of the battery.

Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.
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