A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

Thien C Pham

Abstract


Lithium-ion batteries (LIBs) have recently been used
widely in moving devices. Understand status of the batteries
can help to predict the failure and improve the effectiveness
of using them. There are some lithium-ion information that
define the battery health over time. These are state-of-charge
(SOC), state-of-health (SOH), and remaining-useful-life (RUL).
Normally, a LIB is working under charging and discharging
cycles continuously. In this paper, we will focus on the data
dependency of different time-slots in a cycle and in a sequence of
cycles to retrieve RUL. We leverage multi-channel inputs such as
temperature, voltage, current and the nature of peaks cross the
cycles to improve our prediction. Comparing to existing methods,
the experiments show that we can improve from 0.040 to 0.033
(reduce 17.5%) in RMSE loss, which is significant.


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DOI: http://dx.doi.org/10.21553/rev-jec.290

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