Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging
  Estimation and Prediction Based on Relaxation Voltage Curves
Yue Xiang Bo Jiang Haifeng Dai
The degradation process of lithium-ion batteries is intricately linked to
their entire lifecycle as power sources and energy storage devices,
encompassing aspects such as performance delivery and cycling utilization.
Consequently, the accurate and expedient estimation or prediction of the aging
state of lithium-ion batteries has garnered extensive attention. Nonetheless,
prevailing research predominantly concentrates on either aging estimation or
prediction, neglecting the dynamic fusion of both facets. This paper proposes a
hybrid model for capacity aging estimation and prediction based on deep
learning, wherein salient features highly pertinent to aging are extracted from
charge and discharge relaxation processes. By amalgamating historical capacity
decay data, the model dynamically furnishes estimations of the present capacity
and forecasts of future capacity for lithium-ion batteries. Our approach is
validated against a novel dataset involving charge and discharge cycles at
varying rates. Specifically, under a charging condition of 0.25C, a mean
absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores
the model's adeptness in harnessing relaxation processes commonly encountered
in the real world and synergizing with historical capacity records within
battery management systems (BMS), thereby affording estimations and
prognostications of capacity decline with heightened precision.