Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves

TL;DR

This paper introduces a deep learning hybrid model that accurately estimates and predicts lithium-ion battery aging by analyzing relaxation voltage curves and historical capacity data, enhancing battery management systems.

Contribution

It presents a novel hybrid deep learning approach that simultaneously estimates current capacity and forecasts future aging, integrating relaxation process features with historical data.

Findings

Achieved a MAPE of 0.29% under 0.25C charging conditions.

Effectively utilizes relaxation voltage curves for aging estimation.

Demonstrates improved prediction accuracy over existing methods.

Abstract

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.