锂离子电池老化估计和预测的深度学习混合模型

Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction

摘要:

锂离子电池的衰退过程关系到其作为动力源和储能元件的整个生命周期，包括但不限于性能发挥和梯次利用等。因此，准确和快速地估计或预测锂离子电池的老化状态引起了广泛的关注。然而，现有研究大多集中在老化估计和预测的某一方面，缺少对二者的动态结合。本文提出了一种基于深度学习的容量老化估计和预测的混合模型，从充电和放电弛豫过程中提取与老化高度相关的特征，结合历史容量衰减数据，动态地给出锂离子电池当前容量的估计值和未来容量的预测值。在一个不同倍率充放电的新数据集上验证了我们的方法，针对0.25 C充电工况，取得了0.29%的MAPE。这一结果表明，该模型能够充分利用真实世界中常见的弛豫过程，结合BMS中记录的历史容量数据，以较高精度同时给出容量衰退的估计值和预测值。

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 0.25 C， 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.