可靠性(半导体)
分位数
可靠性工程
电池(电)
锂(药物)
健康状况
分布(数学)
锂离子电池
计算机科学
计量经济学
统计
工程类
数学
物理
心理学
热力学
功率(物理)
数学分析
精神科
作者
Ying Zhang,Ming Zhang,Chao Liu,Zhaoxuan Feng,Yuchun Xu
标识
DOI:10.1016/j.ress.2024.110002
摘要
Lithium-ion batteries (LIBs) are widely used in many fields, such as electric vehicles and energy storage, and directly impact the device performance and safety. Therefore, the state of health (SOH) assessment is critical for LIB usage. However, most of the existing data-driven SOH modeling methods overlook the inherent uncertainty in battery health prediction, which decreases the reliability of the model. To address this issue, this paper proposes a novel SOH assessment model based on the deep learning framework. The SOH results are derived from the quantile distribution of deep features, giving the SOH values with associated confidence intervals. This enhances the reliability and generalization of SOH assessment results. Additionally, to complete the optimization of the deep model, a Wasserstein distance-based quantile Huber (QH) loss function is developed. This function integrates Huber loss and quantile regression loss, enabling the model to be optimized based on a distribution output. The proposed method is validated using the NASA dataset, and the results confirm that the proposed method can effectively estimate the SOH of LIB while accounting for uncertainty. The incorporation of SOH distribution enhances the reliability and generalization ability of the SOH assessment model.
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