Affiliation:
1. Department of Industrial Engineering and Management and the State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China
Abstract
AbstractAccurate capacity estimation and degradation trajectory predictions are crucial part of effectively monitoring the current and future health status of lithium‐ion batteries, which ensures their optimal performance and longevity. However, it is hard to acquire precise predictions under various application conditions when relying on limited historical data. In this paper, we propose a novel deep time‐index framework based on meta‐learning to achieve online capacity estimation and degradation trajectory predictions with only a few monitoring data from early cycles. Highly correlated health feature from lithium‐ion battery degradation data is extracted for online capacity estimation. The deep time‐index framework consists of three parts, Implicit Neural Representations (INRs), Long Short‐Term Memory (LSTM) and Ridge regressor. Cycle numbers are selected as time feature and fed into the INRs to obtain informative representations. A LSTM module is employed to process the temporal representations and capture the long‐term dependencies to Ridge regressor to generate a direct multi‐step predictions. Three distinct lithium‐ion battery degradation datasets are applied to validate the efficacy of the proposed approach. The experimental results validate that within the deep time‐index framework, the short‐term predictions show remarkable performance. This high level of accuracy makes the proposed deep time‐index framework suitable for a wide range of real‐world scenarios, as it demonstrates precise predictions even under varying charging and discharging conditions with limited historical data.
Funder
National Natural Science Foundation of China
Subject
Management Science and Operations Research,Safety, Risk, Reliability and Quality
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献