Work

Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries

Year: 2024

Type: article

Abstract: To ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is challenging. Therefore, building data‐driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short‐term model and the remaining useful life of batteries using a long‐term iterative model. The validity of this method is verified using the open‐source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.

Source: IET Energy Systems Integration

DOI: 10.1049/esi2.12171

Authors: Yuhao Zhang, Yunfei Han, Tao Cai, Jia Xie, Shijie Cheng

Institutions: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,200