Early prediction of battery life by learning from both time-series and histogram data
Paper in proceeding, 2023

Due to dynamic operating conditions, random user behaviors, and cell-to-cell variations, accurately predicting battery life is challenging, especially using information from only a few early cycles. This work proposes a data-driven battery early prediction pipeline using both time-series, measurement-related features, and usage-related histogram features. We
first investigate the prediction performance of using these two feature sources individually, then two methods of systematically combining these two feature sources are devised. Additionally, four machine learning algorithms with different characteristics are applied to compare their performances on battery prognostic problems. We show that the prediction accuracy of using these two feature sources individually is comparable. Moreover, a systematic combination of these two features considerably improves the prediction performance in terms of accuracy and robustness, achieving excellent prediction results with a root mean square error of around 150 cycles using only the first 100 cycle’s data. Finally, experimental data of different cell types and cycling conditions are used to verify the developed method’s effectiveness and generality.

Machine learning Lithium-ion battery Battery life prediction Remaining useful life Battery management system

Author

Yizhou Zhang

Chalmers, Electrical Engineering, Systems and control

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Yicun Huang

Chalmers, Electrical Engineering, Systems and control

John Bergström

China-Euro Vehicle Technology (CEVT) AB

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

IFAC Proceedings Volumes (IFAC-PapersOnline)

14746670 (ISSN)

Vol. 56 2 3770-3775
9781713872344 (ISBN)

22nd World Congress of the International Federation of Automatic Control (IFAC)
Yokohama, Japan,

Data driven battery aging prediction

Swedish Energy Agency (50187-1), 2020-08-01 -- 2023-07-31.

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

Control Engineering

DOI

10.1016/j.ifacol.2023.10.1547

More information

Latest update

2/26/2024