A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
Artikel i vetenskaplig tidskrift, 2022

Accurately predicting batteries’ ageing trajectory and remaining useful life is not only required to ensure safe and reliable operation of electric vehicles (EVs) but is also the fundamental step towards health-conscious use and residual value assessment of the battery. The non-linearity, wide range of operating conditions, and cell to cell variations make battery health prediction challenging. This paper proposes a prediction framework that is based on a combination of global models offline developed by different machine learning methods and cell individualised models that are online adapted. For any format of raw data collected under diverse operating conditions, statistic properties of histograms can be still extracted and used as features to learn battery ageing. Our framework is trained and tested on three large datasets, one being retrieved from 7296 plug-in hybrid EVs. While the best global models achieve 0.93% mean absolute percentage error (MAPE) on laboratory data and 1.41% MAPE on the real-world fleet data, the adaptation algorithm further reduced the errors by up to 13.7%, all requiring low computational power and memory. Overall, this work proves the feasibility and benefits of using histogram data and also highlights how online adaptation can be used to improve predictions.

State of health prediction

Machine learning

Lithium-ion batteries

Real-world fleet data

Online adaptive learning

Remaining useful life

Författare

Yizhou Zhang

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

China-Euro Vehicle Technology (CEVT) AB

Torsten Wik

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

John Bergström

China-Euro Vehicle Technology (CEVT) AB

Michael Pecht

A. James Clark School of Engineering

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Journal of Power Sources

0378-7753 (ISSN)

Vol. 526 231110

Ämneskategorier

Datorteknik

Energiteknik

DOI

10.1016/j.jpowsour.2022.231110

Mer information

Senast uppdaterat

2022-02-24