State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion
Journal article, 2024

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

Multi-model fusion

Machine learning

SoH estimation

Battery capacity estimation

Kalman filter

Author

Yizhou Zhang

Chalmers, Electrical Engineering, Systems and control

China-Euro Vehicle Technology (CEVT) AB

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

John Bergström

China-Euro Vehicle Technology (CEVT) AB

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. 10 1 1494-1507

Data driven battery aging prediction

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

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

DOI

10.1109/TTE.2023.3267124

More information

Latest update

5/20/2024