Combining offline and online machine learning to estimate state of health of lithium-ion batteries
Paper in proceeding, 2022

This article reports a new state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with cell inconsistency and online implementability are addressed using a proposed individualized estimation scheme that blends a model migration method with ensemble learning. A set of candidate models, based on slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are first trained offline by choosing a single-point feature on the incremental capacity curve as the model input. For online operation, the prediction errors due to cell inconsistency in the target new cell are next mitigated by a proposed modified random forest regression (mRFR) for high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably high SOH estimation accuracy with only a small amount of early data and online measurements are needed for practical operation.


Chengqi She

Beijing Institute of Technology

Chalmers, Electrical Engineering, Systems and control

Yang Li

Chalmers, Electrical Engineering, Systems and control

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

2022 European Control Conference, ECC 2022

9783907144077 (ISBN)

2022 European Control Conference (ECC)
London, ,

Data driven battery aging prediction

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

Driving Forces

Sustainable development

Areas of Advance



Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing



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