Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation
Journal article, 2022

This article proposes an adaptive state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with feature extraction, cell inconsistency, and online implementability are specifically solved using a proposed individualized estimation scheme blending offline model migration with online ensemble learning. First, based on the data of pseudo-open-circuit voltage measured over the battery lifespan, a systematic comparison of different incremental capacity features is conducted to identify a suitable SOH indicator. Next, a pool of candidate models, composed of slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are trained offline. For online operation, the prediction errors due to cell inconsistency in the target new cell are then mitigated by a proposed modified random forest regression (mRFR) based ensemble learning process with high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably improved SOH estimation accuracy (15%) with only a small amount of early-age data and online measurements are needed for practical operation. Furthermore, the applicability of the proposed SBC-RBFNN-mRFR algorithms to real-world operation is validated using measured data from electric vehicles, and it is shown that a 38% improvement in estimation accuracy can be achieved.

Lithium-ion batteries

state of health estimation

modified random forest regression

incremental capacity analysis

online machine learning


Chengqi She

Beijing Institute of Technology

Chalmers, Electrical Engineering, Systems and control

Yang Li

Chalmers, Electrical Engineering, Systems and control

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Zhenpo Wang

Beijing Institute of Technology

Fengchun Sun

Beijing Institute of Technology

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. 8 2 1604-1618

Data driven battery aging prediction

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

Driving Forces

Sustainable development

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Subject Categories

Energy Engineering

Control Engineering

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering



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