Tackling Limited Labeled Field Data Challenges for State of Health Estimation of Lithium-Ion Batteries by Advanced Semi-Supervised Regression Chongqing University

Format:

Book

Conference/Event

Author/Creator:

Li, Jinwen, author.

Contributor:

Chen, Wenqiang

Hu, Xiaosong

Khalatbarisoltani, Arash

Lin, Xianke

Liu, Hongao

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs. Subsequently, statistical features are extracted from charging data, and health indicator sets are selected by two correlation analysis methods (Pearson correlation and grayscale correlation). Moreover, two regressors are trained by learning the mapping between labeled SOH and various health indicator sets. To enhance the training dataset, semi-supervised with co-training is utilized to estimate pseudo-labels for unlabeled charging data. The final SOH estimation is achieved through the fusion of these two regressors. Finally, the proposed method is validated using field data from 20 electric forklifts collected over approximately one year. Remarkably, even with only 10 labeled data points, the proposed method achieves a mean absolute error in SOH estimation of a mere 3.96%. This represents a significant reduction of 20% compared to the traditional supervised learning method. Compared with the two benchmarks without co-training, the estimation error drops by 7.69% and 8.76%, respectively

Notes:

Vendor supplied data

Publisher Number:

2024-01-2200

Access Restriction:

Restricted for use by site license