Technical Paper
Lithium-Ion Battery State of Health Estimation Based on Semi-Supervised Regression with Field Data Validation
2024-04-09
2024-01-2200
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 field data and semi-supervised regression is proposed 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 2%, as validated by the full-charge test of the battery packs.