| Title |
Machine Learning-based SOH Estimation Algorithm Using a Linear Regression Analysis |
| Authors |
Seung-Hyun Kang ; Tae-Won Noh ; Byoung-Kuk Lee |
| DOI |
10.6113/TKPE.2021.26.4.241 |
| Keywords |
BMS (Battery Management System); SOH (State-of-Health); ML (Machine Learning); OCV (Open Circuit Voltage); Linear coefficient |
| Abstract |
A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles. |