Practical battery state of health estimation using data-driven multi-model fusion

Paper i proceeding, 2023

Due to dynamic vehicle operating conditions, random user behaviors, and cell-to-cell variations, accurately estimating the battery state of health (SoH) is challenging. This paper proposes a data-driven multi-model fusion method for battery capacity estimation under arbitrary usage profiles. Six feasible and mutually excluded scenarios are meticulously categorized to cover all operating conditions. Four machine learning (ML) algorithms are individually trained using time-series data to estimate the current time step battery capacity. Additionally, a prediction model based on the histogram data is adopted from previous work to predict the next step capacity value. Then, a Kalman filter (KF) is applied to fuse all the estimation and prediction results systematically. The developed method has been demonstrated on cells operated under diverse profiles to verify its effectiveness and practicability.