A Cell-to-Pack State Estimation Extension Method Based on a Multilayer Difference Model for Series-Connected Battery Packs

Battery degradation and cell inconsistency challenge the existing state estimation methods, which often ignore the influence of cell inconsistency or suffer from heavy computation complexity. To address the difficulty of state estimation by cell inconsistency and realize joint estimation of the state-of-charge (SOC) and capacity for series-connected battery packs, a novel cell-to-pack state estimation extension method based on a multilayer difference model (MDM) is investigated. The proposed extension method can efficiently realize accurate SOC and capacity estimation for a battery pack based on the existing estimation algorithms for a single cell. Two state-difference estimators for SOC and capacity differences are constructed and realized separately through adaptive extended Kalman filter and recursive least squares algorithm. Considering that time-varying characteristics of states and state differences are different, the MDM estimator runs in multiple timescales. Based on battery pack cycling experiments, the cell-to-cell consistency evolution during aging is revealed. The proposed MDM's accuracy, efficiency, and adaptability are verified through the experiments of a series-connected battery pack under different dynamic conditions. The proposed method also shows adaptability to various battery temperatures and different cell inconsistencies. The maximum SOC root-mean-squared error and capacity average error for battery pack during aging are 2.35% and 3.02%, respectively.