A Novel Simplified State-of-Energy Estimation Method for Lithium Battery Pack Based on the “Representative Cell” Selection by the State Machine

Accurate state of energy (SOE) estimation of the battery pack is the key to determining the driving range of electric vehicles. Due to the cell-to-cell inconsistency among the individual cells of a battery pack, online SOE estimation of battery packs is still a pressing problem in the practical application of the existing battery management system. In this context, this paper proposes a novel simplified SOE estimation method for a series-connected lithium-ion battery pack based on the “representative cell” selection by the state machine. Firstly, the operating state of the battery pack is determined by the average value of the battery pack voltage and the state machine, and representative cells are selected according to the corresponding state. Subsequently, the Recursive Least Square algorithm and the Extended Kalman Filter algorithm were applied to estimate the SOE of the representative cells. Finally, the battery pack SOE is calculated by the adaptive weighted strategy. The results show that under UDDS conditions, the proposed method for estimating the battery pack SOE is within 3% error, while the complexity of the calculation does not increase.