Current-Split Estimation in Li-Ion Battery Pack: An Enhanced Weighted Recursive Filter Method

Lithium ion (Li-ion) battery pack is a complex system consisting of numerous cells connected in parallel and series. The performance of the pack is highly dependent on the health of each individual in-pack cell. An overcharged or discharged cell connected in a parallel string could change the total capacity of the battery pack. In a pack, current-split estimation plays an important role to monitor the cell functions. Therefore, a scheme is required to estimate current-split accurately, which can thereby help to improve the overall pack performance. To what follows, a recursive weighted covariance-based estimation method (RWEM) was proposed to estimate the current-split of each set of parallel connected cells. RWEM assigns weights to the interconnected cell structure by using correlation information between battery parameters in order to estimate the current-split. This was achieved by first deriving the one-step prediction error method, where consistency for covariance was proved. Furthermore, iterative recursion for sparse measurements was also considered. Performance evaluations were conducted by analyzing sets of realtime measurements collected from Li-ion battery pack used in electric vehicles (EVs). Results show that the proposed filter accurately estimated the battery parameters even in the presence of faults and random-noise variances.