A statistical distribution-based pack-integrated model towards state estimation for lithium-ion batteries

The estimation of lithium battery pack is always an essential but troubling issue which has difficulty on considering the inconsistency during state estimation. Herein, an innovative statistical distribution-based pack-integrated model for lithium-ion batteries is proposed and applied for state estimation including state of charge and state of energy. The proposed method highlights the modelling concepts that the terminal voltage of the pack-integrated virtual cell is determined by all cells inside the pack, which takes the advantages of a designed dynamic-weighted terminal voltage according to the voltage distribution inside battery pack. Then, the issue of battery pack modelling and state estimation can be transferred into a virtual single cell and no longer have to consider the inconsistency within battery pack, with the advantages for further extending application from conventional battery modelling method based on single cell. Two kinds of mainstream batteries are experimented for validating, including lithium iron phosphate battery and LiNi_0·5Co_0·2Mn_0·3O₂, battery, and both have satisfactory precision, where the maximum error is about 1%–2%, and root mean squared error (RMSE) is eliminated to about 1%. The proposed method is validated with better precision performances on estimating states of battery pack with less calculation and storage, and can be applied both on embedded systems and cloud management platforms.