A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method

A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery pa-rameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the com-posite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 degrees C and 40 degrees C to verify and analyze the proposed method. The results of battery state estimation indicate that the proposed framework can accurate estimation battery internal states and also provide an effective reference for the driving of powered vehicles.

Automated summary

This paper proposes a co-estimation framework for estimating the state of charge (SOC), state of power (SOP), and battery available capacity of battery systems. The framework utilizes the first-order equivalent model method, ampere-time integration method, and Kalman filters to accurately estimate battery internal states. Experimental data validates the effectiveness of the proposed method and its potential application in the driving of powered vehicles.