As an effective approach to operation of source network load

battery energy storage has been developed toward larger scale direction

and the safe and reliable operation of energy storage battery is the key to restrict its further development and application. Aiming at solving the problem of state estimation and early warning of lithium-ion battery

this paper proposes a nonlinear state estimation method based on data model hybrid drive. Firstly

the classical Thevenin circuit model and extended Kalman filter are used to build the mathematical model of lithium battery. Secondly

a nonlinear estimation method based on the deviation between the actual system and the model simulation system is proposed to realize the effective estimation of the log modulus deviation

and then realize the pre alarm process of the battery. Finally

the simulation example verifies that the proposed method is little affected by the model accuracy

error

and charge/discharge current amplitude. In this paper

it is the first time that the nonlinear state estimation technology(NSET) is applied to lithium battery early warning. The extended Kalman filter(EKF) combined with the data-driven method(nset) can accurately estimate the real-time state of lithium energy storage battery and give early warning through simple calculation

which has practical application value in engineering.