  For the battery management system of electric vehicle, accurate estimation of
the State of Charge of Lithium-ion battery can effectively avoid structural
damage caused by overcharge or over discharge inside the battery. Considering
that the lithium-ion battery is a time-varying nonlinear system, which needs
real-time State of Charge estimation, a joint algorithm of forgetting factor
recursive least squares and improved adaptive H Infinity Extended Kalman Filter
is proposed for online estimation of model parameters and state of charge.
Firstly, Thevenin equivalent circuit model is built in Simulink of MATLAB
R2021b, and the model parameters are estimated by forgetting factor recursive
least square in real time. Secondly, the improved adaptive H Infinity Extended
Kalman Filter is used to estimate State of Charge in true time. Finally, the
feasibility of the algorithm is verified by two different lithium-ion battery
conditions. The experimental results show that improved adaptive H Infinity
Extended Kalman Filter has the highest and most stable State of Charge
estimation accuracy than the other three comparison methods. The Root Mean
Square Error and Mean Absolute Error are 0.6008 % and 0.3578 % under the
Dynamic Stress Test condition, and 1.0068 % and 0.8721 % under the Federal
Urban Driving Schedule condition respectively
