Novel reduced-order modeling method combined with three-particle nonlinear transform unscented Kalman filtering for the battery state-of-charge estimation

Accurate estimation of the lithium-ion battery state of charge plays an important role in the real-time monitoring and safety control of batteries. In order to solve the problems that the real-time estimation of the lithium-ion battery is difficult and the estimation accuracy is not high under various working conditions, a lithium-ion battery is taken as a research object, and the working characteristics of the lithium-ion battery are studied under various working conditions. In order to reduce the computational complexity of the traditional unscented Kalman algorithm, an improved unscented Kalman algorithm is proposed. Considering the importance of accurately estimating the initial state of charge for later estimation, the initial estimation value is calibrated by using the open-circuit voltage method. Then, the improved unscented Kalman filter algorithm based on a reduced-order model is used for assessing and tracking to realize real-time high-precision estimation of the state of charge of the lithium-ion battery. A simulation model is built and combined with a variety of working conditions data for performance analysis. The experimental results show that the convergence speed and tracking effect are good and that the estimation error control is within 0.8%. It is verified that the reduced order of the three-particle nonlinear transform unscented Kalman results in higher accuracy in the state-of-charge estimation of lithium-ion batteries.