An improved smoothing factor-extended kalman filtering method for accurate online state-of-charge estimation of Lithium-ion battery

Abstract

The state of charge of battery is an important index of battery management system. The Lithium-ion batteries are widely used in various industries, so they are full of uncertainty, which makes them difficult to estimate the state of charge of Lithium-ion batteries. To solve the problem of low accuracy and large variability in real-time estimation of Lithium-ion batteries, taking Lithium-ion batteries as the research object, the Thevenin model is used to simulate the working characteristics. On the basis of the extended Kalman filtering algorithm, through the influence of the covariance matrix and noise, a smoothing factor is introduced to increase the Kalman gain and improve flexibility. Experiments have proved that the smoothing factor-extended Kalman algorithm improves the flexibility of the algorithm, and at the same time reduces the non-linear error caused by the rapid charging and discharging changes of Lithium-ion batteries. In the Hybrid Pulse Power Characterization test, the maximum estimation error is 0.14%, and the average estimation error is 0.1%. It provides a new method for estimating the state of charge of Lithium-ion batteries.