A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm

As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as R-p = 16.66 m Omega and capacitance is identified as C-p = 13.71 kF. The ohm internal resistance is calculated as R-o = 68.71 m Omega and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs.