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

Abstract

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 Rp = 16.66 mΩ and capacitance is identified as Cp=13.71 kF. The ohm internal resistance is calculated as Ro = 68.71 mΩ 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.

Evaluation of an in situ QAM‐based Power Line Communication system for lithium‐ion batteries

“…The BMS estimates the state of charge (SoC) and state of health (SoH) of the connected Li-ion cells within a battery pack, and uses this estimation to perform actions such as cell charge balancing, to mitigate overcharging and deep discharging; accelerated ageing; and permanent damage [13][14][15]. Many methods have been studied for the estimation of the SoC and SoH, including extended Kalman filter techniques [16,17], such as the splice Kalman filter algorithm [18].…”

Numerical research on a novel flow field design for vanadium redox flow batteries in microgrid

“…In this paper, a new channel is designed to improve the flow characteristics of the electrolyte and achieve the goal of improving cell performance. [23][24][25] 4.1 | Design and analysis of flow filed structure…”

Connecting battery technologies for electric vehicles from battery materials to management

“…Recently, Wang et al. ( Wang et al., 2020b ) improved an iterate calculation method to predict the SoC of the lithium-ion battery (LIB) packs more accurate by a novel splice Kalman filtering algorithm with adaptive robust modeling and noise correction, delivering a basic SoC prediction approach for LIB packs. The Ni-based battery could only be partially charged while a Li-ion battery would suffer from severe degradation and aging at high temperatures.…”