Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model

With the advantages of accurate and elaborate, the application of the electrochemical models has become a potential trend for states estimation of Li-ion batteries in advanced battery management systems (BMS). This paper presents an original approach for joint estimation of state-of-charge (SoC) and state-of-health (SoH) based on the pseudo-two-dimensional (P2D) model. First, the standard P2D model is simplified and reformulated into a nonlinear state-space form with guaranteed observability. Next, the particle filter (PF) algorithm is employed to estimate the average lithium concentration in real time for SoC calculation. In addition, the battery SoH is calibrated based on the prediction of the average lithium concentration at the cut-off voltages of charging and discharging, which, in turn, improves the accuracy of online SoC estimation for aged batteries. For validation purposes, fifteen batteries with the aging state from 100% to 70% of initial capacity are tested under dynamic current profiles. Results show that the maximum SoH estimation error can be limited to 2.8%, and the SoC estimation error is bounded by 2% for new and aged batteries with the calibrated SoH.