Online joint estimator of key states for battery based on a new equivalent circuit model

Abstract

Accurate estimation of the key states of batteries is essential for safe and reliable battery operation. In this paper, a joint estimation method for state of health (SOH), state of charge (SOC) and state of power (SOP) of batteries based on the autoregressive equivalent circuit model (AR-ECM) is proposed. Firstly, considering the coupling relationship existing between these key states of the battery, the state space-coupling model based on AR-ECM is proposed. Then, by analyzing the different characteristics of the model parameters, a differentiated model parameter identification strategy is proposed. Finally, based on the accurate estimation of the model parameters, the square root unscented Kalman filter (SR-UKF) is used to realize the online estimation of SOH and SOC. The SOP estimation under multiple constraints is realized based on the updated state, voltage, and current. Experimental results in noise free and Gaussian white noise environments show that the multi-state joint estimation algorithm has high estimation accuracy and robustness.