A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias

Abstract

Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness.

A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries

“…With the increasingly serious problems of environmental pollution and energy shortage, related researchers all over the world are paying more and more attention to sustainable development [1][2][3]. Therefore, the existing energy structure is bound to change, and the proportion of clean energy in the energy structure will become higher and higher.…”