State of charge estimation for lithium‐ion battery based on an intelligent adaptive unscented Kalman filter

Abstract

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.

Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

“…[ 11 ] The filter‐based SOC estimation methods are such as extended Kalman filter (EKF), [ 24–26 ] multi‐innovation extended Kalman filter (MI‐EKF), [ 27 ] adaptive extended Kalman filter (AEKF), [ 28,29 ] dual extended Kalman filter (DEKF), [ 17 ] unscented Kalman filter (UKF), [ 30,31 ] and adaptive unscented Kalman filter (AUKF). [ 32,33 ] Most studies among them, however, have been restricted to only regarding the OCV as a function of SOC; the effect of temperature on OCV has received little attention.…”

An improved coulomb counting method based on dual open‐circuit voltage and real‐time evaluation of battery dischargeable capacity considering temperature and battery aging

“…1 However, LIB systems with high energy density have become a major safety hazard in the process of driving under uncertain road traffic conditions. 2 To ensure driving safety, improve the service life and energy efficiency of the battery, and realize precise management of the LIB system, it is necessary to design a stable and effective EV battery management system (BMS) for realtime monitoring and protection. 3 State of charge (SOC) estimation of LIB is one of the key technologies of BMS.…”

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

“…To analyze the performance and estimate a battery state (the state of health (SOH), state of power (SOP), and state of charge (SOC)), it is necessary to model a practical battery and estimate the battery model parameters in realtime accurately. [12][13][14][15] Conventional battery models include an empirical model, electrochemical model, and equivalent circuit model (ECM). The current and SOC are related to battery voltage, and an empirical model of a battery can be established based on these characteristics.…”

“…As a battery is a complex electrochemical‐physical system, it is difficult to describe its behavior and estimate its state accurately. To analyze the performance and estimate a battery state (the state of health (SOH), state of power (SOP), and state of charge (SOC)), it is necessary to model a practical battery and estimate the battery model parameters in real‐time accurately 12‐15 …”