Abstract
High-precision estimation of lithium battery SOC can effectively optimize vehicle energy management, improve lithium battery safety protection, extend lithium battery cycle life, and reduce new energy vehicle costs. Based on the forgetting factor recursive least square method (FFRLS), Thevenin equivalent circuit model and Singular Value Decomposition-Unscented Kalman Filter (SVD-UKF), the SVD-UKF combined lithium battery SOC estimation algorithm with model capacity update is proposed, aiming at further improving the SOC estimation accuracy of lithium battery. The parameter identification of Thevenin model is studied by using the forgetting factor recursive least square method. To overcoming the shortcomings of Kalman filter linearization error and non-positive definite covariance matrix, the singular value decomposition unscented Kalman filter algorithm is proposed. It is worth mentioning that in order to consider the impact of battery available capacity attenuation on the estimation of lithium battery SOC, the model capacity update algorithm is used to optimize the model parameters and state joint estimation algorithm based on FFRLS & SVD-UKF. Verified by simulation and lithium battery test, the results show that the SVD-UKF algorithm based on model capacity update can accurately estimate the SOC of lithium battery in real time with the available capacity of lithium battery continuous attenuation. The purpose of improving the accuracy of SOC estimation of lithium batteries is achieved.
Highlights
State of charge (SOC) estimation of power battery is one of the core functions of battery management system
In this paper, the Singular Value Decomposition-Unscented Kalman Filter (SVD-unscented Kalman filter (UKF)) combined lithium battery SOC estimation algorithm with model capacity update is proposed to further improve the SOC estimation accuracy of lithium battery, which is based on the forgetting factor recursive least square method (FFRLS) and Thevenin equivalent circuit model
The parameters of battery model are identified by combining the forgetting factor recursive least square method with singular value decomposition unscented Kalman filter
Summary
State of charge (SOC) estimation of power battery is one of the core functions of battery management system. In reference [9], the battery state space model is obtained through mathematical derivation, and different Qw and Rv settings are adopted based on Kalman filter to improve the estimation performance and effectively reduce the root mean square error. In reference [12], an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithiumion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. In this paper, the Singular Value Decomposition-Unscented Kalman Filter (SVD-UKF) combined lithium battery SOC estimation algorithm with model capacity update is proposed to further improve the SOC estimation accuracy of lithium battery, which is based on the forgetting factor recursive least square method (FFRLS) and Thevenin equivalent circuit model.
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