Research on optimized SOC estimation algorithm based on extended kalman filter

Abstract

The paper studies the estimation of state of charge (SOC) of batteries. Firstly, the research status of battery management system, battery equivalent model and SOC estimation algorithm is introduced, and the performance of common equivalent circuit model and SOC estimation algorithm in complexity and accuracy is compared and analyzed. On this basis, this paper proposes an extended Kalman filter (EKF) algorithm based on the first-order RC model, and optimizes it by piecewise fitting. The accuracy of the optimized EKF algorithm is greatly improved. Finally, the modeling and simulation are completed through MATLAB/SIMULINK, and the experimental platform is designed and built to test the SOC estimation algorithm based on EKF. The simulation and experimental results verify the accuracy of the estimation algorithm.

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

“…The model-based Kalman filter (KF) algorithm approximates system linearization and then optimally estimates system state based on input and output observations. [5,6,[12][13][14] Ge et al [15] used Taylor's formula to extend the state space equations of the nonlinear system by omitting the higher-order terms and proposed an extended KF (EKF) to solve the SOC estimation problem of the nonlinear battery system with an estimation error of no more than 2.5% based on the linear KF algorithm. However, the use of Taylor's formula to extend the nonlinear expressions in higherorder nonlinear battery models can lead to a large number of inaccuracies.…”