SOC estimation of lithium battery based on fractional order model

Abstract

For the purpose of the present study of lithium battery SOC estimation, fractional-order calculus theory and the fact that the real capacitance is fractional-order in nature mean that integer-order modeling yields incorrect methods. To improve the accuracy of lithium battery state-of-charge (SOC) estimation, a fractional-order traceless Kalman filter technique is proposed with a second-order RC fractional-order model, and a least-squares approach with a variable forgetting factor is utilized to determine battery parameters. The system gives real-time updates to the battery condition and settings through recursive estimation of state and parameter variables. Simulation analysis is performed using experimental data and UDDS operating parameters. The traceless Kalman filter method's simulated values are compared to the simulation outcomes. These results show that the method beats the integer-order traceless Kalman algorithm and that the maximum estimation error of battery SOC can be maintained below 2%. This proves that the proposed approach works as intended.