An improved AUKF algorithm is proposed based on the adaptive traceless Kalman filter (AUKF), and the main factors that affect SOC estimation are parametrically corrected in the algorithm. The goal of this research is to be able to accurately estimate the battery state of charge (SOC) of lithium batteries. In order to do this, the main factors that affect the accuracy of battery SOC estimation as well as the advantages and disadvantages of traditional battery SOC estimation are considered. The algorithm is derived from the second-order RC equivalent circuit model of the battery. To obtain the improved AUKF algorithm, the output deviation covariance of each measurement is taken as the noise covariance. This allows the noise covariance to be updated with time and eliminates the issue that the noise covariance is a constant source of error. The results of the experiments demonstrate that an upgraded version of the AUKF algorithm may provide an accurate estimation of the battery SOC value.
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.
KEYWORDS: System on a chip, Error analysis, Circuit switching, Filtering (signal processing), Simulink, Computer simulations, Complex systems, Temperature metrology, Signal processing, Resistance
The state of charge of Li-ion battery in new energy vehicles is an important parameter reflecting the battery and power system. In order to achieve the purpose of real-time SOC estimation, this paper takes the lithium iron phosphate battery as the research object, establishes the RC second-order circuit model in MATLAB/Simulink environment, then discovers the parameters of different SOC and temperature effects on the battery model by establishing the mixed power pulse characteristics experiment, and brings the discerned parameters into the traditional single extended Kalman filter algorithm (EKF) and the parameters identified are brought into the conventional single extended Kalman filter (EKF) and traceless Kalman filter (UKF) algorithms for simulation experiments, and then the different experimental results of the two algorithms are compared and studied. The final experiments show that the traceless Kalman filter algorithm can significantly improve the estimation accuracy and convergence speed of the Li-ion battery charge state.
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