Abstract
In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.
Highlights
The state of charge (SOC) of the power battery of an electric vehicle is the basis of the energy management of the battery and the vehicle
This paper takes into account the real-time changes of the battery state and introduces the adaptive forgetting factor optimized by the simulated annealing algorithm to more accurately identify the real-time parameters of the battery
I is the operating current, and the charging direction is the direction of the state of charge of lithium-ion batteries, which provides a possible way for estimation rent; R1 is the polarization internal resistance; C1 is the polarization capacitance [17]; U1 is andpolarization correction, but open-circuit voltage cannot be directly measured in real-time
Summary
The state of charge (SOC) of the power battery of an electric vehicle is the basis of the energy management of the battery and the vehicle. The equivalent circuit model observation method is based on the circuit model, using charge and discharge data to first identify the model parameters and combine various filtering algorithms to estimate the battery SOC. Proposed a recursive least squares-extended Kalman filter (RLS-EKF) algorithm, which uses the recursive least squares method to achieve online parameter identification and combined with the extended Kalman filter to estimate the battery SOC. Based on the above analysis, this paper proposes real-time optimization of recursive least squares forgetting factor through particle swarm algorithm, combined with unscented. This paper takes into account the real-time changes of the battery state and introduces the adaptive forgetting factor optimized by the simulated annealing algorithm to more accurately identify the real-time parameters of the battery.
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