The accuracy of the power battery model and SOC estimation directly affects the vehicle energy management control strategy and the performance of the electric vehicle, which is of great significance to the efficient management of the battery and the improvement of the reliability of the vehicle. Based on the research of domestic and foreign battery models and the previous results of SOC estimation, this paper classifies power battery models into electrochemical mechanism models, equivalent circuit models and data-driven models. This paper analyzes the advantages and disadvantages of various battery models and current research progress. According to the choice of battery model, the previous research results of the power battery SOC estimation method are divided into three categories: the direct measurement method not based on battery model, the estimation method using black box battery model, and the battery model SOC estimation method based on state space. This paper will summarize and analyze the principles, applicable scenarios and research progress of the three categories of estimation algorithms aiming to provide references for future in-depth research. Finally, in view of the shortcomings of the battery model and estimation algorithm of the existing method, the future improvement direction is proposed.
The electric vehicle has become an important development direction of the automobile industry, and the lithium-ion power battery is the main energy source of electric vehicles. The accuracy of state of charge (SOC) estimation directly affects the performance of the vehicle. In this paper, the first order fractional equivalent circuit model of a lithium iron phosphate battery was established. Battery capacity tests with different charging and discharging rates and open circuit voltage tests were carried out under different ambient temperatures. The conversion coefficient of charging and discharging capacity and the simplified open circuit voltage model considering the hysteresis characteristics of the battery were proposed. The parameters of the first order fractional equivalent circuit model were identified by using a particle swarm optimization algorithm with dynamic inertia weight. Finally, the recursive formula of a fractional extended Kalman filter was derived, and the battery SOC was estimated under continuous Dynamic Stress Test (DST) conditions. The results show that the estimation method has high accuracy and strong robustness.
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.
In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, a dual Kalman filter method based on the online identification of model parameters is proposed to estimate the state of charge in lithium-ion batteries. Here, we build the first-order equivalent circuit model of lithium-ion batteries and derive its online identification model based on extended Kalman (EKF). Considering that the noise value in the EKF algorithm is difficult to select through experiments to achieve the best filtering effect, this paper combines an improved particle swarm optimization algorithm (IPSO) with EKF to realize online model parameter identification. At the same time, the EKF filtering method derived from the state space equation is also used in SOC estimation. It constitutes a dual Kalman filter method for online identification of model parameters and SOC estimation. The experimental and simulation results show that the IPSO–EKF algorithm can adaptively adjust the noise value according to the complex operating conditions of electric vehicles. Compared with the EKF algorithm, our algorithm can identify battery model parameters more accurately. The dual Kalman filter method composed of the IPSO–EKF algorithm and EKF applied to SOC estimation achieved a higher accuracy in the final algorithm verification.
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