Abstract
With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.
Highlights
Energy shortages and environmental pollution are becoming more and more prominent today.electric vehicles, with many advantages such as resource conservation and environmental friendliness, have attracted more and more attention
The equivalent circuit model of lithium-ion batteries is the crucial basis for most state of charge (SOC) estimation algorithms, such as extended Kalman filter (EKF) [3], adaptive extended Kalman filter (AEKF) [4], etc
Based on the second-order RC equivalent circuit model, it is applied to the adaptive forgetting factor recursive least square (AFFRLS) method to identify the equivalent circuit model parameters online
Summary
Energy shortages and environmental pollution are becoming more and more prominent today. The equivalent circuit model of lithium-ion batteries is the crucial basis for most SOC estimation algorithms, such as extended Kalman filter (EKF) [3], adaptive extended Kalman filter (AEKF) [4], etc. The n-order RC dynamic equivalent model can reflect the relationship between the internal parameters of the battery and the temperature or current. Since the forgetting factor is constant, the dynamic identification ability and accuracy of circuit parameters using FFRLS will be affected when the charging and discharging currents change frequently. Based on the second-order RC equivalent circuit model, it is applied to the adaptive forgetting factor recursive least square (AFFRLS) method to identify the equivalent circuit model parameters online. Experiments including the dynamic stress test (DST) are implemented to verify the real-time performance and accuracy of the AFFRLS algorithm
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