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The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.

electronics

High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction