Accuracy of Model and State-of-Charge Estimation for LiNCM Battery: An Approach towards Influence Analysis and Optimization of Sampling Frequency

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. Several different kinds of battery models have been proposed in literature, which can be generally classified into white-box models, gray-box models, and black-box models. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality. The tradeoff strategy between the sampling frequency and the model accuracy has high applicability and universality. In future works, the voltage data of battery pack with cells connected in series and parallel at different sampling frequencies will be obtained, and the effect of voltage mismatch on the necessary sampling frequency will be studied.