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

The foundation for battery modelling and state estimation is battery characterisation data. The finer the data, and the higher the model and state estimation accuracy, it is widely assumed that the higher the sampling frequency, the finer the data. In the literature, several distinct types of battery models have been proposed, which can be classed as white-box models, gray-box models, or black-box models. Scientific sampling frequency selection approach, on the other hand, is critical but rarely researched. The effect of sample frequency on the accuracy of battery model and state estimate is investigated in this research using four different sample frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, to describe the link between accuracy and sample frequency, a function is provided, which demonstrates an optimal selection principle. The model parameters are identified using an iterative identification approach, and the state-of-charge (SOC) is estimated using an extended Kalman filter approach. The relationship between sampling frequency, accuracy, and data quantity is clearly demonstrated by experimental findings under various operating settings, and the proposed selection approach has a high practical value and universality. The sample frequency vs. model accuracy tradeoff technique has a wide range of applicability and universality. The voltage data of a battery pack with cells connected in series and parallel at various sampling frequencies will be gathered in future work, and the influence of voltage mismatch on the required sampling frequency will be investigated.

Author(s) Details

Pingwei Gu
School of Control Science and Engineering, Shandong University, Shandong 250061, China.

Zhongkai Zhou
School of Control Science and Engineering, Shandong University, Shandong 250061, China.

Shaofei Qu
School of Control Science and Engineering, Shandong University, Shandong 250061, China.

Chenghui Zhang
School of Control Science and Engineering, Shandong University, Shandong 250061, China.

Bin Duan
School of Control Science and Engineering, Shandong University, Shandong 250061, China.

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