SOC estimation method of power battery based on BP artificial neural network |
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| Authors: | SU Zhenhao LI Xiaojie QIN Jin DU Wenjie HAN Ning |
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| Affiliation: | 1 North University of China, School of Energy and Power Engineering, Taiyuan 030051, Shanxi, China;
2 North University of China, School of Mechanical Engineering, Taiyuan 030051, Shanxi, China |
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| Abstract: | The state of charge (SOC) is one of the key technologies in battery management system. Among many estimation methods, neural network has obvious advantages in accuracy and robustness of estimation. The huge amount of data is an important factor to obtain the accurate value of SOC. To solve the above problems, a method of estimating SOC of power batteries based on BP artificial neural network is proposed. Taking a certain type of batteries as the experimental object, a large amount of data is obtained by collecting the data of voltage, current, internal resistance and temperature of batteries. Equivalent circuit model of battery was established, and the initial data were corrected considering the effects of battery polarization, charge-discharge ratio and temperature. BP artificial neural network model is established based on MATLAB platform, and the data are modified to train the network model, and the feasibility of the model is verified. The model is applied to the prediction of experimental data, and the estimation of SOC is realized by function fitting. Finally, by comparing the predicted and measured values of SOC, the validity of the artificial neural network model for estimating SOC is proved. |
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| Keywords: | power battery equivalent circuit data correction neural network model SOC estimation |
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