Abstract
An accurate estimation on the battery state of charge (SOC) could serve as a foundation for the secure and stable operation of battery management systems. The rapid development of data science and artificial intelligence provides a new solution for battery SOC estimation. However, existing methods that directly utilize measured data to establish the SOC estimation will suffer from low prediction accuracy due to the insufficient incorporation of mechanism information. The equivalent circuit model is a reliable battery mechanism model that could be adopted to calculate the open-circuit voltage, which has been proved to be directly correlated with the battery SOC. The open-circuit voltage has rarely been applied to the SOC estimation because it is hard to be measured online. Therefore, the physical information provided by the equivalent circuit model can be combined with data-driven prediction model to obtain the real-time approximation of the open-circuit voltage, with which more useful features can be extracted to improve prediction accuracy. For this purpose, a mechanism-aid deep learning method is proposed, in which the loss function of the LSTM neural network is modified by the equivalent circuit model. And the neural network model could converge to the mechanism relationship of the open circuit voltage in the training stage and obtain accurate estimation on the SOC. The proposed method is applied to the Panasonic battery dataset, which is collected by conducting the tests at various real-world driving profiles. Compared with related methods that only consider voltage, current and temperature, the root mean square error and mean absolute error of the SOC estimation decreases 72.49 % and 72.23 %, respectively. Then the effectiveness of the proposed method is further verified under different test conditions, demonstrating the significance to introduce mechanism information in battery SOC estimation.
