Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks.

Aiming at the problems of low prediction accuracy and poor generalization ability of lead-acid battery performance prediction model in substation, this paper proposes a lead-acid battery performance prediction model based on meta-learning and gating network. Firstly, based on the historical operating data of the battery, the long-term internal resistance change prediction of the battery is realized by introducing meta-learning and gating network. Then, based on the variation trend of battery internal resistance, the battery voltage, internal resistance, temperature and other key factors as well as the data of battery charge and discharge curve are used to build a lead-acid battery performance prediction model, and the prediction accuracy of the model is improved by combining CNN and attention mechanism. The experimental results show that the proposed model can effectively improve the accuracy and generalization ability of battery performance prediction.