Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs.

中文翻译：

---

一种新颖的鸽子启发式优化RBF模型用于并联电池分支预测

电池储能是可再生能源系统改革的关键项目，也是能源流的有效监管者。并联电池组可以有效地增加电池模块的容量。然而，由并联电池支路电流的不确定性引起的功率损耗对电动汽车的经济性和安全性提出了严峻挑战。需要电池支路电流预测的准确性来改善并联连接。本文提出了一种基于鸽子启发式优化方法的径向基函数神经网络模型，并将该算法成功地应用于预测电池组的并联支路电流。