关键词: 自适应神经模糊推理系统, 模糊C均值聚类, 减法聚类, 剩余电量

Abstract: The prediction of surplus capacity of battery can be used to reasonably control the battery charge and discharge situation and extend the battery life as the core of intelligent battery management system. However, the complicated influence factors of surplus capacity cause the difficulty of predicting accurately. To solve this challenging problem, a prediction algorithm based on the improved fuzzy C-means clustering and Adaptive Network-based Fuzzy Inference System is proposed. The initial fuzzy inference system is built by subtractive clustering and weighted fuzzy C-means clustering, then the hybrid algorithm is used to train the parameters of fuzzy system to establish a nonlinear prediction model. The simulation results show that the improved clustering algorithm not only solves the shortcomings of traditional fuzzy C-means clustering but also accelerates the convergence rate and the battery surplus capacity prediction model has a high prediction accuracy.

Key words: adaptive network-based fuzzy inference system, fuzzy C-means clustering, subtractive clustering, surplus capacity 