基于软测量技术的蓄电池容量估计方法研究

Research of Estimation Method of State-of-Charge for Battery Based on Soft Measurement

蓄电池剩余容量SOC(state of charge)是蓄电池能量管理系统以及设计快速充电器的一个重要的参数;文章采用仿射传播聚类算法对采集的铅酸蓄电池数据进行预处理,该方法可以降低输入数据维数,并且也可以去除数据之间的相关性。运用自适应模糊神经网络对蓄电池剩余容量进行估计,自适应模糊神经网络的输入变量为蓄电池的电压、电流和电压变化率;经过仿真实验表明:自适应模糊神经网络能够很好的解决非线性问题,模型的泛化能力强,具有较高精度的蓄电池剩余容量估计。

Battery residual capacity （SOC） is a very important parameter for battery energy management system and the design of quick charger. The paper used affine propagation clustering algorithm for acquisition of the lead--acid battery data pretreatment, this method can reduce the dimension of input data, and also can remove the correlation of data, using adaptive fuzzy neural network to estimate battery of re- sidual capacity. After the simulation experiments show that： Adaptive fuzzy neural network can be a very good solution to solve nonlinear problem and the model have generalization ability and the method of battery residual capacity estimation has a high precision.