摘要
针对电动汽车动力电池荷电状态(SOC)的估计问题,对动力电池的荷电状态估计方法进行了研究。对电池荷电状态的影响因素进行了归纳,提出了基于反向传播神经网络(BP神经网络)的动力电池荷电状态估计方法。利用汽车仿真软件ADVISOR对电动汽车行驶典型的汽车测试工况进行了模拟,得到了电动汽车动力电池荷电状态与电池的充放电电流、温度之间的关系。对得到的训练样本数据进行了归一化处理,经过训练,得到基于BP神经网络的动力电池荷电状态估计模型。同样,利用ADVISOR软件得到的测试数据,对得到的神经网络模型进行了测试。研究结果表明,该模型的估计值和输出值之间的误差最大值为4%左右,模型的精度符合动力电池荷电状态估计的使用要求。
Aiming at estimating state-of-charge of electric vehicle batteries precisely,study on modeling state-of-charge（ SOC） was investigated. After analyzing effect factors of SOC,a model of SOC on BP neural network was established. Electric vehicle simulation software ADVISOR was used to simulate an electric vehicle on typical driving cycles. The relationship between current,temperature and SOC was derived through simulation. After normalization of the training data and training the neural network,a SOC estimation model based on BP neural network was derived. The model was tested by testing data obtained by ADVISOR. The results indicate that the maximum error between the model estimation and actual values is 4%,which can satisfy the requirements of actual use of SOC.
出处
《机电工程》
CAS
2015年第1期128-132,共5页
Journal of Mechanical & Electrical Engineering
关键词
电动汽车
电池荷电状态
神经网络
electric vehicle
state-of-charge（SOC）
neural network
