首页> 外文会议>Chinese Automation Congress >The SOC estimation and simulation of power battery based on self-recurrent wavelet neural network
【24h】

The SOC estimation and simulation of power battery based on self-recurrent wavelet neural network

机译:基于自递归小波神经网络的动力电池SOC估计与仿真

获取原文

摘要

Improving the accuracy of power battery state of charge (SOC) estimation is an important basis for formulating and optimizing the control strategy of hybrid electric vehicle. Aiming at the problem of the traditional SOC large estimation error, a SOC estimation method based on self-recurrent wavelet neural network (SRWNN) is proposed. Firstly, the equivalent model of power battery and its SOC estimation model are established. Then, The SOC estimation algorithm based on SRWNN is designed in detail. At last, The SOC estimation method based on SRWNN is compared with other SOC estimation methods from SOC estimation error and vehicle performances in the Matlab simulation environment. The simulation results show that its SOC estimation accuracy is highest and vehicle performances by using SRWNN is best.
机译:提高动力电池充电状态(SOC)估计的准确性是制定和优化混合动力汽车控制策略的重要基础。针对传统SOC大估计误差的问题,提出了一种基于自回归小波神经网络(SRWNN)的SOC估计方法。首先,建立了动力电池的等效模型及其SOC估计模型。然后,详细设计了基于SRWNN的SOC估计算法。最后,基于Matlab仿真环境中的SOC估计误差和车辆性能,将基于SRWNN的SOC估计方法与其他SOC估计方法进行了比较。仿真结果表明,采用SRWNN的SOC估计精度最高,车辆性能最佳。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号