为解决纯电动车控制策略开发周期长、可靠性及稳定性差等问题,基于D2P(from Development to Production)开发平台,在Matlab/Simulink模型化开发环境下,设计了纯电动车核心控制策略。利用MotoHawk模块库建立产品级整车控制器(Vehicle Con...为解决纯电动车控制策略开发周期长、可靠性及稳定性差等问题,基于D2P(from Development to Production)开发平台,在Matlab/Simulink模型化开发环境下,设计了纯电动车核心控制策略。利用MotoHawk模块库建立产品级整车控制器(Vehicle Control Unit,VCU)与控制策略的联系,基于SAE_J1939标准设计了整车控制器局域网络(Controller Area Network,CAN),并通过编写Matlab脚本文件实现了其自动化的模型在环(Model in-the-loop,MIL)测试功能。采用LabVIEW软件构建了VCU控制策略测试平台,并进行了实车测试。结果表明:所搭建控制策略能够迅速响应驾驶员操作意图,有效保障整车的安全性、稳定性和可靠性;基于D2P开发平台的纯电动车整车控制策略研究有利于提高开发效率,缩短开发周期,节省研发成本。展开更多
To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,...To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.展开更多
针对新能源汽车控制器开发周期长、无法快速进行小批量生产等问题,该文应用基于模型的快速开发方法进行永磁同步电机控制算法开发。文中先分析了永磁同步电机的数学模型,在此基础上设计了速度环的ADRC算法;然后在D2P(development to pro...针对新能源汽车控制器开发周期长、无法快速进行小批量生产等问题,该文应用基于模型的快速开发方法进行永磁同步电机控制算法开发。文中先分析了永磁同步电机的数学模型,在此基础上设计了速度环的ADRC算法;然后在D2P(development to production)平台下,搭建车用永磁同步电机矢量控制代码模型,以及代码自动生成和仿真验证;最后进行台架试验,验证控制算法,在线整定参数,实现控制算法的快速开发。实验结果表明,采用基于模型的快速开发方法能够缩短开发周期和成本,并且自动生成的代码具有较高的可靠性,能满足新能源汽车各项性能要求。展开更多
文摘为解决纯电动车控制策略开发周期长、可靠性及稳定性差等问题,基于D2P(from Development to Production)开发平台,在Matlab/Simulink模型化开发环境下,设计了纯电动车核心控制策略。利用MotoHawk模块库建立产品级整车控制器(Vehicle Control Unit,VCU)与控制策略的联系,基于SAE_J1939标准设计了整车控制器局域网络(Controller Area Network,CAN),并通过编写Matlab脚本文件实现了其自动化的模型在环(Model in-the-loop,MIL)测试功能。采用LabVIEW软件构建了VCU控制策略测试平台,并进行了实车测试。结果表明:所搭建控制策略能够迅速响应驾驶员操作意图,有效保障整车的安全性、稳定性和可靠性;基于D2P开发平台的纯电动车整车控制策略研究有利于提高开发效率,缩短开发周期,节省研发成本。
基金supported by National Natural Science Foundation of China(22478239)Science and Technology Commission of Shanghai Municipality(19DZ2271100)National Natural Science Foundation of China(22208208)。
文摘To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.
文摘针对新能源汽车控制器开发周期长、无法快速进行小批量生产等问题,该文应用基于模型的快速开发方法进行永磁同步电机控制算法开发。文中先分析了永磁同步电机的数学模型,在此基础上设计了速度环的ADRC算法;然后在D2P(development to production)平台下,搭建车用永磁同步电机矢量控制代码模型,以及代码自动生成和仿真验证;最后进行台架试验,验证控制算法,在线整定参数,实现控制算法的快速开发。实验结果表明,采用基于模型的快速开发方法能够缩短开发周期和成本,并且自动生成的代码具有较高的可靠性,能满足新能源汽车各项性能要求。
