针对传统电控空气悬架电子控制单元(Electronic Control Unit,ECU)下线检测过程中存在的人工检测精准度差、效率低等问题,开发了一款自动电控空气悬架ECU下线检测系统。分析了电控空气悬架ECU下线检测系统各项技术需求,设计了上位机+系...针对传统电控空气悬架电子控制单元(Electronic Control Unit,ECU)下线检测过程中存在的人工检测精准度差、效率低等问题,开发了一款自动电控空气悬架ECU下线检测系统。分析了电控空气悬架ECU下线检测系统各项技术需求,设计了上位机+系统检测平台的系统架构;选择数据采集卡、CAN卡等部件搭建了下线检测系统检测平台;采用C#编程语言开发了上位机软件,软件采用UI界面层、业务逻辑层和数据层的三层架构,通过CAN总线通讯方式实现上位机、检测平台及待测ECU的双向通讯。测试结果表明,该下线检测系统可实现电控空气悬架ECU自动下线质量检测,并完成了测试数据的智能管理,满足电控空气悬架ECU的检测功能需求。展开更多
In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And th...In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.展开更多
文摘针对传统电控空气悬架电子控制单元(Electronic Control Unit,ECU)下线检测过程中存在的人工检测精准度差、效率低等问题,开发了一款自动电控空气悬架ECU下线检测系统。分析了电控空气悬架ECU下线检测系统各项技术需求,设计了上位机+系统检测平台的系统架构;选择数据采集卡、CAN卡等部件搭建了下线检测系统检测平台;采用C#编程语言开发了上位机软件,软件采用UI界面层、业务逻辑层和数据层的三层架构,通过CAN总线通讯方式实现上位机、检测平台及待测ECU的双向通讯。测试结果表明,该下线检测系统可实现电控空气悬架ECU自动下线质量检测,并完成了测试数据的智能管理,满足电控空气悬架ECU的检测功能需求。
文摘In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.