摘要
为满足汽车进一步智能化的需要,同时支持时间触发与事件触发服务兼具设计与调度灵活性成为汽车总线发展的方向,针对当前现有车上控制器局域网络(Controller area network,CAN)总线不能有效处理时间触发的周期性信息,时间触发的控制器局域网络(Time-triggered CAN,TTCAN)协议具有时间触发功能但缺乏信息调度的灵活性等问题,分析基于动态规划的柔性时间触发控制器局域网络(Flexible time-triggered CAN,FTTCAN),研究FTTCAN双相基本周期结构。分别推导出双相内基于动态规划调度的信息传输时间特性参数分析方法,并基于FTTCAN原理具体设计出纯电动汽车FTTCAN总线控制系统。采用动态规划方法设计出信息调度策略,对信息传输特性进行分析及与CAN方案对比。最后在CAN总线开发系统上进行FTTCAN协议运行试验。对比与试验结果验证了采用FTTCAN的方案兼具时间触发与事件触发服务功能,且具有较好灵活性特点,是汽车总线系统优化设计的好方法。
To meet the requirement of automotive intelligentizing, the next step in the direction of automotive bus is to support both time-and event-triggered communication services and to attain more flexible of design and scheduling. But controller area network (CAN) protocol is inefficient for time-triggered messages, while time-triggered controller area network (TTCAN) can efficiently fulfill joint support for both event- and time-triggered traffic but lacks flexibility in message scheduling. To these questions, a new dynamic planning-based protocol that is flexible in time-triggered controller area network (FTTCAN) is analyzed. And the elemen- tary cycle comprising of two phases and real-time analysis method for communications of messages in the two phases axe explained. Then an FTTCAN-bus system of electric vehicle based on FTTCAN protocol is designed, and the scheduling strategy of signals based on dynamic planning is adopted. And performance analysis of the new system is carried out by comparing with CAN system. Finally, operation test of FTTCAN protocol is carried out on CAN bus development system. The comparison and test results verify that FTTCAN is a good way to design automotive bus system, which can meet the requirements of supporting both time-and event-triggered communication services and being flexible in design and scheduling.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2008年第5期142-146,共5页
Journal of Mechanical Engineering
基金
国家自然科学基金(50535010)
沈阳市科技基金(63287-1-00)资助项目。
关键词
动态规划
柔性时间触发控制器局域网络
电动汽车
实时分析
Dynamic planning Flexible time triggered controller area network(FTTCAN) Electric vehicle Real time analysis