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Performance Analysis of FlexRay-based ECU Networks. Andrei Hagiescu, Unmesh D. Bordoloi, Samarjit Chakraborty Department of Computer Science, National University of Singapore Prahladavaradan Sampath, P. Vignesh V. Ganesan, S. Ramesh General Motors R&D – India Science Laboratory, Bangalore.
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Performance Analysis of FlexRay-based ECU Networks Andrei Hagiescu, Unmesh D. Bordoloi, Samarjit Chakraborty Department of Computer Science, National University of Singapore Prahladavaradan Sampath, P. Vignesh V. Ganesan, S. Ramesh General Motors R&D – India Science Laboratory, Bangalore
Performance Analysis of FlexRay-based ECU Networks • In a high-end car there are upto 70 ECUs exchanging upto 2500 signals. • Commonly used protocols include CAN, LIN, J1850 etc. • These can be broadly divided into event triggered and time triggered protocols – each has certain advantages anddis-advantages. • Lot of emphasis on hybrid protocols – FlexRay. • FlexRay is also backed by major automotive companies and hence, there has been a lot of interest in performance analysis of FlexRay-based designs.
ECU ECU ECU FlexRay-based ECU Networks • Tasks have different activation rates and execution demands • Each computation/communication element has a different scheduling/arbitration policy EDF Fixed Priority Round Robin Output Events • Timing Properties? • End-to-end delay? • Buffer requirements? Input Events Comm. Controller FlexRay Bus
Processor Task Input Data/Events Concrete Instance Abstract Representation Service Model Event Model Processing Model Abstract Models for Performance Analysis
t number of events in t=[0 .. 2.5] ms maximum/minimum number of events in any interval of length 2.5 ms Event Model – Modeling Execution Requirements l() <= R(t+) – R(t) <= u() Arrival Pattern events slide windowand record max and min 2.5 t [ms] Arrival Curves [l, u] u events l [ms] 2.5
t available service in t=[0 .. 2.5] ms maximum/minimum available service in any interval of length 2.5 ms Service Model – Modeling Resource Availability l() <= S(t+) – S(t) <= u() Resource Availability availability 2.5 t [ms] u Service Curves [l, u] service l [ms] 2.5
Service Model Event Model Processing Model Processing Model processed events remaining supply
Compositional Analysis Compositional Schedulability/Timing Analysis Modeling dependency PE1 PE2
Actuators Sensor ECU3 ECU2 Fixed Priority Fixed Priority ECU4 Radar 1 Radar 2 Throttle and Brake Torque Arbitration Brake Control Anti-lock Braking System Throttle Control Wheel Sensor Path Estimator Data Fusion Object Selection Adaptive Cruise Control ECU1 TDMA Object Detection Object Detection Case Study – An ACC Application Task dependencies DYN message ST message 3 Tf Bf 1 2 m5 m1 m3 m4 m6 m2 m7 (to crash control subsystem) ’ FlexRay Bus An adaptive cruise control application
Performance Analysis • Minimum end-to-end delay when DYN segment length = 9ms and ST segment length = 5 ms • Variations in the end-to-end delay with different sampling periods(ST= 8 ms and DYN = 6 ms) 125 150 124 145 123 140 bus cycle length = 14 ms 122 135 121 Delay (ms) Delay (ms) 120 130 119 125 118 120 117 9 45 25 30 50 116 35 7 55 Wheel Sensor Period (ms) Radar Period (ms) 40 115 60 DYN Length (ms) 9 5 8 7 6 5 ST Length (ms)
