OVERVIEW OF TRENDS IN ONBOARD DIAGNOSTICS WITH RESPECT TO MULTICORE SYSTEMS COMPUTING PERFORMANCES

Transcription

1 OVERVIEW OF TRENDS IN ONBOARD DIAGNOSTICS WITH RESPECT TO MULTICORE SYSTEMS COMPUTING PERFORMANCES 12 May 2016, Stuttgart ANNUAL AUTOMOTIVE EMBEDDED MULTI-CORE SYSTEMS SUMMIT Ing. Attilio Brighenti S.A.T.E. President 1 / 28

2 Summary THE IMPORTANCE AND OPPORTUNITY OF MULTI-CORE SYSTEMS FOR AUTOMOTIVE APPLICATIONS MODEL-BASED ON-BOARD DIAGNOSTICS OPTIMISATION AND PORTABILITY OF SOLUTIONS FOR MULTI-CORE SYSTEMS 2 / 28

3 Vehicle Networks become complex There is an increasing number of ECUs There are many subnets within the vehicle $280 billion New protocols lead to more functions with higher speeds Report by Grand View Research, Inc. 3 / 28

4 More data are available Additional devices Vehicle network High precision GPS information (e.g. GALILEO constellation) 4 / 28

5 More computing power is needed Multi-core platforms in vehicles enable Advanced On-board Model-based diagnostics Features: 1) complex models of vehicle components or other subsystems (e.g. driver behaviour) 2) online or real-time processing to provide fast and reliable output 5 / 28

6 Summary THE IMPORTANCE AND OPPORTUNITY OF MULTI-CORE SYSTEMS FOR AUTOMOTIVE APPLICATIONS MODEL-BASED ON-BOARD DIAGNOSTICS OPTIMISATION AND PORTABILITY OF SOLUTIONS FOR MULTI-CORE SYSTEMS 6 / 28

7 On-board model-based diagnostics Model-based approach provides redundancy which cannot be achieved through hardware especially in the automotive context (cost + space) Dynamic equations + behavior prediction Driver behavior can be part of the diagnostic equation and predictive maintenance scheme 7 / 28

8 Model-based diagnostics (1) Oil pressure of a vehicle hydraulic system TBD considers only static or zone based limits MBD looks at this difference 8 / 28

9 Model-based diagnostics (2) Prognosis of engine failures by poor lubrication 0. 4 T r e n d E N D U R A N C E S. A. T. E. s. r. l. S y s t e m s & A d v a n c e d T e c h n o l o g i e s E n g i n e e ri n g 0. 2 C a r N u m b e r : M ( + 3 ) Normal Alarm Faulty (-3 ) Failure T e s t s e s s i o n km 9 / 28

10 PROs of model-based diagnostics Model-based approach is equivalent to using dynamic thresholds: Decrease in false alarms Decrease in missed alarms Increase in diagnostic quality (with a small code footprint) 10 / 28

11 On-board diagnostic solutions BCM Battery Charge Monitor FCM Fuel Consumption Monitor ATD Anomalous Turns Detector EDA EcoDriving Advisor DRIX DRiver behaviour IndeX(es) ENDURANCE Cooling & Lubricating Diagnostic Models SEW-AID Clutch, Gearshift and Alternator Diagnostic Models t s r G TTyre Plus Sensorless Tyre temperature and pressure estimation TDD Tyre Deflation Detector LAE - Lean Angle Estimator 11 / 28

12 On-board implementation 1) Dynamic model definition and implementation 2) Model setup in desktop development environment 3) Code generation for target hardware 4) Code integration and testing SCALABILITY AND PORTABILITY 12 / 28

13 1 Instant vel. calculation Long. slip. calculation VSLX [m/s] Ax calculation VSLX The wheel subsystems block are all identical The masked index parameter follows the convention : 1 = ANTDX 2 = ANTSX 3 = POSDX 4 = POSSX FOUR WHEEL MODEL WITH FORCED AIR CONVECTION Rev. 1-30/07/2008 Vslip X Vslip X Vslip X Vslip X wheel 1 Two degree of freedom Tyre Thermal Model 1 wheel 2 Two degree of freedom Tyre Thermal Model 2 wheel 3 Two degree of freedom Tyre Thermal Model 3 wheel 4 Two degree of freedom Tyre Thermal Model 4 T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] 1 TANTDX _m [ C] 5 TiANTDX _m [ C] Qwheel _1 Goto _1 2 TANTSX _m [ C] 6 TiANTSX _m [ C] Qwheel _2 Goto _2 3 TPOSDX _m [ C] 7 TiPOSDX _m [ C] Qwheel _3 Goto _3 4 TPOSSX _m [ C] 8 TiPOSSX _m [ C] Qwheel _4 Goto _4 Example: TTYRE Sensorless tyre tread temperature estimation Longitudinal slip Hysteresis (rolling resistence) Lateral slip Forced convection Tyre internal thermal capacity long. acceleration lateral acceleration wheels speed external temperature Natural convection Model Tyre tread thermal capacity estimated tyre tread temperature estimated tyre internal temperature 13 / 28

14 Example: TTYRE 75 TTyre - Test Results Order of model accuracy Model 03 - Opt # 19: DX Front Wheel Temperature C Simulated T Measured T Simulated Ti Temperature [ C] Time [s] Data: TT_test01_LAPS_TST04_00rp mat File: Opt_19_Mod03_FrontDX_T_00rp / 28

15 1 Instant vel. calculation Long. slip. calculation VSLX [m/s] Ax calculation VSLX The wheel subsystems block are all identical The masked index parameter follows the convention : 1 = ANTDX 2 = ANTSX 3 = POSDX 4 = POSSX FOUR WHEEL MODEL WITH FORCED AIR CONVECTION Rev. 1-30/07/2008 Vslip X Vslip X Vslip X Vslip X wheel 1 Two degree of freedom Tyre Thermal Model 1 wheel 2 Two degree of freedom Tyre Thermal Model 2 wheel 3 Two degree of freedom Tyre Thermal Model 3 wheel 4 Two degree of freedom Tyre Thermal Model 4 T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] T _m [ C] Ti [ C] Qw [W] 1 TANTDX _m [ C] 5 TiANTDX _m [ C] Qwheel _1 Goto _1 2 TANTSX _m [ C] 6 TiANTSX _m [ C] Qwheel _2 Goto _2 3 TPOSDX _m [ C] 7 TiPOSDX _m [ C] Qwheel _3 Goto _3 4 TPOSSX _m [ C] 8 TiPOSSX _m [ C] Qwheel _4 Goto _4 Example: TTYRE Portability Windows XP Simulink TTyre model Fujitsu MB91F460 ARM 9 Windows CE FreeScale MPC5121 Hitachi SH7058 Fujitsu MB91F / 28

16 Example: Tyre Deflation Detector Press. OK Press. OK Alteration of the statistical distribution Press. OK -0.2 bar 16 / 28

17 Example: CONDIZ 1. Real time model simulating automotive HVAC refrigeration cycles. 2. The model runs either: a) offline as a standalone model or b) in real time hardware in the loop (HIL) rapid prototyping systems c) as part of an advanced control system 5 connected subsystems with functional diversification 17 / 28

18 Summary THE IMPORTANCE AND OPPORTUNITY OF MULTI-CORE SYSTEMS FOR AUTOMOTIVE APPLICATIONS MODEL-BASED ON-BOARD DIAGNOSTICS OPTIMISATION AND PORTABILITY OF SOLUTIONS FOR MULTI-CORE SYSTEMS 18 / 28

19 Simulation and deployment on multi-core targets Problem statement: Models complexity vs Real-time requirements for HIL tests or embedded applications 19 / 28

20 Simulation and deployment on multi-core targets Problem solution: Models partitioning exploiting real-time execution on multi-core targets to distribute computational load and reduce execution times TASK 1 TASK 2 20 / 28

21 Simulation and deployment on multi-core targets Key issue: Implications of model partitioning in terms of data transfer latencies on solver accuracy TASK 2 TASK 1 Core 2 Core 1 21 / 28

22 Simulation and deployment on multi-core targets Partitioning optimisation: Desktop simulation before generated code on HIL or embedded platform to determine the optimum partitions of the model prior to code generation and execution on target hardware. 22 / 28

23 Simulation and deployment on multi-core targets Portability Different hardware configurations may be considered to deploy an application: multicore processors multiprocessors systems FPGAs, Architecture-related aspects to be taken into account for application manual deployment: Different number and types of processor nodes Communication and data transfer Standards of events, data protection and synchronisation USE AUTOMATIC PARTITIONING AND CODE GENERATION TOOLS 23 / 28

24 Simulation and deployment on multi-core targets Simulink mapping and profiling tool The user configures the model for on-target concurrency Separate tasks are assigned to different parts of the model Battery pack modelling, simulation, and deployment on a multicore real time target, Gazzarri et al., Mathworks, SAE int. J. Aerosp./ Vol 7, Issue 2, Dec / 28

25 Simulation and deployment on multi-core targets Simulink mapping and profiling tool: A scheduler assigns the tasks to the CPU cores 25 / 28

26 Simulation and deployment on multi-core targets Simulink mapping and profiling tool: Assess the performance of the partitioned model by simulating the effects of model partitioning in terms of load balance and evaluating several configurations 1 TASK 2 TASKS 4 TASKS 26 / 28

27 Conclusions The complexity of vehicle networks offers new opportunities The availability of multi-core systems should be exploited to bring forward advanced on-board applications On-board diagnostics enhances vehicle reliability, safety and pollution Model-based diagnostics enhances the widely used thresholdbased diagnostics Advanced tools are available for optimising models partitioning prior to code generation and execution on target hardware Advanced tools for automatic code generation are available which allow generating suitable code for model deployment into multicore platforms Exploit automatic optimisation tools and code generation tools for multicore targets instead of manual programming the application for concurrent execution enhances solutions portability 27 / 28

28 THANK YOU! QUESTIONS? 28 / 28