Pre-‐Production Powertrain Optimization Using Telematics Vinicio Franco, Control-‐Tec Brasil
ABSTRACT
Powertrain Systems development is entering a period of unprecedented challenge driven by the convergence of many factors: emissions, fuel economy, increased competition, reduced workforce, tighter budgets and timing to deliver. The application of telematics and information technology to engineering development can provide the efficiency gains required for engineers to deliver a robust powertrain system in a timely manner. Engineers can focus their time on problem areas during their normal development process and deliver a quality product. The information in this document shows some examples on how this methodology has been applied by Ford Brasil in some of their programs using a VDR (Vehicle Data Recorder) and software developed by Control-‐tec.
INTRODUCTION Powertrain systems development has become increasingly difficult in recent years due to tighter emissions standards, ODB diagnostics requirements, increase competition, tighter timing to deliver. The development process must change in order to effectively deliver a robust powertrain calibration. The application of affordable innovations in telematics and information technology to enhance the development process in one potential method to enable engineers to launch quality products and afford fleet managers the ability to stay connected with their global fleet. Ford Brasil has been using a complete automated system with a Vehicle Data Recorder (VDR) installed in some of their vehicles to diagnostic and prognostic data for numerous subsystems throughout the entire vehicle with automated data processing, analysis and reporting. In recent test program more than 11000 drive cycles (Trips) containing thousands of records (All DTC, freeze framas, soft-‐fails & events.) of data and automatically assessed the performance of several subsystems like (Start Performance, Idle quality, Trans shift quality, OBD Monitor results recorded, Fuel Learning, Misfire Profile learning ) as well as providing the necessary data for all subsystem failures that occurred during the testing. This document show several examples how a system is applied to simultaneously support both diagnostic and prognostic engineering methods. The system provides traditional reactive methods assisted by telematics to reliably provide useful data and expedite root cause analysis. The reactive process begins by acquiring data with a VDR when a pre-‐defined event is detected (Soft Fail Events). This data is then automatically downloaded to a back office server where it is processed and Engineering is automatically notified. The
application of statistical process control theory to OBD is used to assess the robustness of individual diagnostics like monitor completion with a set threshold and subsystems like starts, shift events, Idle quality, OBD monitor results, DTC monitoring. Every subsystem metric is continuously evaluated and recorded by the VDR. This data is then analyzed and the robustness assessed by comparing the diagnostic threshold to the mean and standard deviation of the test results. The Diagnostics and prognostic data are available via web-‐based application featuring management data analysis tools, automated issue management and reporting.
Data Collection Types •
Trip Data
o A file of predefined set of information that is collected at 1 Hz while the vehicle is running (all continuous data if feasible). o Engine Duty Cycle parameter (engine speed, torque, % load, temperatures, pedal, etc). o Vehicle Duty Cycle parameters (vehicle speed, GPS, etc)
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Snapshot Record
o An Array of predefined information that is designed to assist system optimization. o Triggered by the occurrence of a predefined activity irrespective of result. o ie: engine Start Regen Complete.
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High Speed Event Data log
o A file of predefined information that is collected at 20 Hz and is designed to assist system optimization. o Triggered by the exceedance of a predefined threshold. o Ie: Engine Start Flare > 2000 RPM, DTC, pending code, etc. o Engine Duty Cycle parameter (engine speed, torque, % load, temperatures, pedal, etc). o Vehicle Duty Cycle parameters (vehicle speed, GPS, etc)
Hardware VDR (Vehicle Data Recorder)
Data Acquisition Detail
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State Flow Design Start Mode / Run Mode Shutdown / Charging Mode Data on Demand Synchronous Mixed Mode Data CAN PIDs DMRs Analog Thermocouples GPS Multiple Rasters High speed: 20 Hz Periodic – every 5m, 10m, etc. Low speed: 1 Hz, 0.4 Hz, 0.2 Hz Once per trip Software Synthetic variables and custom logic Multi-‐config capability with auto update and generic backup
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Embedded Linux OS runs Control-Tec proprietary software Supports ISO 15765, KWP2000, GMLAN, J1939, ISO14229, J1850 PWM & VPW, and J1708/J1587 Utilizes SAE J2534 API Standard 16 analog channels, +/1 5V @ 12 bits GPS & (2) USB ports WiFi or Cellular Logger interface
New Validation Process
Light Duty companies typically test vehicles during development and consider no identified failures a “pass” but typically know little about the performance of the powertrain system. Engineering feedback is often limited to a contact with a fleet supervisor when the system fails, Figure 5. However, no information is
known other than the fact that a failure occurred. The engineer is now forced to try to reproduce the issue in order to collect the data necessary to solve the problem and has no idea if this issue was a ‘one off’ or is indicative of a potentially major problem.
Figure 5. Reactive Validation Approach
Instead, the validation process can take a proactive approach by measuring the parameters that characterize subsystem performance every time the system is exercised, Figure 6. The robustness of the subsystem can now be understood and engineers can more readily solve the issue. Sensitivity to the noise space can be analyzed as well by studying the performance of the subsystem under the different operating conditions recorded along with the subsystem metrics.
Figure 6. Proactive Validation Approach
The powertrain system conducts several hundred ‘transactions’ during a typical drive cycle that can be measured to assess the robustness of the system. For example, one engine start produces a handful of metrics that can characterize the robustness of that start such as: the time taken for the engine to start after the ignition was commanded to start, the peak engine speed observed during the start, the time it took to reach the desired engine speed, and more, Figure 7. Also, these measures are usually a function of the operating conditions during the start, which can also be measured and recorded: the engine coolant temperature, the ambient air temperature, barometric pressure, etc. All major
powertrain subsystems can be measured to assess engine start performance, idle quality, transmission shift quality, diagnostic robustness, and more. Advances in vehicle data recorders (VDR), wireless data transfer and data storage now make it possible to measure every powertrain system transaction in order to assess the robustness of the system.
Figure 7. Engine Start Subsystem
Case Studies Control-‐Tec implemented an automated robustness system utilizing a VDR that automatically downloaded vehicle data from an existing test fleet of vehicles to a server where the data was then processed and issues were identified. This enabled Ford Engineering to focus on these problem areas and correct the issues before launch. Three examples are presented here. In these examples, an automated statistical assessment utilizing the statistical process control metric Cpk was used to identify potential issues. Cpk is defined as: Cpk = |Monitor Threshold -‐ µ | / (3 σ ) where µ is the mean of the data and σ is the standard deviation. This result estimates process capability for a one-‐sided diagnostic and assumes an approximately normal distribution. Existing processes are recommended to have Cpk values greater than 1.25 to be robust and new processes greater than 1.50. Cpk in relation to process fallout is indicated in Table 1. For diagnostic systems, the Failure Rate needs to be analyzed based on the number of times the diagnostic is exercised. Vehicles in Brasil for Ford have a warranty for 5 years or 100000 km. Assuming an average trip distance of 50 km, a vehicle will have 2,000 drive cycles during its useful life. The new OBD diagnostics mandated to make a decision on at least 33% of the drive cycles. Therefore, each diagnostic in a compliant vehicle will produce a minimum of 666 test results during its useful life. Warranty can now be forecasted based on Cpk with assumptions for vehicle volume and repair cost. The warranty in Table 1 assumes a vehicle volume of 50,000 units and a repair cost of R$150. This warranty compounds for non-‐ robust diagnostics that are not fixed properly on the first visit resulting in repeat repairs and dissatisfied customers.
Cpk 0.33 0.67 1.33 2.00
# σ From Threshold
Process Yield
Failure Rate (R/666)
1 2 4 6
68.27% 95.45% 99.99% 99.9999998%
211.32 30.3 0.0666 0.000001332
Warranty (R$) R$1,584,900 R$227,250 R$499 R$0.0099
Table 1. Cpk in Relation to Failure Rate and Warranty
Example for OBD CAT Monitor Completion
In the following example we can see the statistical analysis for a sample of more than 1000 observations for the final result for CAT Monitor.
Example for adaptive learning
In the following example we can see the statistical analysis for a sample of more than 10000 observations for each vehicle program. This was only for a period of 3 months.
Conclusion
A reliable method for providing useful data to Engineering in a timely manner can be accomplished by combining VDR’s with telematics and information technology. By automating the data acquisition, transfer, processing, and monitoring the VDR for performance, Engineering will be immediately notified of any issue and be able to begin to solve problems with useful vehicle data.
Acknowledgments
Bill Leisenring CONTRLOTEC Ford Motor Brasil
References 1. Zhang, Y., Gantt, G. W., Rychlinski, M. J., Edwards, R. M., Correia, J. J., Wolf, C. E., Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice, IEEE Transactions on Reliability 58 (2), pp. 286294, June, 2009. 2. M. McCarthy, Update on Light Duty OBDII, SAE OBD TOPTEC, September, 2005, Pasadena, CA USA. 3. Society of Automotive Engineers specification J1939 4. Environmental Protection Agency Emissions Regulations, www.epa.gov 5. California Air Resources Board Emissions and Diagnostic Regulations, www.arb.ca.gov 6. SEC data analysis provided by Warranty Week, www.warrantyweek.com Contact Vinicio Franco vfranco@control-‐tec.com www.control-‐tec.com 19-‐ 98128 0934
Definitions/Abbreviations VDR Cpk DAQ OBD DTC
Vehicle Datat Recorder Process capability index Digital Acquisition On Board Diagnostics Diagnostic Trouble Codes