Designing a control system that can robustly detect faulted emission control devices under all environmental and driving conditions is a challenging task for OEMs. In order to gain confidence in the control strategy and the values of tunable parameters, the test vehicles need to be subjected to their limits during the development process. Complexity of modern powertrain systems along with the On-Board Diagnostic (OBD) monitors with multidimensional thresholds make it difficult to anticipate all the possible scenarios. Finding optimal solutions to these problems using traditional calibration processes can be time and resource intensive. A possible solution is to take a data driven calibration approach. In this method, a large amount of data is collected by collaboration of different groups working on the same powertrain. Later, the data is mined to find the optimum values of tunable parameters for the respective vehicle functions. This large scale data (terabytes) gives the engineers more samples to analyze and confidence in the calibration. A robust data analysis platform capable of handling large scale data and flexible enough to cater to different engineers’ needs is required to accomplish this task. In this paper, some salient features of such a platform developed by Ford Motor Company are presented. The memory efficiency, Model-In-Loop and parallel processing capability of this platform are highlighted. The use of MapReduce programming techniques to analyze large scale calibration data is also discussed.
