Engine ECU subsume the numerous control functions of electrical systems in the vehicle based on various sensors inputs and the control parameters present inside ECU such as maps, multiplication factors, constants and so on. These control parameters need to be calibrated effectively for better performance as well as to meet stringent emission norms. Since, the most efficient way of calibration is through CAN by means of XCP/CCP protocol, this process involves logging and processing of the ECU data to estimate the appropriate values followed by downloading the modified values to the ECU manually. Even though from theoretical calculations it is possible to estimate the approximate parameter values, these values need to be validated in engine test beds or on road and are fine tuned to attain the optimum results by repeating the same trial under same test conditions numerous times. After each trial, the data is analyzed and new set of data is determined which is downloaded to ECU before the next trial. This process is carried out until optimum results are achieved which is time consuming. In this paper, a new approach has been explained which will eliminate the human interference during the trials and speeds up the process of establishing the master slave communication between PC and ECU through any CAN transceiver hardware with the help of python, and its machine learning algorithms to carry out the analysis tasks between successive trials which develops regression models for predicting the parameter values based on the previous trials with in a shorter period of time increasing the human potential of calibration.
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