Commercial Vehicle – Drive Cycle Development and Validation Using GT-RealDrive & 1D GT-SUITE Electric Vehicle Models

The paper presents an approach used to generate a customer-oriented drive cycle using the MATLAB-based drive cycle generation tool for EVs developed by Isuzu Technical Center of America. The drive cycle generation tool extracts important features from the customer vehicle data and compares it with the globally used pre-existing candidate cycle to generate a representative drive cycle. The tool can read multiple file formats of preprocessed data or raw data from the vehicle telematics systems. This data is then processed using a unique and efficient algorithm developed by the Isuzu engineers, calculating seven important vehicle dynamic parameters. These selected parameters are compared with the pre-existing candidate cycles used across the globe in multiple iterations to generate a custom representative drive cycle that best fits real-world customer driving behavior. The generated drive cycles are then validated using the 1D vehicle model in the GT-POWER tool, resulting in a fidelity of more than 95% [1] concerning the governing criteria and seven key parameters. The tool provides high flexibility and control over the process of generating a drive cycle. The seven key parameters selected in this tool are prominent parameters used globally to evaluate vehicle performance. By altering one of these parameters, one can simulate vehicle operation under various use scenarios. The tool gives users authority over the parameter weightage for drive cycle development. The drive cycle that is ultimately created using it will be a combination of varied proportions of the candidate cycles employed in the automotive sector [1]. This paper focuses on the usage of the tool, and the development of the tool has been explained. In this paper, the tool is utilized to analyze the 7 key parameters from customer driving patterns and these 7 parameters are used to develop a real-world drive cycle(routes) with the help of GT-RealDrive. The key parameters of these driving routes are then compared and judged based on correlation with the customer driving parameters and the best-suited iteration is finalized.