Abstract
A virtual 'model' is generally a mathematical surrogate of a physical system and when well correlated, serves as a basis for understanding the physical system in part or in entirety. In the automotive domain, this concept of understanding a physical vehicle system through a mathematical representation in a virtual vehicle model holds good and has been attempted in the recent past. Drive Quality defines a driver's 'experience' of a blend of controlled responses to an applied input. The 'experience' encompasses physical, biological and bio-chemical perception of vehicular motion by the human body. Drive Quality can be classified into ‘Performance’ and ‘Drivability’. While ‘Performance’ speaks of absolute quantifiable metrics with clear numbers characterizing an aspect of Drive Quality, ‘Drivability’ deals with finer aspects that indicate perceptible yet difficult-to-quantify relative aspects defining 'character' of the vehicle. A common challenge faced by car-makers is the recurrence of issues related to the Drivability aspect despite building a competitive edge on Performance and other quantifiable parameters ('USPs'). Owing to varied methods of trying to quantify Drivability aspects, there could be variations in comprehension of the 'Voice of Customer'. In the path to achieving the RLM (Road-to-Lab-to-Math) goal, one of the requirements is to establish strong virtual environments of high fidelity in terms of correlation levels with physical vehicles from standard test maneuvers run on-road and in-simulation. This would help advance many development activities, particularly on the propulsion system controls algorithm and calibration aspect, which also has a strong bearing on the final Drive Quality built into a vehicle. Once a new advanced Hybrid Vehicle Concept is available, a virtual platform with Embedded 'Controls-in-Loop' enables Drive Quality Prediction and Analysis. The objective is to develop a Controls Simulation Model for Hybrid Electric Vehicle, perform Drive Quality Analysis for both performance and drivability aspects and correlate to Vehicle Test Data, thereby enabling scope for 'front-loading' iterative activity in the development cycle.