# Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions
## 2 Optimization Problem and Objective Functions  
The behavior of a vehicle and its components can usually be influenced through calibration parameters. These parameters are part of the software of the corresponding vehicle component, which offers the possibility of adjusting the software to different types of vehicles without changing software functions, or the entire software. These parameters also enable the possibility to influence the system behavior to meet customer requirements. The calibration parameters have to be set iteratively under different environmental conditions to ensure a functioning vehicle with the most achievable quality and by meeting any requirement in most possible conditions. Therefore, many calibration parameters are required within the software, which results in a high calibration effort. To influence the driving behavior of a vehicle these calibration parameters are implemented in the TCU software. To set these parameters, experienced engineers usually optimize the driving behavior based on their subjective feelings. Therefore, the assessment of the driving behavior can vary between different engineers, due to the fact that the evaluation is biased by personal preferences. This obviously can lead to further adjustment loops and an increased calibration effort [6]. Hence, to reduce the workload of an engineer, the automated optimization of the drivability is strived to be deployed in the development process. Therefore, subjective feelings have to be transferred into objective measurements to let the optimization algorithms monitor the quality of the driving behavior without the input of an engineer [2]. The transfer of these subjective feelings in this study takes place by measuring the launch behavior with four different objectives. The objectives are: (1) the acceleration objective, (2) the reaction time objective, (3) the engine speed objective, and (4) the clutch torque objective.  
Before applying these objectives in the automated optimization, the objectives are first investigated in a driving simulator. The driving simulator allows the investigation of the influence factors in a reproducible test environment and the conduct of the test subject studies. With the help of statistical tests, the EDTs can be detected, which provide certain optimization targets for the calibration of the powertrain.  
The driving simulator is developed for the investigation of human perception during longitudinal drive maneuvers and has the ability to simulate the dynamics in the longitudinal direction of the vehicle. It is able to represent the acceleration and deceleration by combining translatory and rotatory movements of the driver cabin [42]. By applyingvirtual reality technology, simulating the driving environment (e.g., the streets, traffic signs, landscape), and synthesizing the engine sound the test subjects face a reality-close driving situation. Besides the visual and auditory senses, the simulation of the haptic sense is considered as well. More details about the driving simulator can be found in [43].  
In the studies, the test subjects experienced launch procedures, with varying objectives (engine speed with drops, maximal acceleration, jerk, and reaction time). Further, the subjects had to evaluate these launches on a scale from 1 to 5 according to the evaluation criteria: comfortable, acceptable, jerky, sporty, and agile. The evaluations were processed and analyzed with statistical tests. The utilized tests in the studies were the variance analysis [44], the \(t\)-test, and the Wilcoxon-test. These methods study the influence of the objectives and can detect whether the evaluations of launches are significant differences when the objectives vary. [PERSON] et al. [7] introduced the test subject study and the study results of the objectives of maximal acceleration and jerk in detail. The investigation process of other objectives is comparable.  
The optimization objectives are separated into customer objectives (Section 2.1), as well as in discomfort objectives (Section 2.2).