Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions

Vehicles with dual clutch transmissions (DCT) are well known for their comfortable drivability since gear shifts can be performed jerklessly. The ability of blending the torque during from one to other, making type automated transmission a perfect alternative converters, which also comes higher efficiency. Nevertheless, DCT have some drawbacks. actuation two clutches requires an immense control effort, is handled in implementation wide range software functions on unit (TCU). These usually contain parameters, makes behavior adaptable different vehicle and engine platforms. adaption these parameters called calibration, iterative time-consuming process. calibration embedded solutions units widely problem automotive industry. any subsystem (e.g., engine, transmission, suspension, driver assistance systems autonomous driving, etc.) costly test trips ambient conditions. To reduce effort accompanying use professionals, several approaches automize process proposed. Due fact that solution desired optimize problems, generic metaheuristic approach aimed. Regardless, scope current research optimization launch vehicles equipped since, particularly at low speeds, must meet intention (drivers tend more perceptive speeds). clarify characteristics launch, subject studies performed. influence factors, such as sound, maximal acceleration, acceleration build-up (mean jerk), reaction time, taken into account. Their evaluation relation criteria sportiness, comfort, jerkiness, examined based studies. According results study, reference values derived. contains study existing optimizing driving metaheuristics genetic algorithms, reinforcement learning, etc.). Since drawbacks (in problem) new proposed, outperforms ones. itself hybrid learning (RL) supervised (SL) applied loop environment, vehicle.