Kazuhide Togai

Vibration suppression strategy with model based command shaping: application to passenger car powertrain

Speed feedback into driving force is an effective measure to add damping for motion control systems with no damping element, while effect of the feedback is decreased for systems with larger delay. A passenger car powertrain system is prone to be oscillatory and engine torque has structural dead time. An engine torque control algorithm that generates engine torque command from an accelerator operation to reduce vehicle longitudinal vibration without deteriorating acceleration using an online vibration model is developed. Numerical simulations and experiments show effectiveness of the proposed method.

Input Torque Shaping for Driveline NVH Improvement and Torque Profile Approximation Problem with Combustion Pressure

The powertrain is the largest excitation source of automotive noise and vibration. Torque variations from the engine cause noise and vibration in two frequency regimes: Engine firing fluctuations at the engine rotation frequency and its harmonics and driver-demanded propulsion torque variations at lower frequencies. This paper examines the mechanisms of gear rattle noise caused by engine firing fluctuations and shock and jerk caused by propulsion torque variations. Gear rattle behaviour is simulated and various approaches for reducing gear rattle are investigated. It is found that isolation of engine firing fluctuations from the transmission is the most effective practical way to reduce rattle. Torque profile shaping by robust control is a method that can be used to reduce shock and jerk. A pre-compensator approach based on knowledge of the driveline resonant properties is proposed and the sensitivity of the controller to errors in the assumed resonant frequencies is investigated. It is shown that one of the constraints of this strategy is that the resolution of control system depends on the firing frequency.

A Real-Coded Dynamic Genetic Algorithm for Optimizing Driver's Model in Emission Test Cycle

This paper proposes a real-coded Genetic Algorithm for finding optimal parameters values of a drivers model. Drivers models are one of the important requirements in de-signing controllers for vehicle electrification technology. Latest drivers model that includes drivers response delay, drivers response time and the level of drivers skills has been proposed previously. This model contains several critical parameters that need to be optimally determined in accordance with the vehicles properties as well as the test cycle data. The objective of this research is to search for the near-optimal values of drivers model parameters that minimize the error between the target speed and the models speed, as well as maximize the smoothness of the driving operation. The algorithm is evaluated using two driving cycle data, Japans 10-15 mode cycle and Europes NEDC. The solutions are compared with other existing heuristic techniques. Experimental results demonstrate a good performance and the consistency of the proposed algorithm.