Implementation of an Open-loop Controller to Design the Longitudinal Vehicle Dynamics in Passenger Cars

Paper #:
  • 2017-01-1107

Published:
  • 2017-03-28
Abstract:
In order to offer a wide range of driving experiences to their customers, original equipment manufacturers implement different driving programs. The driver is capable of manually switching between these programs which alter drivability parameters in the engine control unit. As a result, acceleration forces and gradients are modified, changing the perceived driving experience. Nowadays, drivability is calibrated iteratively through road testing. Hence, the resulting set of parameters is strongly dependent on the individual sentiments of the test engineers. It is shown, that implementing a set of objective criteria offers a way of eliminating personal preferences and sentiment from the drivability calibration process. In combination with the expertise of the testing engineers, the desired vehicle behavior can be transferred into a transient set point sequence to design the acceleration behavior. A closed-loop controller controls the set point sequence representing the longitudinal dynamic of the vehicle in order to meet the customer expectations. This approach minimizes the number of calibration parameters, thus speeding up the calibration process. However, comfort reducing drive train oscillations (nonlinearities e.g. effect of backlash) can still be alleviated effectively. An Extended KALMAN-Filter estimates the longitudinal vehicle behavior, thus providing a control variable to the closed-loop controller. In combination with the Active Set Method, this online algorithm estimates the sum of the half shaft torque as adequate signal for the longitudinal dynamic. Due to the fact that the predictions are based on a physical drive train model, no additional hardware-sensors are required to obtain good results during real driving tests. If the mass inertia of the engine is known, the online algorithm is also capable to estimate all other required model parameters autonomously. This novel approach works online and independently of different combustion concepts or drive train architectures (e.g. front-wheel drive, rear-wheel drive) if a gear position is set.
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