Malonga Makosi, C., Rinderknecht, S., Binz, R., Uphaus, F. et al., "Implementation of an Open-Loop Controller to Design the Longitudinal Vehicle Dynamics in Passenger Cars," SAE Technical Paper 2017-01-1107, 2017, doi:10.4271/2017-01-1107.
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 incorporated within the engine control unit is strongly dependent on the individual sentiments and decisions of the test engineers.It is shown, that implementing a set of objective criteria offers a way to reduce the influences of personal preferences and sentiments in the drivability calibration process. In combination with the expertise of the test engineers, the desired vehicle behavior can be formalized into a transient set point sequence to give final shape to the acceleration behavior. To control the longitudinal dynamic of the vehicle in order to meet the customer expectations regarding to this set point sequence an open-loop controller is used.To alleviate comfort reducing drive train oscillations (non-linearities e.g. effect of backlash) effectively a trigger signal can be derived from the half shaft torque to reduce the engine torque build up abruptly during backlash traverse. Thus, no impulse will be induced into the drive train and comfort reducing oscillations can be minimized.The half shaft torque is an adequate signal for longitudinal vehicle dynamics and can be estimated in an online application. Therefore one single Extended KALMAN-Filter in combination with a drive train model is used to estimate the sum of the half shaft torque.Thus, 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 if a gear position is set.