Adaptive Brake By Wire: From Human Factors to Adaptive Implementation

The introduction of the Brake By Wire is replacing the traditional mechanical control systems with ECUs and it is raising the need to reproduce feelings of eliminated static mechanical components (i.e. hydraulic fluids, pumps and cylinders). Thanks to electromechanical actuators and human-machine interfaces (i.e. active pedal) it is possible to reproduce such feelings and, therefore, arbitrarily change their features. In this way it will be possible to customize the pedal feelings and the vehicle deceleration needed depending on several factors (i.e. surrounding braking scenario, driver characteristics, race vs day-by-day driving condition). Since braking maneuvers are typically critical and involve the driver, the design and development of brake by wire system must start from the consideration of human factors in order to increase acceptance and braking effectiveness. The objective of this research was to redesign the pedal feelings, making them adaptable to the surrounding. Driver acceptance and braking effectiveness could be highly improved by means of adaptive pedal feelings. The starting points of this research were humans factors in the braking domain. Literature and relevant studies have been taken into consideration to put into evidence human mechanisms and behaviors during braking phases. On such basis, two main results have been found out: braking use cases and pedal feeling curves. With regard to the pedal feelings curves, 4 different pedal curves which describe both force on brake pedal travel and acceleration on brake pedal travel are designed. The pedal feeling depends on several factors like the pedal travel, the pedal idle travel, the effort, responsiveness, deceleration perceived, ease of balance (i.e. ease of modulation), gradual braking and so on. Regarding braking use cases, they are described by vehicle data as speed, acceleration, angles and relevant rates, engine rpm, gas and brake pedal position/speed and so on. These use cases have been clustered in order to meet the 4 pedal curves. The research continued on the implementation of a Matlab/Simulink/Stateflow model for the use case recognition. Basing on the vehicle data, the model is able to find out in which use case the vehicle is (parking, low speed maneuvers, emergency, downhill, and so on). Once it finds out the scenario, the model applies the most appropriate pedal feeling curve (both force feedback and deceleration needed). In the end, the model commands an EC brushless motor which is responsible of the changing of static springs force feedback of the pedal. The scenario recognition model has been validated through vehicle data on real road whereas the pedal feeling and relevant motor behaviors have been validate on bench tests.