Frederic Holzmann

Torque Vectoring with a feedback and feed forward controller - applied to a through the road hybrid electric vehicle

This paper concentrates on the torque commands for electric propulsion motors in a through the road hybrid electric vehicle. By using a linear quadratic gaussian controller, a flat feed forward controller and a linear desired value generator the lateral vehicle dynamics are influenced. Understeering, oversteering, agility and cornering speed can be optimized by proper controller design. A 14 degree of freedom vehicle model with a Dugoff tire model is used to simulate the vehicle behaviour. The simulation results show improved vehicle dynamics and increased handling for the driver compared to a equal distributed torque command.

Predictive estimation of the road-tire friction coefficient

The road-tire friction coefficient mu is as fundamental information for the algorithms dealing with the vehicle dynamics with high accuracy like in emergency cases. This paper introduces a new predictive methodology for the estimation of mu by using a camera and a microphone. After a description of the limits of the current methodologies, the new concept will be described step-by-step by following the data flow. The algorithm extracts the patterns corresponding of the different mu depending on the general luminance. These patterns will be matched on the current specimens to deduce the friction coefficient along the road ahead and a confidence value. Finally the results will be auto-correlated over the time to improve their stability. Moreover the reliability will be improved over a correlation with local measures based on microphone

Robots go automotive - the SPARC approach

This paper introduces a new concept for advanced driver assistance by means of a redundant architecture including all system components spanning from environment perception to vehicle controllers. The first part of this paper is an overview of the project framework and the research platforms. After that the elements of the architecture themselves will be described. The use of sensors and the fusion of their outputs will be presented. Different controllers will be used depending on the scenarios around the vehicle in order to provide a theoric solution. This solution will be downsized after that with a dynamic vehicle model to the feasible safe motion vectors. This list of motion vectors will be compared to the drivers command and will lead to the choose of his/her command or an other safe motion vector if the driver does not react convenient to the situation. The final part describes some preliminary results and concludes towards future work and research issues.

Smart and Green ACC: As Applied to a Through the Road Hybrid Electric Vehicle

The Smart and Green ACC (SAGA) or simply Green ACC (GACC) may be defined as a system which autonomously generates longitudinal control commands for a vehicle while balancing the safety and efficiency factors. In previous studies, the SAGA function is investigated as applied to a battery electric vehicle. As a continuation of the SAGA function development, this paper investigates the behavior of the autonomous longitudinal controller as applied to a “Through the Road” (TtR) hybrid electric vehicle. Given the presence of two power sources, the implementation of a SAGA system in HEV/PHEV has a higher level of complexity as compared to pure EV. As an autonomous longitudinal driver command generating system, SAGA acts as a surficial controller which is then combined with a core powertrain management system. The Equivalent Consumption Minimization Strategy (ECMS) is used to determine the optimum power split between the IC engine and electric motor.

Simplified Architecture by the Use of Decision Units

E/E architectures become more and more complex and thus hamper the introduction of new functions which in turn are essential for the development of electric vehicles. Consequently, new concepts are needed to enable an easy introduction of new technologies along with an interconnected but still manageable architecture. The proposed functional architecture approach aims at a safe operation of electric vehicles with a well defined decision path from the driver and the ADAS functions in the command layer to the actuators of the execution layer. The backbone of this architecture consists of two dedicated decision units. The command decision unit mitigates between requests coming from the driver and the ADAS functions whereas the execution decision unit selects the best actor signals for the current vehicle status. One benefit of this hierarchic approach is the ability to control the validity and the transition between driving modes by a set of rules which can be designed and adjusted to meet the vehicle requirements individually. The concept is not limiting the number of driving modes but is scalable for future applications, different market needs, and vehicle configurations.

Smart and Green ACC, Safety and Efficiency for a Longitudinal Driving Assistance

Driving Assistances aim at enhancing the driver safety and the comfort. Nowadays, the consumption is also a major criterion which must be integrated in the driving assistances. Then, we propose to redefine the behavior of an ACC with energy efficiency consideration to perform a Smart and Green ACC. We apply our development to the specific use case of the electric vehicle that allows regenerative braking. The ACC, once activated, operates under two possible modes (speed control and headway spacing control). We define the behavior of the driving assistance under these both possible modes, focusing on the distance control. We present the efficiency of various strategies without trading off safety. We conclude on the efficiency by presenting several use cases that show the SAGA behavior.

Introduction of a full redundant architecture into a vehicle by integration of a virtual driver

Concepts like Dickmanns et. al. show that automation can be sophisticated enough to drive fully autonomously in certain environments. The concept described here uses automation to introduce a virtual driver redundant to, but not excluding, the human driver. The first part of this paper is an overview of the project framework based on a model of the driver cognition. After that the requirements for the engineering of a virtual driver will be described into more details. An adapive cooperation and its use to optimize the drivers command depending on his fitness and the confidence of the virtual driver will be explained one section after. The final part shows the inherent problem of conflicts due to the introduction of this kind of technology.

Improvement of the driving safety using a virtual driver

This paper introduces a new concept for advanced driver assistance by means of a defined architecture including all system components redundant from the environment perception to the vehicle controllers. The first part analyzes the bottleneck of the current association driver with vehicle. After that a virtual driver, which computes the safety envelope of the motion vector of the vehicle, would be described. The drivers command would be monitored depending on the output of this virtual driver to give a feedback to the driver or to take the control of the vehicle. In the next part a concept of exchange of information to enhance the cognition of the vehicle and of the driver would be explained. At the end this paper would conclude towards future works and research issues.

Smart and Green ACC, adaptation of the ACC strategy for electric vehicle with regenerative capacity

This paper presents an optimization of a conventional Adaptive Cruise Control system (ACC) for the specific use of electric vehicles with regenerative capacity, namely the Smart and Green ACC (SAGA). Longitudinal control strategies, that are developed for the driving assistances, mainly aim at optimizing the safety and the comfort of the vehicle occupants. Electric vehicles have the possibility, depending on the architecture, the speed and the braking demand, to regenerate a part of the electric energy during the braking. Moreover, the electric vehicle range is currently limited. The opportunity to adapt the braking of an ACC system to extend slightly the range must not be avoided. When the ACC is active, the vehicle speed is controlled automatically either to maintain a given clearance to a forward vehicle, or to maintain the driver desired speed, whichever is lower. We define how we can optimize both mode and what is the impact, in term of safety and strategy, including the knowledge of the future of the road, integrating a navigation system.

Smart and Green ACC series: A city and highway specific approach towards a safe and efficient eDAS

The successful transition of fully electric vehicle into automotive market is plagued with expensive product prices and limited drive range. While manufacturers point to fuel saving benefits, the actual cost savings after the first battery replacement presents negative economics. Hence it is necessary to maximise the fuel saving costs and to prolong the battery life as much as possible. The situation calls for an assistant system which takes into consideration the inherent propulsion system dynamics of electric vehicle in two typical situations - namely city and highway. Here we propose a combination of two systems, first a dynamic programming based acceleration controller for city cycle and second, an eHorizon based ACC system for maximum recuperation on highways. This paper is an extension of papers [1,2] and forms a series which is attributed to the development of a partial or complete “Safe and energy efficient longitudinal vehicle controller”. Such a controller is named “SAGA” - Smart and Green Automated Cruise Control. It is an ecological driver assistance system (eDAS) that adapts the vehicle speed over all its speed range according to a forward vehicle and to road events in a near horizon (legal speed, curves, etc...) with an aim to reduce the energy consumption without compromising on safety.