Bernard Bäker

Predictive driving strategies under urban conditions for reducing fuel consumption based on vehicle environment information

This brief deals with the improvement of a vehicles pass-through of predictively known urban driving situations concerning its fuel consumption. Todays technology enables the prediction of information about traffic events. This information can be used to identify efficient driving strategies. The main aim is to reduce the dynamics in the velocity profiles of driving situations and with it the corresponding fuel consumption in urban traffic. An algorithm has been built to calculate fuel consumption optimized driving trajectories. Input parameters are temporal and spatial depending constraints of the driving situation as well as other restrictions like a speed-limit. The main objective of the function was to enable a situation adaptive reaction to every predictively known forthcoming traffic event. Thus an optimized driving trajectory can be steadily calculated for the next route section of the vehicle provided that predictive information about the traffic events are available. The higher the availability of information the better an optimization of the driving strategies will be possible. Fuel characteristics and other energetically relevant data for a real-world vehicle have been created by detailed simulation to evaluate the fuel consumption of the driving strategies. For demonstration purposes the common driving situation ”traffic light” was chosen. The fuel consumption calculated for the predictive driving strategies is compared to the consumption of a simulated average driver without predictive information. The calculated potentials have been verified by measuring the fuel consumption of an experimental vehicle for the simulated driving strategies.

Efficiency-increasing driver assistance at signalized intersections using predictive traffic state estimation

Frequent necessary stops at red traffic signals and related braking and acceleration processes significantly affect the fuel consumption and emission rates of a vehicle. The efficiency-increasing potential (fuel saving potential) of a predictive driver assistance system proposing an intelligent vehicle speed adaption well in advance the intersection is examined by a traffic flow model-based simulation. The considered predictive speed assistance system is based on the transmission of traffic light controller information into the approaching vehicle. Besides information on the traffic light timing chart, provision of accurate information on current traffic conditions between the current position of the vehicle and the stop line are necessary for a majority of driving situations. Here, the use of queue length estimation (QLE) techniques based on commonly installed induction loop sensor systems is described to extend the functional benefit of the driver assistance system. From QLE data, two additional main indicators (distance to virtual stop line and time to cleared intersection) can be derived to calculate a energy-efficient speed profile. For a single vehicle approaching an isolated intersection signalized with a standard timing cycle fuel savings of 8–11% can be found. The benefit of a QLE-included control scheme of the assistance system is demonstrated by simulation. Simulation results show situation-specific fuel saving potential differences of up to 28% compared to a basic system control scheme (only traffic light timing information).

Predictive online control for hybrids: Resolving the conflict between global optimality, robustness and real-time capability

In this paper an approach for the real time optimal control of vehicles with more than one source for providing traction power (e.g. HEVs) is presented. After a short classification of existing approaches and their respective characteristics the new modular concept as a combination of two algorithms is introduced. For the basic online management an equivalent consumption minimization strategy (ECMS) is implemented. The adaption towards changing driving conditions is realized by an independent calculation and adjustment of the main decision criterion of the ECMS towards the predicted operational profile using a calculation time optimized dynamic programming approach.

Predictive supervisory control strategy for parallel HEVs using former velocity trajectories

This article deals with the usage of forecast information for an optimized supervisory control strategy in hybrid power trains. In contrast to other publications the influence of the prediction quality towards fuel consumption is discussed. Firstly a supervisory control strategy without using predictive driving trajectories is developed. Secondly the integration of predictive information is presented to reduce fuel consumption. The battery size (maximum battery energy) is varied using a 100% ideal prediction. The simulation results are compared with the global optimum reference fuel consumption calculated by Richard Bellmans Dynamic Programming. Velocity prediction is imprecise in real driving scenarios. Therefore recorded driving trajectories of former journeys on the same route were used as prediction source to examine the influence of imprecise prediction to the resulting fuel consumption of the developed predictive control strategy.

An iterative algorithm for the global optimal predictive control of hybrid electric vehicles

Increasing energy storage capacities, especially in plug-in hybrid vehicles, lead to high computational burden using conventional methods like dynamic programming (DP) for globally solving the control problem. DP with its high calculation times may be acceptable for offline simulations, but used as a predictive adaption algorithm for the main decision criterion of a real-time ECMS (equivalent consumption minimization strategy) a more feasible solution has to be found [1]. Hence, in this paper an approach replacing the originally implemented predictive dynamic programming (PDP) by a fast iterative algorithm is presented and compared to the results gained by DP with respect to computational effort and optimality.

ElisaTM - Car to infrastructure communication in the field

Car to infrastructure (C2I) communication as an aspect of Intelligent Transportation Systems (ITS) is a topic that is currently under wide research. Most works however deal with theoretical analysis, simulative evaluation or closed testbeds. There are only a few publications describing real world application of C2I on real world streets with real world traffic. In this paper we present the results of initial tests and measurements performed in the testbed ElisaTM (Efficient Light Signal Adaptation Testbed Munich). The testbed consists of four intersections in a mostly residential area in Munich, Germany. The results include an analysis of data availability and data quality under different traffic conditions.

Optimal Energy Management and Velocity Control of Hybrid Electric Vehicles

An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagins Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage, engine state and gear, kinetic energy, and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space.

Structured Development and Evaluation of Electric/Electronic-Architectures of the Electric Power Train

The evaluation of electric/electronic-architectures is the main topic of this document. The electric power train is chosen as example system, as it embodies the typical challenges of modern e/e-architecture development. New electric and electronic functionalities are developed and required in the automotive industry and already existing mechanically realized functionalities are exchanged by electric and electronic ones. The electric power train meats both occurrences at one time. An introduction is given to the widely used scenario based evaluation method of electrical systems and software, as well as two alternative evaluation methods. The methods are analyzed regarding their usability to the given use-case. Finally the chosen method is described in detail. With the topic of evaluation, this paper is to be understood as a contribution to the optimization of e/e-architectures; in specific to the logical allocation, functional integration, and interconnection of the to be integrated technical modules and components.

Potential of an electric brake resistor to increase the efficiency of electric vehicles

Electric brake resistors are well known in the domain of power electronics, railway or elevator technology to guarantee electric braking or to damp high electric power peaks. The generated heat energy, however, is usually dissipated. This work deals with the potential of an automotive application of the electric brake resistor to enable brake energy regeneration (recuperation) also at low temperatures and high state of charge when the charge performance of the traction battery is limited. By reusing the excess recuperation energy to support the vehicles cabin heating, the overall energy efficiency can be increased. In this paper three classes of battery electric vehicles are simulated with different driving environments and start parameters to assess the influences on the efficiency potential of this application. It is shown that, depending on the start conditions and drive cycle, the total energy demand can be reduced by up to 12% with the use of a 6kW rated brake resistor.

Energy-efficient routing strategies based on real-time data of a local traffic management center

Electric cars form nowadays a competitive alternative to conventional cars with combustion engine. Unfortunately, there is still the problem of a relatively short range electric cars are able to reach which makes it quite unattractive to costumers. Many research projects deal with this issue to increase the range by following different approaches. E-City-Routing is one of those projects aiming to develop an energy-efficient routing algorithm especially for urban areas with its test field in Dresden, Germany. Thereby not only vehicle- and infrastructure-related impact factors are considered. Also dynamic traffic data provided by the local traffic management center such as current traffic situations and traffic signal states are taken into account in order to route electric cars with a minimum energy consumption through the city.