Mattias Åsbogård

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed. The predictive control was based on information supplied by the vehicle navigation system. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The results indicate that, for an urban route with varying topography, the use of predictive control can significantly reduce the fuel consumption.

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route

Improving system design of a hybrid powertrain using stochastic drive cycles and dynamic programming

A new approach for system design of hybrid powertrains was demonstrated in a case study. The method is based on the following presumptions: The performance of a Hybrid Powertrain Concept (HPC) is evaluated using computer simulation; a HPC cannot be correctly evaluated without an Energy Management Strategy (EMS) for the energy buffer; the optimal EMS is different for each HPC. Dynamic programming was used to generate EMSs that were optimal for the vehicles intended traffic environment and for each given HPC, enabling evaluation of a large number of HPCs. Over-adaptation of the EMSs was avoided by using a stochastic drive cycle model. The final delivery is a competitive powertrain component sizing and the corresponding optimal EMS.

Evaluating Hybrid Vehicle Concepts using Optimal Control and Generic Powertrain Modelling

Abstract This paper describes how operational cost of various vehicle concepts can be estimated using optimal control based on non-linear programming. The advantage of comparing optimal results is that vehicle hardware concepts can be evaluated and compared without having to design any control system. Since the method should be applicable to all kinds of hybrid and conventional vehicles, the main focus lies on a modelling method that produces dynamic models appropriate for optimal control implementation. Results show that the method allows complex powertrain systems to be modelled in a way suitable for control optimisation.