Optimization of a Predictive Drive Strategy for a Plug-In Hybrid Vehicle : Optimierung der vorausschauenden Antriebssteuerung bei einem Plug-In Hybrid

Abstract:

As the world’s natural resources are depleting while the environment constraints on society become om- nipresent, new technological alternatives for transports are sought.

Over the past years, the regulations in terms of pollutants stimulated the industries’ innovation in almost all regions of the world, using directives such as the European emission standards have managed to limit the carbon dioxide emissions up to 82% between Euro 1 and Euro 4.

Between research towards new fuels and the improvement of internal combustion engines (ICE), one solution stands out: combining an electrical machine with a traditional ICE to reduce the environmental impact and the fuel consumption. The most advanced level of hybridization is the Plug-In Hybrid Vehicle (PHEV), which usually has a battery capacity allowing it to drive all electric on short ranges and can be recharged at a power socket. This technological solution opens to a wide range of possibilities and improvements in terms of fuel consumption, as well as commercial opportunities.

This master thesis, conducted at Audi AG in Ingolstadt (Germany) will focus on one development feature of Plug-In Hybrid Vehicles: the predictive drive strategy. Driving all electric everywhere would be the best solution in terms of fuel economy. However, a PHEV does not always allow it, especially on long rides. Mainly three drive modes are available: an electric mode that will use the ICE as seldom as possible, a hybrid mode that allows electrical experience at low speed and uses the ICE for higher needs in power, and a charge mode aiming at recharging the battery through the ICE. Some enhanced algorithms are also able to detect the different environments the vehicle is expected to drive through, and compute fuel and electrical consumption estimates for each modes available. Based on this information, an optimizer will determine the best drive strategy - i.e the best temporal combination of modes - in terms of fuel consumption, but also taking into account some external requirements, such as Zero Emission Zones that might come up along the route.

The method used to compute the best drive strategy relies on four main stages. A first step is to identify the different driving environments constituting the route, based on long-range power predictions. This can be done using statistical comparison methods based on the assumption that one driving environment can be characterised by a range of values for a single parameter. The interval the parameter belongs to will determine the driving environment. The Kolmogorov-Smirnov test function is the second method used, which compares the cumulative distributions of two series of values and assesses their similarity. Once the itinerary is properly split into segments, it is possible to compute the expected fuel and electrical consumptions by taking the drivetrain efficiency chain, the regenerative braking and E-boost characteristics into account. For each section, a high- priority drive mode can also be given in order to allow a more flexible optimization in terms of coding and algorithmic. Mainly two prioritization options have been studied in this thesis. One pertains to the use a finite number of identified driving environments, to which a prioritization of the driving mode has been performed offline beforehand. The second option, the online priority, uses realtime computed values for a given segment and compares them in order to find the mode to be prioritized. Finally, two optimizers have been developed to compute an efficient drive strategy. The tree-based global optimizer considers the drive mode combinations for the whole route and selects the most efficient one, while a simplified optimizer would only improve a given basic strategy to reduce the fuel consumption.

The results of the comparison methods show interesting perspectives for the Kolmogorov-Smirnov test func- tion, as it only tells two segments apart without trying to identify the environment they belong to, hence a faster implementation. The parametric method proved its efficiency when working with a dual-environment condition, although an extension to more than two environments would make the whole interval computation process much more complex. Then, the consumption computation processes have been precisely defined. Fi- nally, both optimizers gave interesting results. The global optimizer is very complex algorithm which requires considerable CPU resources. However, it provides absolute optima to the optimization problem that will be used to observe the trend, behaviour and accuracy of some drive strategies. The simplified optimizer is a suggestion of a faster algorithm that does not computes the best solution but rather a good one. If the quality of the resulting solution is very route-dependant, it can provide drive strategies very close to the best ones computesby the global optimizer.