Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles

Increasing costs of fossil fuels and the requirement of reduced
$ \hbox{CO}_{2}$
emissions for road vehicles make the development of alternative propulsion systems a top priority in automotive research. Hybrid hydraulic vehicles (HHVs) can contribute to improving the fuel efficiency of heavy vehicles such as garbage trucks and city buses. The combination of a conventional diesel engine with an additional hydraulic powertrain allows for regenerative braking. Further improvements with regard to fuel efficiency become possible through additional optimization of the energy management strategy, which decides when to apply which propulsion system. Rule-based strategies are the state of the art, but they cannot utilize the full potential because their performance is only superior on the cycles for which they have been developed. Approaches including numerical optimization are independent from the actual drive cycle and result in much higher savings. However, these techniques usually require a prediction of the driving profile. In this paper, a complete solution for predictive energy management in HHVs is presented. The fuel savings obtained through the developed algorithms used for prediction and optimization are determined in a simulation study, and the functionality of the concept is proven in a hybrid hydraulic testing vehicle.