Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles

Increasing costs of fossil fuels and the requirement of reduced CO2 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.

[1]  Robert Stawiarski,et al.  Kosten bremsen und Umwelt schonen mit hydraulischem Hybridantrieb , 2009 .

[2]  Yeong-Il Park,et al.  Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition , 2002 .

[3]  M Maarten Steinbuch,et al.  Energy management in hybrid electric vehicles: benefit of prediction , 2010 .

[4]  Zoran Filipi,et al.  Hydraulic Hybrid Propulsion for Heavy Vehicles: Combining the Simulation and Engine-In-the-Loop Techniques to Maximize the Fuel Economy and Emission Benefits , 2010 .

[5]  Oliver Sawodny,et al.  A Moving Horizon Based Sensor Fusion and Load Estimation Concept for Driving Profile - Based Operating Strategy Optimization in Hybrid Hydraulic Trucks , 2009 .

[6]  Andrew G. Alleyne,et al.  A model predictive control approach for a parallel hydraulic hybrid powertrain , 2011, Proceedings of the 2011 American Control Conference.

[7]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[8]  Lino Guzzella,et al.  Predictive Reference Signal Generator for Hybrid Electric Vehicles , 2009, IEEE Transactions on Vehicular Technology.

[9]  R. Beck,et al.  Model Predictive Control of a Parallel Hybrid Vehicle Drivetrain , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[10]  Guoqing Liu,et al.  Integrated modeling and optimization of a parallel hydraulic hybrid bus , 2010 .

[11]  Zoran Filipi,et al.  Optimization of Power Management Strategies for a Hydraulic Hybrid Medium Truck , 2002 .

[12]  Ilya V. Kolmanovsky,et al.  Optimally controlling Hybrid Electric Vehicles using path forecasting , 2009, 2009 American Control Conference.

[13]  L. Guzzella,et al.  Control of hybrid electric vehicles , 2007, IEEE Control Systems.

[14]  Michael Back,et al.  PREDICTIVE CONTROL OF DRIVETRAINS , 2002 .

[15]  Zoran Filipi,et al.  Optimal Power Management for a Hydraulic Hybrid Delivery Truck , 2004 .

[16]  Karl Henrik Johansson,et al.  Road grade estimation for look-ahead vehicle control using multiple measurement runs , 2010 .

[17]  Thierry-Marie Guerra,et al.  Control of a parallel hybrid powertrain: optimal control , 2004, IEEE Transactions on Vehicular Technology.

[18]  Oliver Sawodny,et al.  Determining the fuel savings potential of parallel hybrid hydraulic vehicles , 2011 .

[19]  F. R. Salmasi,et al.  Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends , 2007, IEEE Transactions on Vehicular Technology.

[20]  K. T. Chau,et al.  Overview of power management in hybrid electric vehicles , 2002 .

[21]  Anna G. Stefanopoulou,et al.  Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments , 2005 .

[22]  Oliver Sawodny,et al.  Modeling and identification of a built-in turbocharged diesel engine using standardized on-board measurement signals , 2008, 2008 IEEE International Conference on Control Applications.

[23]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process , 2005, IEEE Transactions on Vehicular Technology.

[24]  Raja Sengupta,et al.  Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization , 2005, IEEE Transactions on Control Systems Technology.

[25]  Huei Peng,et al.  Power management strategy for a parallel hybrid electric truck , 2003, IEEE Trans. Control. Syst. Technol..

[26]  Yi Lu Murphey,et al.  Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion , 2009, IEEE Transactions on Vehicular Technology.

[27]  Markus G Kliffken,et al.  Hydraulic Hybrid Systems for Commercial Vehicles , 2007 .

[28]  Stefano Di Cairano,et al.  MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle , 2012, IEEE Transactions on Control Systems Technology.

[29]  Oliver Sawodny,et al.  Location-based energy management optimization for hybrid hydraulic vehicles , 2013, 2013 American Control Conference.

[30]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .