Velocity trajectory optimization in Hybrid Electric trucks

Hybrid Electric Vehicles (HEVs) enable fuel savings by re-using kinetic and potential energy that was recovered and stored in a battery during braking or driving down hill. Besides, the vehicle itself can be seen as a storage device, where kinetic energy can be stored and retrieved by changing the forward velocity. It is beneficial for fuel consumption to optimize the velocity trajectory in two ways; i) to assist the driver in tracking an optimal velocity trajectory (e.g. input to an Adaptive Cruise Controller); ii) to estimate the future power request trajectory which can be used to optimize the hybrid components use. Taking advantage of satellite navigation, together with the vehicles current mass and road load parameters, an optimization problem is formulated, and solved for a driver defined time constraint. Despite tight velocity constraints, this can result in 5% fuel saving compared to a Cruise Controller with constant velocity setpoint. The benefit of velocity trajectory optimization is indicated with experimental results.

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

[2]  M Maarten Steinbuch,et al.  Predictive Cruise Control in Hybrid Electric Vehicles , 2009 .

[3]  Erik Hellström,et al.  Look-ahead control for heavy trucks to minimize trip time and fuel consumption , 2007 .

[4]  Erik Hellström,et al.  Look-ahead Control for Heavy Trucks to minimize Trip Time and Fuel Consumption , 2007 .

[5]  Masafumi Miyatake,et al.  Application of dynamic programming to the optimization of the running profile of a train , 2004 .

[6]  V. Monastyrsky,et al.  Rapid computation of optimal control for vehicles , 1993 .

[7]  Jan Swevers,et al.  Model predictive control of automotive powertrains - first experimental results , 2008, 2008 47th IEEE Conference on Decision and Control.

[8]  M Maarten Steinbuch,et al.  Optimal Energy Management in Hybrid Electric Trucks Using Route Information , 2010 .

[9]  Hans Bruneel Heavy Duty Testing Cycles Development: A New Methodology , 2000 .

[10]  van T.A.C. Keulen,et al.  Influence of driver, route and vehicle mass on hybrid electric truck fuel economy , 2008 .

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

[12]  A P Stoicescu On fuel-optimal velocity control of a motor vehicle , 1995 .

[13]  Olaf Stursberg,et al.  Combined time and fuel optimal driving of trucks based on a hybrid model , 2009, 2009 European Control Conference (ECC).