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Efficient Simulation and Optimal Control for Vehicle Propulsion
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Efficient drive cycle simulation of longitudinal vehicle propulsion models is an important aid for design and analysis of power trains. Tools on the market today mainly use two different methods for such simulations, forward dynamic or quasi-static inverse simulation. Here known theory for stable inversion of non linear systems is used in order to combine the fast simulation times of the quasi-static inverse simulation with the ability of including transient dynamics as in the forward dynamic simulation. The stable inversion technique with a new implicit driver model together forms a new concept, inverse dynamic simulation. This technique is demonstrated feasible for vehicle propulsion simulation and specifically on three powertrain applications that include important dynamics that can not be handled using quasi-static inverse simulation. The extensions are engine dynamics, drive line dynamics, and gas flow dynamics for diesel engines, which also are selected to represent important properties such as zero dynamics, resonances, and non-minimum phase systems. It is shown that inverse dynamic simulation is easy to set up, gives short simulation times, and gives consistent results for design space exploration. This makes inverse dynamic simulation a suitable method to use for drive cycle simulation, especially in situations requiring many simulations, such as optimization over design space, powertrain configuration optimization, or development of powertrain control strategies.

Optimal vehicle propulsion control is developed with special focus on heavy trucks used for long haulage. The power to mass ratio for a typical heavy duty truck makes even moderate road slopes significant in the sense that it is impossible to keep a constant cruising speed. This gives an interesting problem how to control vehicle speed such that fuel consumption is minimized. Todays telematic systems together with three dimensional roadmaps can provide the vehicle control system with information of the road topography. This enables intelligent cruise controllers that utilize this information to control engine fueling and gear shifting such that an optimal speed trajectory is obtained.

First the optimal control problem is solved numerically by dynamic programming, giving a controller with real time capabilities that can be used on-line in the vehicles control system. Simulations of such a system on authentic road profiles show that it has potential for significant fuel savings. To achieve knowledge about the underlying physics that affects the optimal solution, the optimal control problem is solved in detail and analytical expressions for the conditions of optimality are derived. Those expressions are then used to find optimal solutions on constructed test road profiles. Such test cases point out the typical behavior of an optimal solution and also which parameters that are decisive for the fuel minimization problem, and also how they quantitatively influence the behavior. It is for example shown that small non-linearities in the engine torque characteristics have significant effect on the optimal control strategy. The solutions for the non linear engine model have a smoother character but also require longer prediction horizons. For optimal gear ratio control it is shown that the maximum fueling function is essential for the solution. For example, in the case of a continuously variable transmission it is shown that the gear ratio never is chosen such that engine speed exceeds the speed of maximum engine power. For a discrete step transmission the gear shifting losses are essential for the optimal shift positions, but over all the solutions are close to continuous solutions.

Place, publisher, year, edition, pages
Institutionen för systemteknik , 2008.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1180
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-11475ISBN: 978-91-7393-904-1 (print)OAI: oai:DiVA.org:liu-11475DiVA: diva2:17910
Public defence
2008-05-26, Visionen, Hus B, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2008-05-08 Created: 2008-05-08 Last updated: 2009-03-10
List of papers
1. Efficient Drive Cycle Simulation
Open this publication in new window or tab >>Efficient Drive Cycle Simulation
2008 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, Vol. 57, no 3, 1442-1453 p.Article in journal (Refereed) Published
Abstract [en]

Drive cycle simulations of longitudinal vehicle models are important aids for the design and analysis of power trains, and tools currently on the market mainly use two different methods for such simulations: the forward dynamic and quasi-static inverse simulations. Here, a known theory for the stable inversion of nonlinear systems is used to combine the fast simulation times of the quasi-static inverse simulation with the ability of the forward dynamic simulation to include transient dynamics. The stable inversion technique and a new implicit driver model together form a new concept: inverse dynamic simulation. This technique is demonstrated to be feasible for vehicle propulsion simulation and specifically for three power train applications that include important dynamics that cannot be handled using quasi-static inverse simulation. The extensions are engine dynamics, driveline dynamics, and gas flow dynamics for diesel engines, which are also selected to represent important properties, such as zero dynamics, resonances, and nonminimum-phase systems. It is shown that inverse dynamic simulation is easy to set up, gives short simulation times, and gives consistent results for design space exploration. This makes inverse dynamic simulation a suitable method to use for drive cycle simulation, particularly in situations requiring many simulations, such as optimization over design space, power train configuration optimization, or the development of power train control strategies.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13142 (URN)10.1109/TVT.2007.907310 (DOI)
Available from: 2008-05-08 Created: 2008-05-08
2. Inverse Dynamic Simulation of Non-Quadratic MIMO Powertrain Models -Application to Hybrid Vehicles
Open this publication in new window or tab >>Inverse Dynamic Simulation of Non-Quadratic MIMO Powertrain Models -Application to Hybrid Vehicles
2006 (English)In: IEEE Vehicle Power and Propulsion, 2006, 1-6 p.Conference paper, Published paper (Refereed)
Abstract [en]

The method for stable inversion of nonlinear systems has earlier been demonstrated as an efficient tool in inverse dynamic vehicle propulsion simulation. However, that method is restricted to quadratic systems, i.e. systems with equally many inputs and outputs. Here that restriction is relaxed for typical vehicle propulsion simulation where the number of inputs, e.g. accelerator pedal and brake pedal, are greater than the number of outputs, e.g. vehicle speed. Also restrictions to states and inputs resulting in time varying system order and relative degree is discussed. A model of a parallel hybrid vehicle is used for demonstration.

Keyword
MIMO systems, brakes, electric propulsion, hybrid electric vehicles, nonlinear systems, power transmission (mechanical), time-varying systems, accelerator pedal, brake pedal, hybrid vehicles, inverse dynamic vehicle propulsion simulation, nonlinear systems, nonquadratic MIMO powertrain models, parallel hybrid vehicle, quadratic systems, time varying system, vehicle propulsion simulation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13143 (URN)10.1109/VPPC.2006.364292 (DOI)1-4244-0158-5 (ISBN)
Available from: 2008-05-08 Created: 2008-05-08 Last updated: 2009-05-14
3. Controlling Gear Engagement and disengagement on heavy trucks for minimization of fuel consumption
Open this publication in new window or tab >>Controlling Gear Engagement and disengagement on heavy trucks for minimization of fuel consumption
2005 (English)In: Proceedings of the 16th IFAC World Congress, 2005Conference paper, Published paper (Refereed)
Abstract [en]

There is a potential to save fuel for heavy trucks by storing kineticenergy in the vehicle when driving downhill, because the speed adds kinetic energyto the vehicle which can be used after the downhill slope to propell the vehicle.This behavior can be even more utilized by disengaging the gear to reduce thefriction in the driveline and thus increase the speed even more. Two differentcontrol strategies to choose when to disengage the gear is presented: One schemethat uses instantaneous inclination and one predictive control scheme that useslook ahead information of the road topology. Simulation results show that geardisengagement in downhills can reduce the fuel consumption about 3%.

Series
Keyword
Driveline Control, Dynamic Programming, Model Predictive Control
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13144 (URN)
Available from: 2008-05-08 Created: 2008-05-08 Last updated: 2009-05-14
4. A Real-Time Fuel-Optimal Cruise Controller for Heavy Trucks using Road Topography Information
Open this publication in new window or tab >>A Real-Time Fuel-Optimal Cruise Controller for Heavy Trucks using Road Topography Information
2006 (English)In: SAE World Congress, 2006Conference paper, Published paper (Refereed)
Abstract [en]

New and exciting possibilities in vehicle control are revealed by the consideration of topography, for example through the combination of GPS and three-dimensional road maps. How information about future road slopes can be utilized in a heavy truck is explored. The aim is set at reducing the fuel consumption over a route without increasing the total travel time.

A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function that weighs fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use is used to define the optimization criterion.

Computer simulations back and forth on 127 km of a typical highway route in Sweden show that the fuel consumption in a heavy truck can be reduced 2.5% with a negligible change in travel time.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13145 (URN)
Available from: 2008-05-08 Created: 2008-05-08
5. Explicit Fuel Optimal Speed Profiles for Heavy Trucks on a Set of Topograhic Road Profiles
Open this publication in new window or tab >>Explicit Fuel Optimal Speed Profiles for Heavy Trucks on a Set of Topograhic Road Profiles
2006 (English)In: Electronic Engine Controls, SAE World Congress 2006, 2006Conference paper, Published paper (Refereed)
Abstract [en]

The problem addressed is how to drive a heavy truck over various road topographies such that the fuel consumption is minimized. Using a realistic model of a truck powertrain, an optimization problem for minimization of fuel consumption is formulated. Through the solutions of this problem optimal speed profiles are found. An advantage here is that explicit analytical solutions can be found, and this is done for a few constructed test roads. The test roads are constructed to be easy enough to enable analytical solutions but still capture the important properties of real roads. In this way the obtained solutions provide explanations to some behavior obtained by ourselves and others using more elaborate modelling and numeric optimization like dynamic programming.

The results show that for level road and in small gradients the optimal solution is to drive with constant speed. For large gradients in downhill slopes it is optimal to utilize the kinetic energy of the vehicle to accelerate in order to gain speed. This speed increase is used to lower the speed on other road sections such that the total average speed is kept. Taking account for limitations of top speed the optimal speed profile changes to a strategy that minimizes brake usage. This is done by, e.g., slowing down before steep down gradients where the truck will accelerate even though the engine does not produce any torque.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13146 (URN)978-0-7680-1738-0 (ISBN)
Available from: 2008-05-08 Created: 2008-05-08 Last updated: 2009-09-14
6. Optimal Control Utilizing Analytical Solutions for Heavy Truck Cruise Control
Open this publication in new window or tab >>Optimal Control Utilizing Analytical Solutions for Heavy Truck Cruise Control
Manuscript (Other academic)
Identifiers
urn:nbn:se:liu:diva-13147 (URN)
Available from: 2008-05-08 Created: 2008-05-08 Last updated: 2010-01-13

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