Predictive Control of Hybrid Electric Vehicles on Prescribed Routes
Doctoral thesis, 2009

At the highest level in the powertrain control system in a hybrid electric vehicle an energy management controller interprets the driver’s pedal actions as a torque that is to be delivered at the powertrain output. The energy management controller distributes this torque between the engine and the electric machines and decides when to change gear and when to turn off the engine for pure electric propulsion. The torque distribution should be done in such a way that the fuel consumption and emissions are minimized while at the same time assuring safe operation of the individual components in the powertrain. It is well known that the performance of the energy management controller depends on how well it is adapted to the driving conditions. By using a Global Positioning System (GPS) and a digital map it is possible to develop predictive energy management controllers that adapt to the current and future driving conditions, resulting in close to optimal performance. Dynamic programming is in some sense ideally suited to the problem since it can handle most practical requirements that can be put on the energy management controller. The disadvantage with dynamic programming is the high computational effort compared to other methods such as the Equivalent Consumption Minimization Strategy (ECMS). Dynamic programming has therefore previously primarily been a method for benchmarking energy management controllers in simulation studies. However, by settling for an approximate solution it is shown in the thesis that the computational effort of dynamic programming can be significantly reduced without a degradation of the performance. A novel predictive control scheme based on dynamic programming is presented. Using a GPS and a digital map the predictive control scheme plans both the charging and discharging of the energy storage system as well as the gear shifts and engine on/off decisions. When evaluated in simulations on a city bus, the fuel savings compared with a non adaptive ECMS controller are between 2-4.5% depending on the bus line. These results are for a powertrain variant that is not sensitive to the engine on/off decisions. When simulating a variant of the powertrain that is sensitive to the engine on/off decisions the savings are between 1-12%.

Energy Management

Powertrain Control

Predictive Control

Dynamic Programming

Vehicle Telematics

Hybrid Electric Vehicle

Hybrid Electric Powertrains

HC1, Hörsalsvägen 14, Chalmers
Opponent: Prof. Lino Guzzella, Mechanical and Process Engineering, ETH, Switzerland


Lars Johannesson

Chalmers, Signals and Systems, Systems and control

A Novel Algorithm for Predictive Control of Parallel Hybrid Powertrains based on Dynamic Programming

Fifth IFAC Symposium on Advances in Automotive Control,; (2007)

Paper in proceeding

Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains

Proceedings of the 17th IFAC World Congress, 2008,; (2008)

Paper in proceeding

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains using Stochastic Dynamic Programming

IEEE Transactions on Intelligent Transportation Systems,; Vol. 8(2007)p. 71-83

Journal article

Subject Categories

Vehicle Engineering

Control Engineering



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie

HC1, Hörsalsvägen 14, Chalmers

Opponent: Prof. Lino Guzzella, Mechanical and Process Engineering, ETH, Switzerland

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