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Reference Number EP/H050337/1
Title The Design and Development of Multivariable Controls with the Application for Energy Management of Hybrid Electric Vehicles.
Status Completed
Energy Categories ENERGY EFFICIENCY(Transport) 100%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr F Assadian
No email address given
School of Engineering
Cranfield University
Award Type Standard
Funding Source EPSRC
Start Date 22 June 2010
End Date 21 December 2011
Duration 18 months
Total Grant Value £204,729
Industrial Sectors Transport Systems and Vehicles
Region East of England
Programme Energy : Energy
Investigators Principal Investigator Dr F Assadian , School of Engineering, Cranfield University (99.999%)
  Other Investigator Professor ND Vaughan , School of Engineering, Cranfield University (0.001%)
  Industrial Collaborator Project Contact , Ford Motor Company (0.000%)
Project Contact , Jaguar Land Rover Limited (0.000%)
Project Contact , Lotus Engineering (0.000%)
Project Contact , KPIT Infosystems Ltd. (0.000%)
Project Contact , AVL Powertrain UK Ltd (0.000%)
Web Site
Abstract In recent years, a lot of research has been carried out in the field of energy management for full HEVs and EVs. Strategies that are based on heuristics can be easily implemented in a real vehicle by using a rule-based strategy or by using fuzzy logic. To find the global optimal solution, control techniques such as linear programming, quadratic programming, optimal control, especially dynamic programming have been studied. A different approach has been proposed in some recent work. In this approach instead of considering one particular driving cycle for calculating an optimal control law, a set of driving cycles is considered, resulting in a stochastic optimization approach.After considering the optimisation based methods described above, the following observation is made. The principal common drawback of all the aforementioned strategies is consideration of drivability as an afterthought. The drivability is considered in an ad hoc fashion as these approaches are not dynamic model based. At best, techniques such as game-theoretic optimisation utilises quasi-static models which are not sufficient to address drivability requirements. Another important drawback of these strategies is robustness. Theses strategies do not include a feedback control block, so the robust performance of the strategy is not guaranteed as the vehicle parameters deviated from their nominal conditions. Ad hoc adaptation of these strategies for drivability does impact their optimality and therefore a negative impact on the emissions and fuel consumption.The robust multivariable control has extensively been used in the aerospace industry, process control and chemical and petrochemical plants. However, the application of the multivariable control in the automotive industry is scarce and when it comes to the energy management development, there is no application. There exists multivariable based method such as Model Predictive Controls, however, these techniques are mainly time domain based, which has its drawbacks and they have the limitations such as on-line implementation is not possible. The multivariable control design has been used by this author for integration of active chassis systems (Active Roll Control and Active Limited Slip Differential). There is a tremendous opportunity for reducing fuel consumption, emissions, and calibration effort to production utilising this methodology. The user fuel economy data obtained from Toyota Prius has shown that there is approximately 10-15% difference between the average real world and driving cycle fuel economy. This difference is mainly due to the drivability impact. Our conservative projection of fuel savings and CO2 reduction of this proposal is 5-7%. An approximate annual savings of 400 Million, due to reduction in calibration effort, based on a volume projection of 50,000 hybrid vehicles after the end of this project. Furthermore, additional possible benefits and savings are foreseeable due to transferability between platform variants.The aim of this work is to design and develop a multivariable feedback controller to replace the ad hoc design currently used to address robustness and vehicle drivability issues as stated above. The robust feedback multivariable is derived based on the dynamic models of the plant so the drivability requirements are addressed as a part of the design and development of the controller and it is not an afterthought. Furthermore, the lack of robustness is not an issue with the Multivariable controls as they are inherently derived from feedback policies
Publications (none)
Final Report (none)
Added to Database 10/01/11