New Control Methodology for the Next Generation of Engine Management Systems

Lead Research Organisation: University of Warwick
Department Name: Sch of Engineering


Despite of the fact that electrical cars are under development and have the potential to provide alternatives for short distance light duty transport, the internal combustion engine will continue to be the main power unit in vehicles for several decades to come. Compared with extensive research on combustion and after-treatment systems, little work has been completed with respect to engine system control optimisation, leaving considerable room to improve fuel economy and lower emissions. Current engine calibration process relies on deriving static tabular relationships and the corresponding values between each calibrated engine operating point, with closed-loop feedback control to adjust the settings accordingly for air-fuel ratio control in real engine operation so as to meet the performance targets and emissions legislation. Such a widely adopted method, however, is not efficient in achieving the best fuel economy of the vehicle due to the constraints in the time duration and cost of engine-bed based calibration. Environmental conditions changes, the time required for the closed-loop control to respond, cycle-by-cycle variations, and cylinder-to-cylinder variations make the current engine control impossible to handle the the optimisation of the engine functionalities.

The development trend for future engines is towards an on-board intelligence for control and calibration and some research activities for the development of model based control systems are reported in literature. However, feasible strategies to control the engine operation cycle-by-cycle and cylinder-by-cylinder are not yet available.

Expanding the work of the applicants in the related areas for many years, the overall Goal of this project is to use a combination of joint efforts from 3 research groups with expertise of engine technology, control technology and computing algorithm in order to develop and test a new engine control and calibration methodology with on-line intelligence built in. This overall goal will be achieved through realising the following objectives:

(1) To develop a full real-time multi-cylinder engine model for cylinder-resolved-control purpose
(2) To develop a novel engine control strategy involving optimization of control points and control point locations, and multi-objective evaluation of test cycle performance
(3) To develop dynamic multi-objective evolutionary algorithms for online engine control optimization
(4) To demonstrate the implementation of the engine control models initially on Hardware-in-the-Loop (HIL) dSPACE system and then further rapid prototyping on a test engine.
(5) To compare the engine performance using the new techniques with traditional calibration and control approaches, and demonstrate improvements in terms of engine output, fuel consumption, and emissions.

The new engine control methodology will be evaluated on a new Jaguar gasoline direct injection (GDI) engine model.
Description The project has developed a method to allow the engine calibration process conducted in an automatic way and the time taken is shortened as the intelligent algorithms will find the solution quickly.
Exploitation Route Yes, the algorithms can be taken further by automotive industry.
Sectors Aerospace, Defence and Marine,Energy,Environment,Transport

Description Haldex - optimal break 
Organisation Haldex
Country Sweden 
Sector Private 
PI Contribution Following the EPSRC project, the collaboration with Haldex started from last July. Currently, a project student started working on optimisation of Haldex ABS system. Funding application is in consideration.
Collaborator Contribution Sponsor student project and supervise the project.
Impact Student training.
Start Year 2018
Description JLR 
Organisation Jaguar Land Rover
Country United Kingdom 
Sector Private 
PI Contribution Helped JLR in modelling low pressure exhaust valves
Collaborator Contribution Student placement, supervision of Y3 projects
Impact Student report The initial model of LPEV.
Start Year 2013
Title Sub-structured Artificial Neural Network Optimisation Tool 
Description The optimisation tool have a set of neural network components. The users can involve those simulation optimisation blocks through an optimisation process. THe model can represent the best fit engine model for engine calibration. 
Type Of Technology Software 
Year Produced 2017 
Impact The software will lead to auto calbration of engine provess which will shorten the time duration of an engine calbration.