ISC provides independent, high quality control engineering consultancy and R & D services to the automotive industry, typically in powertrain control.
ISC specialises in:
Optimal control solutions can range from simple classical feedback/feedforward to advanced control, such as model-based methods like MPC. In all cases, simplicity remains a key objective in our solutions, providing performance criteria can be met.
Our standard control design software tools include MATLAB/Simulink and LabVIEW, including the range of dedicated Powertrain Controls Software and Hardware Modules from National Instruments. Our services can be tailored to specific applications or company needs and help clients shorten their learning curve.
We have provided services in various areas of the industry, including:
ISC has designed and developed bespoke control algorithms for modern Hybrid Control Powertrains. Our general services include Research and Development activities, to assess state-of-the-art and innovative control algorithms in modern automotive and other applications.
ISC has been contracted by the major automotive companies over the last two decades to evaluate advanced control methods whilst providing technical support from initial feasibility studies to the final stages of controller development and assessment. Our clients include automotive manufacturers, Tier-1 suppliers and ECU suppliers such as NXPGeneral Motors, Toyota, Chrysler, Ford, Cummins, Jaguar, Ricardo, Visteon and Freescale.
ISC has delivered many training courses to Ford, Cummins, Chrysler, Toyota and Jaguar, ranging from introductory level courses for groups who need an awareness of control applied to engines to advanced control topics for research teams.
In recent years, ISC has delivered training courses to many of the world’s largest automotive companies in the US and Europe. The aim is to train the next generation of engineers in the use of modern and innovative control techniques and to bring experienced engineers up to speed with the latest technological developments. Training courses included the following topics:
We can adapt individual courses to suit your company's needs.
ISC has developed a high-quality simulation system to study and evaluate the performance of Hybrid Powertrains. This is essential to the design of effective controllers to optimize powertrain performance. The performance of a hybrid powertrain depends on the ability of a controller to determine the instantaneous and likely short term and longer-term future vehicle operating conditions. To improve the global optimal performance requires consideration of the overall driving scenario faced by the car. ISC has developed a system to assess hybrid vehicle control using a benchmark based on a Dynamic Programming approach that can handle information about the overall driving scenario. The results and simulation analysis enabled the optimum adjustment of the hybrid powertrain controller parameters to approach the “ideal” DP controller performance.
ISC has been involved in a project to improve the performance of hybrid powertrains by exploiting information from on-board sensors and communication systems, combining with external systems like GPS and weather forecasting systems. This allows an improvement in the powertrain performance to be achieved by changing the controller design philosophy to optimize range and reduce component degradation in for example batteries and fuel cells. ISC has developed an algorithm based on Machine Learning and Data-Driven techniques to convert data from different systems to use in the hybrid powertrain control and monitoring system. Support Vector Machine modelling and identification methods were developed to adapt the controller in uncertain operating conditions to overcome limits and improve the performance over the more traditional baseline controllers.
Model-based predictive control (MPC) was used for Spark Injection (SI) engines to handle constraints and compensate for actuator, process and sensor delays. Nonlinear models were required to represent the nonlinearities and event-based dynamics inherent to automotive engine systems. Linear Parameter-Varying (LPV) models were found to meet the requirements and were used within the Nonlinear MPC framework to control torque tracking, fuel economy, emissions and drivability. Model-based design procedures were used to validate the algorithms in simulation and to optimise them for real-time implementation, prior to the successful tests on the engine itself.