This book presents a step-by-step guide to engine control system design, providing case studies and a thorough analysis of the modelling process, using model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, it enables engineers to improve performance in both hybrid and non-hybrid vehicles.

Control system improvement is one of the major priorities for engineers seeking to enhance an engine. Often possible on a low budget, substantial improvements can be made through applying cutting-edge methods, such as artificial intelligence when modelling engine control system designs and using MPC. The book presents approaches to control system improvement at mid, low and high levels of control. Beginning with model-in-the-loop hierarchical control design of ported fuel injection SI engines, it focuses on optimal control of both transient and steady state, and also discusses hardware-in-the-loop. Chapters on low level control discuss adaptive model predictive control and adaptive variable functioning, as well as designing fuel injection feedback controllers. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, high-level control methodology is detailed in relation to transient torque control of SI engines.

This comprehensive yet clear guide to control system improvement is an essential read for any engineer working in automotive engineering and engine control system design.