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Edité par Taylor & Francis Ltd, London, 2025
ISBN 10 : 1032346159 ISBN 13 : 9781032346151
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC 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 by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Ajouter au panierPaperback. Etat : Brand New. 192 pages. 9.00x6.00x9.02 inches. In Stock.
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Edité par Taylor & Francis Ltd, London, 2025
ISBN 10 : 1032346159 ISBN 13 : 9781032346151
Langue: anglais
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EUR 79,53
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Ajouter au panierPaperback. Etat : new. Paperback. This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC 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 by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Edité par Taylor & Francis Ltd, London, 2025
ISBN 10 : 1032346159 ISBN 13 : 9781032346151
Langue: anglais
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
EUR 77,65
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Ajouter au panierPaperback. Etat : new. Paperback. This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC 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 by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 138,41
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Ajouter au panierHardcover. Etat : Brand New. 224 pages. 9.00x6.00x0.63 inches. In Stock.
Edité par Taylor & Francis Group, 2023
ISBN 10 : 1032346124 ISBN 13 : 9781032346120
Langue: anglais
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Vendeur : moluna, Greven, Allemagne
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Amir-Mohammad Shamekhi received his PhD in Automobile Control from K. N. Toosi University of Technology, Tehran, Iran, in 2021, and was chosen as the superior researcher of the Department of Mechanical Engineering. His works concern Cont.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 192 pages. 9.00x6.00 inches. In Stock. This item is printed on demand.
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Ajouter au panierGebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Amir-Mohammad Shamekhi received his PhD in Automobile Control from K. N. Toosi University of Technology, Tehran, Iran, in 2021, and was chosen as the superior researcher of the Department of Mechanical Engineering. His works concern Cont.
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 110,14
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Ajouter au panierBuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.