Permanent magnet synchronous motors (PMSMs) are popular in the electric vehicle industry due to their high-power density, large torque-to-inertia ratio, and high reliability. This book presents an improved field-oriented control (FOC) strategy for PMSMs that utilizes optimal proportional-integral (PI) parameters to achieve robust stability, faster dynamic response, and higher efficiency in the flux-weakening region. The book covers the combined design of a PI current regulator and varying switching frequency pulse-width modulation (PWM), along with an improved linear model predictive control (MPC) strategy. Researchers and graduate students in electrical engineering, systems and control, and electric vehicles will find this book useful.

Features:

• Implements evolutionary optimization algorithms to improve PMSM performance.

• Provides coverage of PMSM control design in the flux-weakening region.

• Proposes a modern method of model predictive control to improve the dynamic performance of interior PMSM.

• Studies the dynamic performance of two kinds of PMSMs: surface-mounted and interior permanent magnet types.

• Includes several case studies and illustrative examples with MATLAB®.

This book is aimed at researchers, graduate students, and libraries in electrical engineering with specialization in systems and control and electric vehicles.

Permanent magnet synchronous motors (PMSMs) are popular in the electric vehicle industry due to their high-power density, large torque-to-inertia ratio, and high reliability. This book presents an improved field-oriented control (FOC) strategy for PMSMs that utilizes optimal proportional-integral (PI) parameters to achieve robust stability, faster dynamic response, and higher efficiency in the flux-weakening region. The book covers the combined design of a PI current regulator and varying switching frequency pulse-width modulation (PWM), along with an improved linear model predictive control (MPC) strategy. Researchers and graduate students in electrical engineering, systems and control, and electric vehicles will find this book useful.

Features:

• Implements evolutionary optimization algorithms to improve PMSM performance.

• Provides coverage of PMSM control design in the flux-weakening region.

• Proposes a modern method of model predictive control to improve the dynamic performance of interior PMSM.

• Studies the dynamic performance of two kinds of PMSMs: surface-mounted and interior permanent magnet types.

• Includes several case studies and illustrative examples with MATLAB®.

This book is aimed at researchers, graduate students, and libraries in electrical engineering with specialization in systems and control and electric vehicles.

Permanent magnet synchronous motors (PMSMs) are popular in the electric vehicle industry due to their high-power density, large torque-to-inertia ratio, and high reliability. This book presents an improved field-oriented control (FOC) strategy for PMSMs that utilizes optimal proportional-integral (PI) parameters to achieve robust stability, faster dynamic response, and higher efficiency in the flux-weakening region. The book covers the combined design of a PI current regulator and varying switching frequency pulse-width modulation (PWM), along with an improved linear model predictive control (MPC) strategy. Researchers and graduate students in electrical engineering, systems and control, and electric vehicles will find this book useful.

Features:

• Implements evolutionary optimization algorithms to improve PMSM performance.

• Provides coverage of PMSM control design in the flux-weakening region.

• Proposes a modern method of model predictive control to improve the dynamic performance of interior PMSM.

• Studies the dynamic performance of two kinds of PMSMs: surface-mounted and interior permanent magnet types.

• Includes several case studies and illustrative examples with MATLAB®.

This book is aimed at researchers, graduate students, and libraries in electrical engineering with specialization in systems and control and electric vehicles.