An Energy Saving MTPA-Based Model Predictive Control Strategy for PMSM in Electric Vehicles Under Variable Load Conditions

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Detalles Bibliográficos
Publicado en:	Computation vol. 13, no. 10 (2025), p. 231-250
Autor principal:	Gao Lihua
Otros Autores:	Lv Xiaodong, Ma, Kai, Shi Zhihan
Publicado:	MDPI AG
Materias:	Load Electric vehicles Accuracy Torque Control algorithms Cost function Orthogonal functions Optimization Transportation systems Predictive control Basis functions Synchronous motors Variables Energy efficiency Laguerre functions Renewable energy Optimal control Real time Energy consumption Permanent magnets Energy conservation
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems.
ISSN:	2079-3197
DOI:	10.3390/computation13100231
Fuente:	Advanced Technologies & Aerospace Database