Degradation Control for Electric Vehicle Machines Using Nonlinear Model Predictive Control

Abstract

Electric machines (motors and generators) are over actuated systems. In this paper we show how to exploit this actuation redundancy in order to mitigate machine degradation while simultaneously ensuring that the desired closed loop performance is maintained. We formulate a multi-objective optimization problem with a cost function having terms representing closed loop performance and component degradation for an inverter-fed permanent magnet synchronous motor. Such machines are important as they are widely used as the prime mover of commercial electric vehicles. The resulting optimal control problem is implemented online via a Nonlinear Model Predictive Control (NMPC) scheme. The control framework is validated for standard vehicle drive cycles. Results show that the NMPC scheme allows for better closed loop performance and lower degradation than standard industrial controllers such as the field oriented control method. Hence, this work demonstrates how the remaining useful life of a machine can be increased by appropriate controller design without compromising performance.

Non‐cascade backstepping sliding mode control with three‐order extended state observer for PMSM drive

“…Owing to the high‐power density, high efficiency, and large torque‐to‐inertia ratio, permanent magnet synchronous motors (PMSMs) have been widely used in various fields [1], such as aerospace plane [2], electric vehicle [3], and servo system [4]. Owing to the non‐linearity and strong coupling of PMSM [5], a lot of non‐linear control methods such as fuzzy control [6], sliding mode control (SMC) [7], predictive control [8], active disturbance rejection control [9] etc.…”

“…The proposed control method is strongly robust to acute variations of load and machine parameters. However, the control methods proposed in [3, 4] are cascade controllers, and the speed cannot be directly controlled.…”

Adaptive Nonlinear Control of Unified Model of Fuel Cell, Battery, Ultracapacitor and Induction Motor Based Hybrid Electric Vehicles

“…The adaptive fuzzy logic based controller for energy management of fuel cell and battery based HEVs has been used to control the power and maintain the state of charge of the battery [23]. Furthermore, the model predictive controller has been introduced to improve the battery performance and to avoid degradation of the battery and fuel cell [24]. A robust fuzzy model predictive controller has been proposed for energy management system in fuel cell vehicles [25].…”

Robust position anti-interference control for PMSM servo system with uncertain disturbance

“…In order to overcome this issue, related nonlinear control methods such as the sliding mode variable structure control [5], [6], adaptive control [7], [8], model predictive control [9], [10] active disturbance rejection control (ADRC) [11], feedback linearization control [12] and adaptive backstepping control [20], [21] are widely investigated for solving the above problems. For example, A. T. Nguyen et al [13] has proposed a model reference adaptive control (MRAC) based scheme, including an adaptive compensator and a feedback controller to improve the speed response against unknown external interference.…”