Current Distribution Method of Induction Motor for Electric Vehicle in Whole Speed Range Based on Gaussian Process

Xie, Fang; Hong, Wenjie; Wu, Wenming; Liang, Kaili; Qiu, Chenming

doi:10.1109/access.2019.2953293

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…where C : R n x → R n x ×D denotes the generalized coefficient matrix. The model in (11) contains some types of functions, such as the GP mean and KRR models µ(x) in ( 5). If C(x) is determined such that we can solve the HJB equation ( 7), then the optimal controller u * (x) to f (x) is easily derived.…”

Section: Problem Setting To Unify the Identification And Controlmentioning

confidence: 99%

See 1 more Smart Citation

Kernel-Based Hamilton-Jacobi Equations for Data-Driven Optimal Control: The General Case

Ito

FUJIMOTO

2022

IEICE Trans. Fundamentals

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Recently, control theory using machine learning, which is useful for the control of unknown systems, has attracted significant attention. This study focuses on such a topic with optimal control problems for unknown nonlinear systems. Because optimal controllers are designed based on mathematical models of the systems, it is challenging to obtain models with insufficient knowledge of the systems. Kernel functions are promising for developing data-driven models with limited knowledge. However, the complex forms of such kernel-based models make it difficult to design the optimal controllers. The design corresponds to solving Hamilton-Jacobi (HJ) equations because their solutions provide optimal controllers. Therefore, the aim of this study is to derive certain kernel-based models for which the HJ equations are solved in an exact sense, which is an extended version of the authors' former work. The HJ equations are decomposed into tractable algebraic matrix equations and nonlinear functions. Solving the matrix equations enables us to obtain the optimal controllers of the model. A numerical simulation demonstrates that kernel-based models and controllers are successfully developed.

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Section: Problem Setting To Unify the Identification And Controlmentioning

confidence: 99%

“…Main problem: Find a set F of kernel-based drift term models f (x) defined in (11) such that an exact solution V(x) to the HJB equation ( 7) is acquired.…”

Section: Problem Setting To Unify the Identification And Controlmentioning

confidence: 99%

Kernel-Based Hamilton-Jacobi Equations for Data-Driven Optimal Control: The General Case

Ito

FUJIMOTO

2022

IEICE Trans. Fundamentals

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“…The field weakening is mainly actual for the traction drives and others wide speed range applications. For a traction drive, especially for the electric vehicles, efficiency of induction motors applications depends hardly on the field weakening control [14,15,[25][26][27][28]. So, in the papers [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]29] the field weakening control synthesis comprises two directions.…”

Section: Introductionmentioning

confidence: 99%

Reference Input Signals Formation for Induction Motor Control in Traction Drive

Opeiko

2023

Izv. vysh. ucheb

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The purpose of this work is to build the analytical improved (with resistances estimation) real time computation of the reference inputs for rotor flux and torque in the vector control system of an induction motor of a traction electric drive. The reference inputs must maximize electromagnetic torque in conditions of voltage source instability, particularly in magnetic field weakening mode. The conventional way to control the field weakening mode is to form flux coupling task inversely proportional to the speed or inversely proportional to the square of the speed in second and third zones respectively. Such reference input signals are not able to provide the maximum torque capability over the entire speed range, and the improved torque capability is achieved in different ways. For instance, voltage feedback is useful for the torque capability enhancement in conditions of internal and external perturbations. A wide change in speed with the weakening of the flux reveals the nonlinear properties of an induction electric motor. However, in vector control systems, proportional-integrating (PI) regulators are usually used. Therefore, firstly, linear PI controllers must be robust, and secondly, the reference input signals for flux and torque must guaranty linear, not saturated state of each PI controller. The proposed expressions for calculating reference inputs for induction motor rotor flux and electromagnetic torque as functions of actual rotor speed are the approximate expressions. The estimation of the possible error shows that the error is acceptable. Simulation is performed for the vector control system of an induction motor and taking into account the calculation of the control signal by the microcontroller and the dynamics of the frequency invertor. The simulation of the resulting system validates the effectiveness of the control system using the proposed expressions for the formation of real-time reference input signals for setting the flux and torque.

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“…Also, the motor torque and flux performance have not presented for flux weakening control (FWC) mode. Reference 9 presented the current distribution method based on FOC of the IM for EV in a wide speed range. In this method, the current is optimized to maximize the output motor torque in the FWC region.…”

Section: Introductionmentioning

confidence: 99%

A comparative dynamic analysis between model predictive torque control and field‐oriented torque control of IM drives for electric vehicles

Ahmed

Akl

Rashad

2021

Int Trans Electr Energ Syst

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This article compares two torque control methodologies, model predictive control (MPC), and field‐oriented control (FOC), for induction motor (IM) control over a wide speed range applied to an electric vehicle driving system (EVDS). The control technologies are designed and developed to control torque to achieve targeted traction according to vehicle specifications and the demands of the driver. For both control techniques, the specified electric vehicle (EV) is driven under and above base speed with stator flux control and field weakening control of the IM, respectively. In EVDS, the dynamic modeling of the power plants, that is, vehicle dynamics, Li‐ion battery, IM, and road forces, is considered. The dynamic behavior of the EVDS of a four‐passenger city EV is analyzed and evaluated with urban dynamometer driving schedule (UDDS) driving cycle. From simulations and performance appraisal, the comparison between FOC and MPC is investigated.

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Current Distribution Method of Induction Motor for Electric Vehicle in Whole Speed Range Based on Gaussian Process

Cited by 7 publications

References 30 publications

Kernel-Based Hamilton-Jacobi Equations for Data-Driven Optimal Control: The General Case

Kernel-Based Hamilton-Jacobi Equations for Data-Driven Optimal Control: The General Case

Reference Input Signals Formation for Induction Motor Control in Traction Drive

A comparative dynamic analysis between model predictive torque control and field‐oriented torque control of IM drives for electric vehicles

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