Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple—Review

Abstract

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.

A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors

“…The future Magnetless motor, such as induction motor and switched reluctance motor comes with low-cost material. The main issues of the above motors are torque ripple, noises for SRM, and losses in IM for EV applications [11].…”

“…World's population growth, economic expansion, expanding car usage, and urbanization are all contributing to an increased vehicle and E-motor vehicle [10]. EVs provide advantages including being more cost-effective to operate and maintain, as well as better for the environment and human health [11]. The goal of thestudy is to understand, using a survey of real-world electric vehicles, the optimal way to use batteries, DC-DC converters, and motors from the perspective of an application for EVs [12].…”

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

“…15 Building upon this foundation and the motivations discussed earlier, discrete-time sliding mode control (DSMC) schemes have been introduced to enhance the performance of controlled systems, notably BLDC, within the discrete-time domain. 16,17 However, DSMC schemes still exhibit certain limitations: (i) the chattering problem, stemming from high-frequency components in the control signal 18,19 ; (ii) performance dependence on the uncertainty characteristics of the controlled plant under non-matching conditions. 20,21 The foremost consideration in designing control systems, particularly in practical implementations, is to ensure robustness under constraints and uncertainties.…”

“…In contemporary control engineering, ensuring the robustness of a controlled system holds paramount significance, especially in practical applications 15 . Building upon this foundation and the motivations discussed earlier, discrete‐time sliding mode control (DSMC) schemes have been introduced to enhance the performance of controlled systems, notably BLDC, within the discrete‐time domain 16,17 . However, DSMC schemes still exhibit certain limitations: (i) the chattering problem, stemming from high‐frequency components in the control signal 18,19 ; (ii) performance dependence on the uncertainty characteristics of the controlled plant under non‐matching conditions 20,21 …”

“…The main contributions of this work are listed as follows:The introduced equivalent model, DDEM, aims to facilitate online characterization of the dynamic performance of systems characterized by strong nonlinearity and parameter uncertainty. In contrast to traditional DLDM approaches, 5–8,32 our proposed learning law accommodates constraints even when there is no change in the control effort. The development of a controller tailored to address time‐varying behavior and disturbances eliminates the need for state observers, distinguishing it from conventional approaches such as References 10 and 17. This innovative controller capitalizes on nonlinear functions derived from DDEM, allowing for the accommodation of unknown uncertainties and time‐varying parameters. Introducing a distinctive auxiliary control law that enhances the robustness of closed‐loop systems, eliminating the need to compromise between robustness and chattering reduction, as typically encountered in approaches such as References 18,19,26,28.…”