Optimising direct torque control with battery power management for open-end winding induction motor drive in electric vehicles using the light spectrum optimiser algorithm

This paper proposes an efficient light spectrum optimiser (LSO) for enhancing the direct torque control (DTC) strategy for open-end winding induction motor drives used in electric vehicles (EVs). The purpose is to reduce torque and current ripples while balancing the system's power flow and increasing efficiency. The LSO algorithm is utilised to control the optimal switching states of the inverter. By integrating battery power management and LSO, the goal is to achieve more efficient energy utilisation and improved motor performance. By that point, the proposed model has been used as a working model in MATLAB/Simulink, and the execution has been computed based on the available techniques. The proposed method show's the efficiency is high, the torque and current ripples and the systems power flow are balanced compared to existing methods, like wild horse optimiser (WHO) particle swarm optimisation (PSO) and heap-based optimiser (HBO).

Introduction: The growing emphasis on sustainable transportation, driven by climate change awareness, is accelerating the adoption of electric vehicles (EVs). A critical challenge is the precise control of induction motors (IMs) used in EVs. Traditional control methods like Field Oriented Control (FOC) and Direct Torque Control (DTC) suffer from parameter sensitivity and high torque ripple, reducing efficiency. This research proposes a Fuzzy DTC scheme to address these limitations. Objectives: The primary objectives of this research are to develop and implement a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives in Electric Vehicles (EVs), specifically designed to overcome the limitations of conventional DTC methods. This entails achieving a significant reduction in torque ripple, a common issue in traditional DTC, which directly impacts the smoothness and efficiency of the EV's operation. Furthermore, the research aims to enhance the dynamic response of the IM drive system, enabling faster and more precise control of the motor's torque and speed, crucial for the dynamic driving conditions experienced by EVs. Ultimately, the successful implementation of the Fuzzy DTC scheme should lead to an overall improvement in the efficiency and robustness of the IM speed control within the EV system, ensuring reliable and high-performance operation across all driving scenarios, including acceleration, deceleration, and constant speed maintenance. Methods: The methodology employed in this research centers around the development and implementation of a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives. Departing from traditional DTC, which relies on hysteresis bands and a switching table, this approach integrates a Fuzzy Logic Switching Controller (FLSC) to optimize inverter switching decisions. The FLSC takes as inputs the torque error, stator flux error, stator flux angle, and the count of switching updates, providing a more refined control mechanism. A Mamdani fuzzy inference system (FIS) is utilized, employing triangular and trapezoidal membership functions to fuzzify these input variables. The output of the fuzzy controller dictates the switching state, selected from seven possible states represented by crisp triangular membership functions. This fuzzy logic-based approach allows for a more nuanced and adaptive control strategy, enabling the system to respond effectively to the nonlinearities and uncertainties inherent in IM drives. The fuzzy rules, developed based on engineering expertise and practical experience, guide the selection of the optimal switching state. The research leverages simulations using MATLAB/Simulink to model the IM drive system and evaluate the performance of both conventional and Fuzzy DTC schemes under various operating conditions. This allows for a comparative analysis of torque ripple, dynamic response, and overall efficiency, validating the effectiveness of the proposed fuzzy-based control strategy. Results: The simulation results presented in this paper demonstrate the superior performance of the proposed Fuzzy-based Direct Torque Control (DTC) scheme compared to conventional DTC methods for Induction Motor (IM) drives in Electric Vehicles (EVs). Across various operating conditions, including different load and speed combinations, the Fuzzy DTC consistently exhibited a significant reduction in torque ripple. This reduction translates to a smoother and more efficient motor operation, crucial for enhancing the driving experience and overall performance of EVs. Furthermore, the Fuzzy DTC showed improved dynamic response, characterized by lower overshoot and faster settling times. These findings indicate that the fuzzy logic-based control strategy enables more precise and rapid control of the IM's torque and speed, effectively addressing the limitations of traditional DTC. Specifically, the data presented in Table 3 and Figures 18, 19, and 20 highlight the quantifiable improvements in parameters such as torque ripple percentage, slew rate, and overshoot. The comparative analysis consistently favored the Fuzzy DTC scheme, validating its effectiveness in achieving robust and efficient IM speed control under the dynamic operating conditions typical of electric vehicles. Conclusions: This paper has investigated the application of fuzzy based DTC to induction motor (IM) drives in electric vehicles (EVs). The proposed Fuzzy DTC approach addresses the limitations of conventional technique of DTC, including high ripple of torque by integrating fuzzy logic into the control scheme. Simulation results show the proposed Fuzzy DTC effectively achieves precise and robust speed control under various EV operating conditions. The approach optimizes switching decisions based on fuzzy rules, resulting in improved performance compared to traditional DTC methods. The proposed Fuzzy DTC scheme offers reduced torque ripple, improved efficiency, enhanced dynamic performance, and a smoother driving experience.

Introduction: The growing emphasis on sustainable transportation, driven by climate change awareness, is accelerating the adoption of electric vehicles (EVs). A critical challenge is the precise control of induction motors (IMs) used in EVs. Traditional control methods like Field Oriented Control (FOC) and Direct Torque Control (DTC) suffer from parameter sensitivity and high torque ripple, reducing efficiency. This research proposes a Fuzzy DTC scheme to address these limitations. Objectives: The primary objectives of this research are to develop and implement a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives in Electric Vehicles (EVs), specifically designed to overcome the limitations of conventional DTC methods. This entails achieving a significant reduction in torque ripple, a common issue in traditional DTC, which directly impacts the smoothness and efficiency of the EV's operation. Furthermore, the research aims to enhance the dynamic response of the IM drive system, enabling faster and more precise control of the motor's torque and speed, crucial for the dynamic driving conditions experienced by EVs. Ultimately, the successful implementation of the Fuzzy DTC scheme should lead to an overall improvement in the efficiency and robustness of the IM speed control within the EV system, ensuring reliable and high-performance operation across all driving scenarios, including acceleration, deceleration, and constant speed maintenance. Methods: The methodology employed in this research centers around the development and implementation of a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives. Departing from traditional DTC, which relies on hysteresis bands and a switching table, this approach integrates a Fuzzy Logic Switching Controller (FLSC) to optimize inverter switching decisions. The FLSC takes as inputs the torque error, stator flux error, stator flux angle, and the count of switching updates, providing a more refined control mechanism. A Mamdani fuzzy inference system (FIS) is utilized, employing triangular and trapezoidal membership functions to fuzzify these input variables. The output of the fuzzy controller dictates the switching state, selected from seven possible states represented by crisp triangular membership functions. This fuzzy logic-based approach allows for a more nuanced and adaptive control strategy, enabling the system to respond effectively to the nonlinearities and uncertainties inherent in IM drives. The fuzzy rules, developed based on engineering expertise and practical experience, guide the selection of the optimal switching state. The research leverages simulations using MATLAB/Simulink to model the IM drive system and evaluate the performance of both conventional and Fuzzy DTC schemes under various operating conditions. This allows for a comparative analysis of torque ripple, dynamic response, and overall efficiency, validating the effectiveness of the proposed fuzzy-based control strategy. Results: The simulation results presented in this paper demonstrate the superior performance of the proposed Fuzzy-based Direct Torque Control (DTC) scheme compared to conventional DTC methods for Induction Motor (IM) drives in Electric Vehicles (EVs). Across various operating conditions, including different load and speed combinations, the Fuzzy DTC consistently exhibited a significant reduction in torque ripple. This reduction translates to a smoother and more efficient motor operation, crucial for enhancing the driving experience and overall performance of EVs. Furthermore, the Fuzzy DTC showed improved dynamic response, characterized by lower overshoot and faster settling times. These findings indicate that the fuzzy logic-based control strategy enables more precise and rapid control of the IM's torque and speed, effectively addressing the limitations of traditional DTC. Specifically, the data presented in Table 3 and Figures 18, 19, and 20 highlight the quantifiable improvements in parameters such as torque ripple percentage, slew rate, and overshoot. The comparative analysis consistently favored the Fuzzy DTC scheme, validating its effectiveness in achieving robust and efficient IM speed control under the dynamic operating conditions typical of electric vehicles. Conclusions: This paper has investigated the application of fuzzy based DTC to induction motor (IM) drives in electric vehicles (EVs). The proposed Fuzzy DTC approach addresses the limitations of conventional technique of DTC, including high ripple of torque by integrating fuzzy logic into the control scheme. Simulation results show the proposed Fuzzy DTC effectively achieves precise and robust speed control under various EV operating conditions. The approach optimizes switching decisions based on fuzzy rules, resulting in improved performance compared to traditional DTC methods. The proposed Fuzzy DTC scheme offers reduced torque ripple, improved efficiency, enhanced dynamic performance, and a smoother driving experience.

Comparative studies on several direct torque control (DTC) strategies of interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs) are discussed in details, namely basic DTC, DTC combined with space vector modulation (DTC-SVM), and deadbeat DTC (DB-DTC). These DTC strategies are reviewed, meanwhile dynamics and steady-state performance are analyzed and compared. Simulations of a 20kW IPMSM for EVs are carried out for comparison studies including: torque and stator flux ripple, machine parameter sensitivity, computational complexity and stator current total harmonic distortion. The results can be used as guidance for application of IPMSM to EVs and others.

Direct torque control(DTC) scheme is one of the simplest and efficient vector control method. This paper aims to carry out a comparative study of two control methods of DTC. One is conventional DTC scheme based on hysteresis controllers and lookup table. Other method relies on space vector modulation(SVM) algorithm. Lookup table DTC method suffers from high ripples in torque, flux and current as well as varying switching frequency. On the other hand, SVM based DTC produces comparably low torque, flux and current ripples and switching frequency is maintained constant. Simulation of both DTC schemes is carried using MATLAB/Simulink software and the ripple content is estimated.

Comparative Study on Direct Torque Control of Interior Permanent Magnet Synchronous Motor for Electric Vehicle

In this work, a direct torque and flux fuzzy control (DTCF) scheme for voltage-source pulse width modulation inverter-fed induction motor drive is designed to replace the classical direct torque control (DTC) based on hysteresis-band comparators, because of the undesirable torque and current distortion caused by the basic DTC. A substantial reduction of these distortions can be obtained using the technique of DTCF at different sampling periods. In this way, a comparison study between these two techniques is carried out to demonstrate the effect of different sampling periods on the torque and current ripple. Simulation results validate the proposed method and support its ability to follow desired dynamics.

In this paper, a modified direct torque control (DTC) scheme for permanent magnet synchronous motor (PMSM) is investigated, which enables low torque ripple by using an improved voltage vector selection strategy instead of switching table used in conventional DTC. Based on the control of stator flux, torque angle and torque, voltage vector selection strategy of PMSM DTC drive is proposed. In the proposed voltage vector selection strategy, the applied voltage vector is determined according to outputs of hysteresis comparators for stator flux and torque, angular position of stator flux and torque angle, which is finally synthesized by space vector modulation (SVM). Modeling and experimental results for an interior PMSM used in Honda Civic 06My Hybrid electrical vehicle are given. Simulation and experimental results show torque ripple is reduced and the total harmonics of stator current is decreased when compared those of conventional DTC. And a fixed switching frequency is obtained with the help of SVM. In addition, the proposed DTC doesn’t need any additional PI controller, which maintains the simplicity in conventional DTC. DOI: http://dx.doi.org/10.11591/ijpeds.v1i2.141 Keywords : direct torque control, permanent magnet synchronous motor, electrical vehicle, torque ripple, switching frequency

The interior permanent magnet synchronous motor (PMSM) can offer many advantages, including high power-to-weight ratio, high efficiency, rugged construction, low cogging torque and the capability of reluctance torque, so it is widely used in electric vehicle (EV). Two control schemes, namely field oriented control (FOC) and direct torque control (DTC) are used in PMSM drive. In order to decrease current and torque ripple and fix switching frequency, an improved DTC scheme based on the control of stator flux, torque angle and torque was proposed, which used voltage vector selection strategy and the technology of space vector modulation (SVM) to generate the applied voltage vector instead of switching table. And this paper compared these three control schemes based on a 15-kW interior PMSM used in Honda Civic 06My hybrid electrical vehicle. Experimental results show for the FOC using the hysteresis current control, due to the lower sampling period, stator current is more sinusoidal. But it needs the continuous rotor position information and the switching frequency of the VSI is not constant. For the DTC using switching table, current ripple is higher and the switching frequency of the VSI is also not constant. But it does not need the rotor position information except for the initial rotor position. Compared with switching table, the proposed DTC can decrease current and torque ripple and fix switching frequency.

This paper presents a nonlinear optimal direct torque control (DTC) scheme of interior permanent magnet synchronous motors (IPMSMs) based on an offline approximation approach for electric vehicle (EV) applications. First, the DTC problem is reformulated in the stationary reference frame in order to avoid estimating the stator flux angle, which the previous DTC schemes in the rotating stator reference frame require. Thus, the proposed DTC method eliminates the Park's transformation, and consequently, it reduces the computational efforts. Particularly, since the estimated stator flux angle is not accurate in low speed range, the proposed method that does not need this information can significantly improve the control performance. Moreover, a nonlinear optimal DTC algorithm is proposed to deal with the nonlinearity of the IPMSM drive system. In this paper, a simple offline θ- D approximation technique is utilized to appropriately determine the controller gains. Via an IPMSM test bed with a TI TMS320F28335 DSP, the experimental results demonstrate the feasibility of the proposed DTC method by accomplishing better control performances (e.g., more stable in low speed region, much smaller speed and torque ripples, and faster dynamic responses) compared to the conventional proportional-integral DTC scheme under various scenarios with the existence of parameter uncertainties.

This paper presents a demonstration of the maximum torque per ampere (MTPA) based direct torque control (DTC) scheme for improving the energy efficiency as well as dynamic response of induction motor drives used in electric vehicles. The flux reference for MTPA condition was first formulated in the synchronous frame of reference and then it was converted into stationary frame of reference for implementing the DTC scheme. The MTPA controller was modified to incorporate the core losses and the DTC scheme was tested for varying torque reference. The results show that the MTPA based DTC scheme draws lesser current from the inverter than the traditional DTC scheme especially at light loads. This can lead to energy savings in electric vehicles in which the requirement of torque varies widely. An electric vehicle (EV) was modelled in Matlab-Simulink software and simulated to compare the performance of the MTPA based DTC scheme and the traditional DTC schemes for FTP75 drive cycle. The simulation results show that the MTPA based DTC controlled induction motor drives provides significant energy savings in electric vehicles for the specified drive cycle. The MTPA based DTC scheme was also experimentally verified on a laboratory prototype.

A novel Maximum Torque Per Ampere (MTPA) based Direct Torque Control (DTC) scheme is proposed in this paper, for a two-level inverter fed Induction Motor (IM) drive, suited for Electric Vehicle (EV) applications. In the EV applications, reference torque is slowly varied, which opens up the option to control the flux reference in order to incorporate MTPA condition, facilitating improvement in IM efficiency. Firstly, flux reference changes needed to realize MTPA condition are derived in synchronous reference frame and then appropriately translated to the stationary reference frame so as to be adopted for DTC for controlling both torque and flux in the IM. With this relation, an MTPA controller is designed along with a feedback-linearizing controller. The performance of the IM with the proposed MTPA based DTC is tested initially for smooth varying torque reference and results are presented that clearly demonstrates a much lower current drawn from the inverter drive when compared with the use of conventional DTC scheme. This may lead to improved energy savings. To ascertain the benefits obtainable with the envisaged MTPA based DTC scheme for EV applications, the motor drive is tested and experimental results are presented with the European Union (EU) urban drive cycle data.

In the proposed work, a modified look-up table-based direct torque control (DTC) scheme is reported for a neutral point-clamped three-level inverter-fed interior permanent magnet synchronous motor drive. In the classical DTC scheme, there are significant torque and flux ripples in the drive due to low number of available switching states. To reduce the oscillations in torque and flux, a three-level inverter-operated IPMSM drive is discussed. In a three-level inverter, the increased count of active voltage vectors is results in more sinusoidal output waveforms. Moreover, stator flux plane has 12 sectors in the proposed work unlike classical DTC. Besides, the higher level of flux and torque hysteresis comparators is implemented. DC link capacitor voltage balancing scheme is also instigated to minimize voltage stress on semiconductor devices, improve stator current THD and increased capacitor life. Based on the sector information, flux and torque hysteresis output and capacitor voltage balance logic, an improved LUT is established and explained for a three-level DTC scheme. Consequently, improved stator current (THD) and decrement in torque and flux ripples are observed. To make the system robust, model reference adaptive control (MRAC) based sensorless speed estimation algorithm is realized. The proposed DTC scheme is compared with a two-level inverter-fed and a three-level-inverter-fed DTC scheme without DC link voltage balance strategy. To carry out the analysis, MATLAB/Simulink environment is utilized. Moreover, to validate the simulation results, experimental analysis of the proposed technique is carried out using dSPACE1104.

Electric traction drive prefers direct torque control due to its simplicity and easy implementation on permanent magnet machines (PMMs). Variable switching frequency, as well as more torque and flux ripples, are the key challenges of conventional direct torque control. A modified switching table-based direct torque control has been widely adapted for controlling the PMM drives. Artificial intelligent-based switching table substitutes the switching table and hysteresis comparator provides a significant reduction in current harmonic distortion, torque, and flux ripple, which shows a greater advantage in speed control for smart electric vehicles. In this chapter, artificial intelligence-based multisector direct torque control is analyzed for a suitable voltage vector selection to minimize torque and stator flux ripple. To demonstrate, a comparison of the intended switching tables shows the virtues of each switching table on the performance of the multisector direct torque control strategy. This premises on the theory of keeping the divergence between the commanded torque and the calculated torque as small as possible and does not provide information on the conduction time mode of three-phase switching. It adapts changes in the three phase-current waveform to keep electromagnetic torque consistent, eliminating the commutation torque ripple that would have occurred with conventional direct torque control (CDTC). Simulation results are taken in MATLAB®/Simulink®, and it is observed that the PMM ripples are reduced, particularly at high rotational speeds.

Among all control methods for induction motor drives, Direct Torque Control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors. The DTC scheme is characterized by the absence of PI regulators, coordinate transformations, current regulators and PWM signals generators. In spite of its simplicity, DTC allows a good torque control in steady state and transient operating conditions to be obtained. However, the presence of hysterics controllers for flux and torque could determine torque and current ripple and variable switching frequency operation for the voltage source inverter. This paper is aimed to analyze DTC principles, and the problems related to its implementation, especially the torque ripple and the possible improvements to reduce this torque ripple by using a proposed fuzzy based duty cycle controller. The effectiveness of the duty ratio method was verified by simulation using Matlab/Simulink software package. The results are compared with that of the traditional DTC models.