Performance Evaluation of Speed Behaviour of Fuzzy-PI Operated BLDC Motor Drive

Chintawar, Samiksha; Ghodke, Snehal; Khatavkar, V. V.; Alset, Utkarsh; Mehta, Hrishikesh

doi:10.1109/compe53109.2021.9752453

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…[28], [31]- [34] for various electric cars in absence of dedicated and standard driving profile created specifically for EVs, even both WLTP and NeEuDrCy profiles deviate from practical-world driving patterns of EVs.…”

Section: Drive Cycles In Indiamentioning

confidence: 99%

“…According to transition probabilities ('t hu = t ij '), which represent the possibility of switching from one mode ('i') to another ('j'), driving profiles are created by selecting appropriate major-or-micro transportation-based trips by considering various electric driving modes, as shown in Figure 2. The transition probabilities are determined using a variety of statistical approaches, such as "Markov" processes elucidated in [10,11], "Knight's Tour" simulation method described in detailed in [12], factorization-based analysis corresponding to the principal-based component analysis is also explained in detail in [13], fuzzybased logic [31,32,34], and others. The driving profile that most closely resembles the goal driving-based profile is selected/chosen as the final driving profile from the collection of created driving profiles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Electric Vehicle Efficiency Enhancement Through Drive Cycle Optimization: Evaluating its Influence on Electric Range and Longevity

Alset,

Chavan,

Pillewar

et al. 2024

E3S Web Conf.

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The best driving range for electric cars (EVs) is a major difficulty because this parameter is mostly determined by the amount of energy in the battery pack. However, it is crucial to understand that user behavior and particular driving habits have a significant impact on this range. The necessity for a reevaluation of typical drive cycles, which have grown increasingly out of step with changing customer needs, has been highlighted by recent controversy around the stated range statistics by EV manufacturers. Drive cycles must be modified in accordance with the major variances in driving behaviors across various vehicle segments. In addition, the driving cycle and patterns change across different demographic areas in varied countries like India, creating a special difficulty in the development of a new standard driving cycle that can accommodate a wide range of users. Real-world driving patterns must be taken into account in order to effectively anticipate an EV’s range and evaluate the durability and warranty of its battery pack. The careful selection of a very-specific driving-based realistic profile is therefore crucial for the environment surrounding electric vehicles. We explore the complex process of creating driving profiles particularly designed for electric vehicles in this in-depth review article. We examine the subtleties and traits of these profiles to offer light on their importance in the context of EVs. This assessment provides insightful information on how electric car technology is developing as well as the crucial part that driving styles will play in the development of sustainable transportation in the future.

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Section: Drive Cycles In Indiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Electric Vehicle Efficiency Enhancement Through Drive Cycle Optimization: Evaluating its Influence on Electric Range and Longevity

Alset,

Chavan,

Pillewar

et al. 2024

E3S Web Conf.

Self Cite

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“…In [10], the application of PI, FLC, PI + AW controllers in speed control is compared. In [11], the velocity characteristics of fuzzy proportional integral (FPI) controllers based on trigonometric membership functions (trimf ) were evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…From [5][6][7][8][9][10][11], all approaches support fuzzy logic-based controllers, although the performance of FLCs depends on the scaling factors of the FLC inputs and outputs, which can also affect the control system's performance. To overcome these problems, nature-inspired algorithms such as the particle swarm optimized (PSO) algorithm are used to tuning the scaling factors of PID and FLCs to optimize the parameters in the control system.…”

Section: Introductionmentioning

confidence: 99%

Optimizing BLDC motor drive performance using particle swarm algorithm-tuned fuzzy logic controller

Cao

Zhou

2022

SN Appl. Sci.

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A brushless DC (BLDC) motor is synchronous motor with trapezoidal/square wave counter-electromotive force, which is a typical example of highly coupled nonlinear systems. In industrial control, BLDC motor drive usually uses proportional–integral (PI) controller to control the speed, but it is very difficult to adjust the scale factors. In this study, we present a particle swarm algorithm-tuned fuzzy logic-PI (PF-PI) controller applied to the speed control system. The objective of this paper is to optimally tune the PI controller parameters to obtain the best drive response. The scale factors are optimized using particle swarm optimized-PI (P-PI) controller and PF-PI controller. The three performance indicators integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are used to measure the effectiveness of PF-PI controller optimization. The results show that the optimal torque ripple and speed response curves are obtained by using ITAE as the performance indicator. The conclusions demonstrate that the proposed method provides superior dynamic performance for BLDC motor. Highlights In terms of research content, we propose a new PF-PI controller driven control system based on the traditional BLDC speed control system, and the applicability of three performance indicators on the controller is discussed. In terms of research method, we compare the no-load start, variable speed and sudden addition disturbance load start capabilities of P-PI controller and PF-PI controller, and verify the fast and robustness of PF-PI controller. In the research significance, the PI controller structure is improved and the dynamic performance of BLDC speed control system is enhanced.

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Relative Study of Intelligent Control Techniques to Maintain Variable Pitch-Angle of the Wind Turbine

Khatavkar,

Andhale,

Pillewar

et al. 2023

2023 IEEE 8th International Conference for Convergence in Technology (I2CT)

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Performance Evaluation of Speed Behaviour of Fuzzy-PI Operated BLDC Motor Drive

Cited by 3 publications

References 29 publications

A Comprehensive Review of Electric Vehicle Efficiency Enhancement Through Drive Cycle Optimization: Evaluating its Influence on Electric Range and Longevity

A Comprehensive Review of Electric Vehicle Efficiency Enhancement Through Drive Cycle Optimization: Evaluating its Influence on Electric Range and Longevity

Optimizing BLDC motor drive performance using particle swarm algorithm-tuned fuzzy logic controller

Relative Study of Intelligent Control Techniques to Maintain Variable Pitch-Angle of the Wind Turbine

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