# Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications

^{1}

^{2}

^{*}

## Abstract

**:**

_{b}and H

_{c}). In this case, the fault diagnosis allows for the correction and reconstruction of the signal in order to compel the system to work despite the presence of a fault. Indeed, several robust control systems are used within the work to regulate the speed of the motor properly, such as control via fuzzy logic and control via a neural network. This paper presents three BLDC control configurations for EVs, PID, fuzzy logic (FL), and an artificial neural network (ANN), discusses the pros and cons, and develops corresponding mathematical models to enhance a fault-tolerant control strategy which is analyzed and studied using MATLAB-based simulations (by discussing the two cases, the steady state and the transient state), allowing for a novel design based on the analytical models developed. The results obtained from the simulation of this system improved the speed controlled by the neural network compared to the fuzzy logic controller. At the same time, the sensor failure had no effect on the system’s operation due to the efficiency of the FTC control.

## 1. Introduction

## 2. Electrical Traction System Design and Mathematical Model

_{e}is the coefficient of the back EMF of one phase and $\omega $ is angular speed of the rotor.

## 3. Ideal Hall Signal and Hall Sensor (HS) Fault Types

_{a}, H

_{b}, and H

_{c}[26]. As indicated in Figure 2, the rotor locations are split into six sectors by a distinct Hall signal with a $\left(\frac{\pi}{3}\right)$ rad interval angle [27].

_{i}edge signal (i = a, b, c) is represented by E

_{i}:

#### 3.1. Fault Detection

_{abc}. offer four sectors instead of three [28]. In two sectors, inappropriate commutations occur and no phase is energized in the third sector, in contrast to the customary operation control [13].

#### 3.2. Proposed Fault Diagnosis Method

_{a}sensor during counterclockwise rotor rotation without a loss of generality [29]. The many causes of sensor failure are summarized in seven instances based on the sector in which the failure occurs. These cases are provided in Table 4.

_{a}in a counterclockwise rotation:

_{b}and H

_{c}and can be represented as follows:

_{a}are quickly noticed in the clockwise direction if they occur in sectors 1, 3, 4, or 6 but not in sectors 2 or 5.

#### 3.3. Signal Reconstruction

_{a}fails (corresponding to 1 or 0).

_{b}and H

_{c}sensors can provide accurate information about sectors 3 (H

_{b}= 1 and H

_{c}= 1) and 6 (H

_{b}= 0 and H

_{c}= 0) but provide the same information about sectors 1 and 2, in which H

_{b}= 1 and H

_{c}= 0, and both sectors 4 and 5, in which H

_{b}= 0 and H

_{c}= 1.

_{a}, the procedure of reconstructing the sectors must be able to distinguish between sectors 1 and 2 and between sectors 4 and 5.

_{a}in a counterclockwise rotation is as follows:

_{p}represents the previous sector, τ

_{p}is the duration of the previous sector, and t

_{c}is the last switching instant. If H

_{a}is faulty, the previous sector and its duration are used to deactivate sectors 1 and 4. The sector duration is measured using a timer whose value is precise since the transitions corresponding to sectors 3 and 6 are performed by H

_{b}and H

_{c}, which are functioning properly [31].

_{a}’, H

_{b}’, and H

_{c}’) consists of the signals built by the proposed method, and the second type (S, H

_{a}, H

_{b}, and H

_{c}) consists of the actual signals [14]. This method can correct the defect if two Halls fail. We have used fault detection and signal reconstruction for each Hall sensor (H

_{abc}). Each sensor is debugged individually, knowing that the segment’s signal must be added if two Halls fail within the data set.

## 4. BLDC Motor Control Architecture

#### 4.1. Proportional–Integral–Derivative Controller (PID)

_{p}= 0.15 is the proportional gain, K

_{i}= 48 is the integral gain, and K

_{d}= 0.001 is the derivative gain.

_{p}) tuned by gradually increasing its value and observing the system’s response. This allows us to strike a balance between the response speed and stability. Next, the integral gain (K

_{i}) is then introduced to eliminate steady-state error. Finally, the derivative gain (K

_{d}) is employed to dampen overshoot and oscillations in the system. The iterative tuning process requires evaluating the system’s behavior and adjusting the gains accordingly.

#### 4.2. Fuzzy Logic Controller

#### 4.3. Neural Network Controller

## 5. Discussion of Simulation Results

_{a}, H

_{b}, and H

_{c}, the sector created by the standard control algorithm, the sector reconstructed by the proposed approach, the currents in the three phases of the BLDC motor, and the rotor velocity.

_{a}; then, to improve the visualization of the defect, we increased the curves obtained from 1.95 s to 2.05 s, as indicated in the figures below.

#### 5.1. Signal Faults and Reconstructed Sector

- A.
- Case 1: In a Steady State:

_{a}fails in the midst of sector 1, and H

_{a}flips from 1 to 0 and remains there. The problem is discovered at the beginning of sector 6 as expected, and there are incorrect commutations in sector 5 and the remainder of sector 1. In Figure 12, we note that after the fault of the H

_{a}sensor and sector, the proposed FTC was activated and generated the new sector, S’ and H

_{a}’.

_{a}does not perform any transition at the end of sector 1, and its value remains constant at 1. The defect is identified at the start of sector 3 as expected, and there are incorrect commutations in sector 2, finally generating the new sector, S’ and H

_{a}’.

_{a}signal changes from 0 to 1 in the middle of sector 2. As expected, the breakdown is identified promptly, and no incorrect commutation occurs in any sector, with no effect on the operation of the motor, which finally generates the new sector, S’ and H

_{a}’, shortly after the fault occurrs.

_{a}from the respective sectors. The results show that the suggested method provides a satisfactory startup in all cases and can be applied to operate the BLDC motor correctly, even when a Hall effect sensor fails.

- B.
- Case 2: In the Transient State:

_{a}in sector 4; this failure is caught when sector 5 is converted into sector 0.

_{a}sensor and sector, the proposed FTC in the transient state case was activated and generated the new sector, S’ and H

_{a}’.

#### 5.2. Efficiency for the Proposed Speed Control with Fault-Tolerant Control

#### 5.3. Comparison of Different Control Strategies

## 6. Conclusions

_{a}, H

_{b}, and H

_{c}signals if the rotor rotates. The control focuses only on the Hall effect sensors used. To ensure operational continuity, various speed controllers, including fuzzy logic and neural network controllers, were adopted and tested. According to the simulation results, the fuzzy logic controller recovered its operation faster than the PID controller. Moreover, the neural network controller proved to be more effective than the other two types of controllers. This work also shows that achieving speed control through the use of a neural network with FTC is the best control strategy for the application of a BLDC motor. With this control strategy, the EVs’ performances were improved as the vehicle speed performances became more important, proving the vehicles’ total profitability. While our current research primarily focuses on the theoretical aspects of fault-tolerant control strategies, we recognize the importance of conducting extensive experimental studies as part of future work.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

Abbreviations | |

EVs | Electric vehicles |

BLDCM | Brushless DC motor |

BLDC | Brushless DC |

EMF | Electromotive force |

PMSM | Permanent magnet synchronous motor |

PID | Proportional–integral–derivative controller |

FLC | Fuzzy logic control |

ANN | Neural network control |

FTC | Fault-tolerant control |

List of symbols | |

K_{p}, K_{i}, K_{d} | Proportional, integral, and derivative gains |

V_{a},V_{b},V_{c} | Phase voltages a,b,c |

i_{a},i_{b},i_{c} | Phase currents a,b,c |

ω | Rotor speed |

J | Moment of inertia |

Q_{1}–Q_{6} | Switching states |

k_{e} | Coefficient of the electromotive force |

L | Inductance |

B | Coefficient of friction |

$f\left({\theta}_{e}\right)$ | Functions depending on rotor position |

T_{e} | Electromagnetic torque |

T_{r} | Load torque |

S | Sector |

S’ | Reconstruction sector |

## References

- Primiceri, P.; Visconti, P.; Melpignano, A.; Colleoni, G.; Vilei, A. Hardware and software solution developed in arm MBED environment for driving and controlling DC brushless motors based on ST X-Nucleo development boards. Int. J. Smart Sens. Intell. Syst.
**2016**, 9, 1534–1562. [Google Scholar] [CrossRef] [Green Version] - Singh, K.V.; Bansal, H.O.; Singh, D. A comprehensive review on hybrid electric vehicles: Architectures and components. J. Mod. Transp.
**2019**, 27, 77–107. [Google Scholar] [CrossRef] [Green Version] - Eldho Aliasand, A.; Josh, F.T. Selection of Motor foran Electric Vehicle: A Review. Mater. Today Proc.
**2020**, 24, 1804–1815. [Google Scholar] [CrossRef] - Yildirim, M.; Polat, M.; Kurum, H. A survey on comparison of electric motor types and drives used for electric vehicles. In Proceedings of the 16th International Power Electronics and Motion Control Conference and Exposition, Antalya, Turkey, 21–24 September 2014; pp. 218–223. [Google Scholar] [CrossRef]
- Baba, M.A.; Naoui, M.; Cherkaoui, M. Modeling and Simulation of a BLDC Motor Speed Control in Electric Vehicles. Int. Conf. Digit. Technol. Appl.
**2023**, 1, 883–895. [Google Scholar] [CrossRef] - Goswami, R.; Joshi, D. Performance Review of Fuzzy Logic Based Controllers Employed in Brushless DC Motor. Procedia Comput. Sci.
**2018**, 132, 623–631. [Google Scholar] [CrossRef] - Kristiyono, R. Wiyono Autotuning fuzzy PID controller for speed control of BLDC motor. J. Robot. Control
**2021**, 2, 400–407. [Google Scholar] [CrossRef] - Ramírez-Cárdenas, O.-D.; Trujillo-Romero, F. Sensorless Speed Tracking of a Brushless DC Motor Using a Neural Network. Math. Comput. Appl.
**2020**, 25, 57. [Google Scholar] [CrossRef] - Zhang, R.; Gao, L. The Brushless DC motor control system Based on neural network fuzzy PID control of power electronics technology. Optik
**2022**, 271, 169879. [Google Scholar] [CrossRef] - Gamazo-Real, J.C.; Vázquez-Sánchez, E.; Gómez-Gil, J. Position and speed control of brushless dc motors using sensorless techniques and application trends. Sensors
**2010**, 10, 6901–6947. [Google Scholar] [CrossRef] [Green Version] - Vanchinathan, K.; Valluvan, K.T.R.; Gnanavel, C.; Gokul, C.; Renold, R.A. An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances. Int. Trans. Electr. Energy Syst.
**2021**, 31, e13251. [Google Scholar] [CrossRef] - Naoui, M.; Flah, A.; Sbita, L.; Ben Hamed, M.; Azar, A.T. Intelligent Control System for Hybrid Electric Vehicle with Autonomous Charging. In Artificial Intelligence for Robotics and Autonomous Systems Applications; Azar, A.T., Koubaa, A., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 405–437. ISBN 978-3-031-28715-2. [Google Scholar]
- Mousmi, A.; Abbou, A.; El Houm, Y. Binary Diagnosis of Hall Effect Sensors in Brushless DC Motor Drives. IEEE Trans. Power Electron.
**2020**, 35, 3859–3868. [Google Scholar] [CrossRef] - Zhang, Q.; Feng, M. Fast Fault Diagnosis Method for Hall Sensors in Brushless DC Motor Drives. IEEE Trans. Power Electron.
**2019**, 34, 2585–2596. [Google Scholar] [CrossRef] - Ebadpour, M.; Amiri, N.; Jatskevich, J. Fast Fault-Tolerant Control for Improved Dynamic Performance of Hall-Sensor-Controlled Brushless DC Motor Drives. IEEE Trans. Power Electron.
**2021**, 36, 14051–14061. [Google Scholar] [CrossRef] - Zhao, Y.; Huang, W.; Yang, J. Fault diagnosis of low-cost hall-effect sensors used in controlling permanent magnet synchronous motor. In Proceedings of the 19th International Conference on Electrical Machines and Systems (ICEMS), Chiba, Japan, 13–16 November 2017; pp. 2–6. [Google Scholar]
- Gayatri Sarman, K.V.S.H.; Madhu, T.; Mallikharjuna Prasad, A. Fault diagnosis of BLDC drive using advanced adaptive network-based fuzzy inference system. Soft Comput.
**2021**, 25, 12759–12774. [Google Scholar] [CrossRef] - Shifat, T.A.; Hur, J.W. EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal. J. Mech. Sci. Technol.
**2020**, 34, 3981–3990. [Google Scholar] [CrossRef] - Kumar, P.H.; Somasekhar, V.T. An Enhanced Fault-Tolerant and Autoreconfigurable BLDC Motor Drive for Electric Vehicle Applications. IEEE J. Emerg. Sel. Top. Ind. Electron.
**2022**, 4, 368–380. [Google Scholar] [CrossRef] - Mohamed, N.; Aymen, F.; Mouna, B.H.; Lassaad, S. Modeling and simulation of vector control for a Permanent Magnet Synchronous Motor in electric vehicle. In Proceedings of the 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Alkhobar, Saudi Arabia, 6–8 December 2021; pp. 1–5. [Google Scholar]
- Obed, A.A.; Kadhim, A.K. Speed and Current Limiting Control Strategies for BLDC Motor Drive System: A Comparative Study. Int. J. Adv. Eng. Res. Sci.
**2018**, 5, 119–130. [Google Scholar] [CrossRef] - Mondal, S.; Mitra, A.; Chattopadhyay, M. Mathematical modeling and simulation of Brushless DC motor with ideal Back EMF for a precision speed control. In Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 5–7 March 2015; pp. 15–19. [Google Scholar] [CrossRef]
- Krause, P.C.; Wasynczuk, O.; Sudhoff, S.D. Brushless dc Motor Drives. In Analysis of Electric Machinery and Drive Systemsl; Wiley-IEEE Press: Piscataway, NJ, USA, 2010. [Google Scholar] [CrossRef]
- Tibor, B.; Fedak, V.; Ďurovský, F. Modeling and simulation of the BLDC motor in MATLAB GUI. In Proceedings of the IEEE International Symposium on Industrial Electronics, Gdansk, Poland, 27–30 June 2011; pp. 1403–1407. [Google Scholar] [CrossRef]
- Zhang, X.; Zhao, Y.; Lin, H.; Riaz, S.; Elahi, H. Real-time fault diagnosis and fault-tolerant control strategy for hall sensors in permanent magnet brushless dc motor drives. Electronics
**2021**, 10, 1268. [Google Scholar] [CrossRef] - Dong, L.; Jatskevich, J.; Huang, Y.; Chapariha, M.; Liu, J. Fault diagnosis and signal reconstruction of hall sensors in brushless permanent magnet motor drives. IEEE Trans. Energy Convers.
**2016**, 31, 118–131. [Google Scholar] [CrossRef] - Aqil, M.; Hur, J. A Direct Redundancy Approach to Fault-Tolerant Control of BLDC Motor with a Damaged Hall-Effect Sensor. IEEE Trans. Power Electron.
**2020**, 35, 1732–1741. [Google Scholar] [CrossRef] - Tashakori, A.; Ektesabi, M. Position sensors fault tolerant control system in BLDC motors. Eng. Lett.
**2014**, 22, 39–46. [Google Scholar] - Aqil, M.; Hur, J. Multiple sensor fault detection algorithm for fault tolerant control of bldc motor. Electronics
**2021**, 10, 1038. [Google Scholar] [CrossRef] - Dong, L.; Huang, Y.; Jatskevich, J.; Liu, J. Improved Fault-Tolerant Control for Brushless Permanent Magnet Motor Drives with Defective Hall Sensors. IEEE Trans. Energy Convers.
**2016**, 31, 789–799. [Google Scholar] [CrossRef] - Magistrale, L. Sensorless Brushless DC Motors: Development and comparison of different fault tolerant control algorithms. 2018; 2005, (Doctoral dissertation, Politecnico di Torino). [Google Scholar]
- Mousa, M.E.; Ebrahim, M.A.; Hassan, M.A.M. Stabilizing and Swinging-Up the Inverted Pendulum Using PI and PID Controllers Based on Reduced Linear Quadratic Regulator Tuned by PSO. Int. J. Syst. Dyn. Appl.
**2015**, 4, 52–69. [Google Scholar] [CrossRef] - Sushita, K. Performance of BLDC motor with PI, PID and Fuzzy controller and its Comparative Analysis. Eur. J. Mol. Clin. Med.
**2021**, 25, 219–228. [Google Scholar] - Singh, G.; Kour, V.; Singh, L. Design and Performance Analysis of PID Controller for Automatic Generation Control of an Autonomous Power System. Int. J. Eng. Res. Technol.
**2020**, 7, 2520–2524. [Google Scholar] - Kilic, E.; Yilmaz, S.; Ozcalik, H.R.; Sit, S. A comparative analysis of FLC and ANFIS controller for vector controlled induction motor drive. In Proceedings of the Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), 2015 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2015 Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), Side, Turkey, 2–4 September 2015; pp. 102–106. [Google Scholar] [CrossRef]
- Mishra, S.; Anurag, M.; Tomer, S. Speed Control of PMSM Drives by Using Neural Network Controller. Adv. Electron. Electr.
**2014**, 4, 353–360. [Google Scholar] - Huang, C.; Lei, F.; Han, X.; Zhang, Z. Determination of modeling parameters for a brushless DC motor that satisfies the power performance of an electric vehicle. Meas. Control
**2019**, 52, 765–774. [Google Scholar] [CrossRef] [Green Version]

**Figure 20.**Speed of the BLDC motor according to the different controllers used (PID, FLC, and ANN) in the presence of faults. (

**a**): speed, (

**b**) zoom B, and (

**c**) zoom A.

**Figure 21.**Current phase curves of different controllers (PID, FLC, and ANN) in the presence of faults.

**Figure 22.**The motor’s electromagnetic torque results for the three control cases (FLC, PID, and ANN).

${\mathit{\theta}}_{\mathit{e}}$ | H_{a} | H_{b} | H_{c} |
---|---|---|---|

$0<{\theta}_{e}<30$ | 1 | 0 | 0 |

$30<{\theta}_{e}<90$ | 1 | 1 | 0 |

$90<{\theta}_{e}<150$ | 0 | 1 | 0 |

$150<{\theta}_{e}<210$ | 0 | 1 | 1 |

$210<{\theta}_{e}<270$ | 0 | 0 | 1 |

$270<{\theta}_{e}<330$ | 1 | 0 | 1 |

$330<{\theta}_{e}<360$ | 1 | 0 | 0 |

H_{a} | H_{b} | H_{c} | Sector | Active Switches | Stator Magnetic Axis |
---|---|---|---|---|---|

1 | 1 | 0 | 1 | Q_{3} Q_{6} | V_{2} |

0 | 1 | 0 | 2 | Q_{3} Q_{2} | V_{3} |

0 | 1 | 1 | 3 | Q_{5} Q_{2} | V_{4} |

0 | 0 | 1 | 4 | Q_{5} Q_{4} | V_{5} |

1 | 0 | 1 | 5 | Q_{1} Q_{4} | V_{6} |

1 | 0 | 0 | 6 | Q_{1} S_{6} | V_{1} |

**Table 3.**States of Hall effect sensors and active switches in operation with faults in “H

_{a}= 0” and “H

_{a}= 1”.

H_{a} = 0 | |||||

H_{a} | H_{b} | H_{c} | Sector | Active Switches | Stator Magnetic Axis |

0 | 1 | 0 | 2 | Q_{3} Q_{2} | V_{3} |

0 | 1 | 0 | |||

0 | 1 | 1 | 3 | Q_{5} Q_{2} | V_{4} |

0 | 0 | 1 | 4 | Q_{5} Q_{4} | V_{5} |

0 | 0 | 1 | |||

0 | 0 | 0 | 0 | No | 0 |

H_{a} = 1 | |||||

H_{a} | H_{b} | H_{c} | Sector | Active Switches | Stator Magnetic Axis |

1 | 1 | 0 | 1 | Q_{3} Q_{6} | V_{2} |

1 | 1 | 0 | |||

1 | 1 | 1 | 0 | No one | 0 |

1 | 0 | 1 | 5 | Q_{1} Q_{4} | V_{6} |

1 | 0 | 1 | |||

1 | 0 | 0 | 6 | Q_{1} Q_{6} | V_{1} |

H_{a} = 0 | |||

Cases | Failure Sector | Sector Switches From | |

Case 1 | 6 in (from 0° to 30°) | From 6 to 0 | $\overline{{H}_{a}}\xb7\overline{{H}_{b}}\xb7\overline{{H}_{c}}=1$ |

Case 2 | 1 in (30–90°) | From 1 to 2 | $\overline{{H}_{a}}\xb7{H}_{b}\xb7\overline{{H}_{c}}=1$ |

Case 3 | 5 in (270–330°) | From 5 to 6 | $\overline{{H}_{a}}\xb7\overline{{H}_{b}}\xb7{H}_{c}=1$ |

Case 4 | 6 in (from 330° to 360°) | From 6 to 0 | $\overline{{H}_{a}}\xb7\overline{{H}_{b}}\xb7\overline{{H}_{c}}=1$ |

H_{a} = 1 | |||

Cases | Failure Sector | Sector Switches from | |

Case 5 | 2 in (from 90° to 150°) | From 2 to 1 | ${H}_{a}\xb7{H}_{b}\xb7\overline{{H}_{c}}=1$ |

Case 6 | 3 in (150–210°) | From 3 to 0 | ${H}_{a}\xb7{H}_{b}\xb7{H}_{c}=1$ |

Case 7 | 4 in (210–270°) | From 4 to 5 | ${H}_{a}\xb7\overline{{H}_{b}}\xb7{H}_{c}=1$ |

E/CE | NB | NM | NS | Z | PS | PM | PB |
---|---|---|---|---|---|---|---|

NB | NB | NB | NB | NB | NM | NS | Z |

NM | NB | NB | NB | NM | NS | Z | PS |

NS | NB | NB | NM | NS | Z | PS | PM |

Z | NB | NM | NS | Z | PS | PM | PB |

PS | NM | NS | Z | PS | PM | PB | PB |

PM | NS | Z | PS | PM | PB | PB | PB |

PB | Z | PS | PM | PB | PB | PB | PB |

Parameter | Value |
---|---|

Input elements | 3 |

Output element | 1 |

Training | 70% |

Validation | 15% |

Testing | 15% |

Number of Hidden Neurons | 10 |

Training Algorithm | Levenberg–Marquardt |

Epochs | 5000 |

Parameter | Value |
---|---|

Base velocity | 2300 (r/min) |

Stator inductance | 58.9 µH |

Supply voltage | 400 v |

Maximum power (kW) | 90 |

Magnet excitation flux | 0.0030225 wb |

Number of pairs of poles | 4 |

e.m.f. constant (ke) | 0.18 |

Moment of inertia of rotating parts (j) | 0.0000528 n.m.s^{2} |

Stator resistance | 0.025 ω |

Controller Type | Control System Parameters | ||||
---|---|---|---|---|---|

Settling Time (s) | Peak Overshoot | Steady Stat Error | Numbers of Oscillations | Response Time When a Signal Fault | |

PID controller | 0.07 | Low | 0.6 | 1 | Medium |

FLC controller | 0.02 | Nil | Very low | Nil | Low |

ANN controller | 0.06 | Nil | Very low | Nil | - |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ahmed Baba, M.; Naoui, M.; Cherkaoui, M.
Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications. *Sustainability* **2023**, *15*, 10430.
https://doi.org/10.3390/su151310430

**AMA Style**

Ahmed Baba M, Naoui M, Cherkaoui M.
Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications. *Sustainability*. 2023; 15(13):10430.
https://doi.org/10.3390/su151310430

**Chicago/Turabian Style**

Ahmed Baba, Mariem, Mohamed Naoui, and Mohamed Cherkaoui.
2023. "Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications" *Sustainability* 15, no. 13: 10430.
https://doi.org/10.3390/su151310430