Sensor angle‐based control strategy and dynamic analysis of a sinusoidal pulse width modulation‐operated permanent magnet synchronous machine drive for electric propulsion unit

Sain, Chiranjit; Banerjee, Atanu; Biswas⃰, Pabitra Kumar; Babu, Thanikanti Sudhakar; Baladhanautham, Chitti Babu

doi:10.1002/2050-7038.13090

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The battery helps in meeting the peak power demands and improving the overall efficiency of the vehicle. The characteristics and parameters of the battery we have taken reference [20]. These may include the nominal voltage, capacity, internal resistance, charge/discharge efficiency, and voltage limits.…”

Section: System Descriptionmentioning

confidence: 99%

Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique

Chatterjee,

Biswas,

Sain

et al. 2023

IEEE Access

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This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certain disadvantages to using petrol or diesel, such as poor fuel economy and pollution-causing exhaust gas emissions. Furthermore, there are some limitations with existing available work, and the merger of these different optimisation techniques will be advantageous for achieving optimal performance. To address them, the purpose of this research is to create an efficient energy management approach by combining SVM, KNN, and the Naive Bayes technique. Additionally, by combining these classifier techniques better performing EMS is developed. Using the proposed features, the optimisation approach's performance accuracy is increased. Furthermore, these individual classifiers comprising of SVM, KNN & Naïve Bayes is giving accuracy percentage of 96%, 92% & 94% respectively. Finally, after combining these three classifiers we have achieved an accuracy percentage of 98%.

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Section: System Descriptionmentioning

confidence: 99%

Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique

Chatterjee,

Biswas,

Sain

et al. 2023

IEEE Access

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“…Several strategies for real‐time performance analysis of energy management for combined power systems have also been reported employing fuel cells. Among these are classical dynamic programming [15], neural networks [16], adaptive optimum control [17], and H‐infinity control [18]. Due to their complexity and high computational demands, these strategies might make the EMS's response time slower.…”

Section: Introductionmentioning

confidence: 99%

SVM classifier based energy management strategy for dual‐source fuel cell hybrid electric vehicles

Chatterjee,

Biswas,

Sain

et al. 2024

The Journal of Engineering

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This study proposes an optimal energy management strategy for dual‐source fuel cell hybrid electric vehicles (FCHEV) utilizing the support vector machine (SVM) classifier. The goal is to optimize power distribution between fuel cells and batteries to enhance vehicle's performance and efficiency. The SVM classifier is trained using a dataset of driving conditions and corresponding optimal power distribution (OPD) values obtained through simulation. The trained classifier predicts real‐time OPD based on driving conditions. In comparison to existing literature, this study conducts a comparative analysis of energy management control strategies like model predictive control (MPC), fuzzy, equivalent consumption minimization strategy (ECMS), proportional‐integral (PI) control, and state machine control (SMC) strategy for FCHEVs using the MATLAB/SIMULINK platform and real‐world driving dataset. The proposed strategy is then tested in a real‐time EV simulator to verify its efficacy. Additionally, this study introduces the SVM classifier technique for selecting the optimal energy management strategy for FCHEVs. Performance analysis using SVM reveals that the MPC control strategy offers the highest efficiency compared to other techniques based on selected features, achieving an average performance of 95% through cross‐validation. This analysis demonstrates the most cost‐effective and fuel‐efficient utilization of electricity flow in a modern energy‐efficient environment.

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“…Therefore, high-sensitivity optical encoders or angle detectors (resolvers) are used for position detection. The use of this type of sensor causes an increase in system cost and complexity of control [Gamazo-Real et al, 2010;Sain et al,2021].…”

mentioning

confidence: 99%

Backstepping Control Design in Conjunction with an EKF-based Sensorless Field-Oriented Control of an IPMSM

Uyulan¹

2022

Preprint

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The collector and brushless electronic commutation machines based on the working principle of the direct current machines have been widely used in industrial applications through the help of the developments in power electronics, microelectronics, permanent magnets, microprocessors&control, digital signal processing technologies, etc. Internal permanent magnet synchronous motors (IPMSMs) are used in increasing numbers due to their advantages such as high torque/current and torque/inertia, robust construction, high efficiency, reliability, etc. The problems brought by position sensors, especially in terms of application, performance, mass production, and cost, have made sensorless control a necessity in drive systems and applications.This paper presents a backstepping control method for speed sensorless IPMSM based on an extended Kalman filter (EKF). First, a comprehensive nonlinear dynamical model of the IPMSM in the direct and quadrature ( ) rotor frame is derived and its state-space representation is obtained. Then, the rotor speed and current tracking backstepping controllers are designed to achieve precise tracking and anti-disturbance performance. The designed controllers are embedded into the field-oriented control (FOC) scheme. The asymptotic stability condition for the backstepping controller is guaranteed through the Lyapunov stability theorem. Finally, An EKF is designed for estimating the immeasurable mechanical parameters of IPMSM and tracking the system states in a finite time with high steady-state precision. The effectiveness of the proposed methodology is proved by conducting simulations having various dynamic operating conditions such as sudden torque load change, command speed change, and parameter variation.

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Sensor angle‐based control strategy and dynamic analysis of a sinusoidal pulse width modulation‐operated permanent magnet synchronous machine drive for electric propulsion unit

Cited by 5 publications

References 28 publications

Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique

Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique

SVM classifier based energy management strategy for dual‐source fuel cell hybrid electric vehicles

Backstepping Control Design in Conjunction with an EKF-based Sensorless Field-Oriented Control of an IPMSM

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