Simulation Analysis of Extended Kalman Filter Applied for Estimating Position and Speed of a Brushless DC Motor

Chojowski, Maciej

doi:10.2478/pead-2018-0008

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The proposed controller decreases the percentage overshoot and reduce the settling time. In 2018, Chojowski [13] proposed the EKF to estimates the parameters of the BLDC motor. A critical step in the filter design is the choice of the initial values for the covariance matrices of the plant model (Q) and observation model (R) as they affect the performance, convergence, and stability.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Speed Control of a Brushless DC Motor Using Particle Filter (PF)

Abedulabbas¹,

Yaseen²

2022

MMEP

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The equivalent of electricity has recently been used to replace all the wear-prone moving mechanical components that produce faults. The electronic unit that substitutes the mechanical commutation unit in Brushless Direct Current (BLDC) motors improves dynamic properties, noise level, and efficiency. This work describes a method for estimating the BLDC machine's rotor speed and position by using Extended Kalman Filter (EKF) and Particle Filter (PF). The BLDC is a non-linear system with nonlinear measurements. To perform the EKF, Jacobian linearization of the motor model and the observation are needed. Linearization leads to a decrease in the accuracy of filter estimation. In PF, the relative likelihood of each particle is computed according to the measurements. Resampling gives set particles are distributed according to power density function (pdf). Then the PF can compute any desired statistical measure of this pdf. A sensorless drive has an accurate good throughout a wide speed range and with varied load torque, according to the simulation's results. The results show that the velocity inaccuracy rate at PF is approximately 0.01% and that at EKF it is approximately 1%. According to the findings, the PF outperformed the EKF in a comparison between them.

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Section: Introductionmentioning

confidence: 99%

Sensorless Speed Control of a Brushless DC Motor Using Particle Filter (PF)

Abedulabbas¹,

Yaseen²

2022

MMEP

View full text Add to dashboard Cite

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Energy Management Strategy with Regenerative-Breaking Recovery of Mixed Storage Systems for Electric Vehicles

Fouad,

Elhak,

Elhakim

et al. 2024

Power Electronics and Drives

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The present paper addresses the energy management (EM) strategy between batteries and ultracapacitors (UCs) in a dual-propulsion urban electric vehicle (EV). The use of two propulsion machines proves advantageous for high-performance EVs facing spatial constraints. Allocating load power requirements among the propulsion machines and energy storage components poses a significant challenge in this design. In this paper, the control strategy presents managing the energy flow between the converters and the two brushless DC motors (BLDCs) motors via the DC link in order to maintain the energy demand of the EV coming from the dynamics of the latter. For this, power control is carried out by a management algorithm. This management is based on the power requested/generated by the two machines (BLDCs), the state of charge of the batteries (SOCBat) and the state of charge of the ultracapacitors (SOCUC). The bidirectional DC-DC converter is controlled with current to ensure the functioning of the motor or the generator of the vehicle. We also integrate the controls of the DC bus and BLDC. Additionally, the recovered energy during braking is stored in the battery or in the UC depending on the operating conditions.

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Simulation Analysis of Extended Kalman Filter Applied for Estimating Position and Speed of a Brushless DC Motor

Cited by 2 publications

References 13 publications

Sensorless Speed Control of a Brushless DC Motor Using Particle Filter (PF)

Sensorless Speed Control of a Brushless DC Motor Using Particle Filter (PF)

Energy Management Strategy with Regenerative-Breaking Recovery of Mixed Storage Systems for Electric Vehicles

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