Renewable Energy Resources Integration for EV Applications Using Sensor less Control and Regenerative Braking

This article introduces an innovative controller and motor architecture designed for an e rickshaw powered by Renewable Energy Sources (RES). In the absence of Hall effect sensors, brushless DC (BLDC) motor drives often experience commutation delays across both low and high speeds. Traditional control methods are limited by the small magnitude of back EMF, making them less effective. The proposed sensorless control strategy addresses this by implementing commutation error correction, enabling full range motor operation. For cost effective EV applications, a simple commutation error compensation technique is developed to detect freewheeling pulses, eliminating the need for low pass filters and streamlining control. Additionally, a zero crossing detection (ZCD) method is introduced, which corrects commutation delay without requiring a separate phase compensator. Any unwanted spikes in the ZCD circuit are removed using a fixed delay digital filter. A modified Landsman converter is used for efficient MPPT control, delivering a smooth current output and reducing the need for a front end ripple filter. The regenerative capability of the BLDC motor drive helps alleviate EV range anxiety, as the renewable energy source ensures a continuous power supply. A PI based Fuzzy Logic Controller is employed, and the effectiveness of the system is validated through MATLAB/Simulink simulations.

Authors

Publication Details

Journal
International Journal of Advanced Trends in Engineering and Management
Published
2024-10-15
DOI
https://doi.org/10.59544/covc4406/ijatemv03i09p1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

Renewable Energy Resources Integration for EV Applications Using Sensor less Control and Regenerative Braking

V. T., G Shashank, S. Saranya, B. Bharathi et al.
International Journal of Advanced Trends in Engineering and Management
Advanced Battery Technologies Research
article

Renewable Energy Resources Integration for EV Applications Using Sensor less Control and Regenerative Braking

V. T., G Shashank, S. Saranya, B. Bharathi, Tarakesh KMM, L Kavya, Tejeswara Rao B
article en

Abstract

This article introduces an innovative controller and motor architecture designed for an e rickshaw powered by Renewable Energy Sources (RES). In the absence of Hall effect sensors, brushless DC (BLDC) motor drives often experience commutation delays across both low and high speeds. Traditional control methods are limited by the small magnitude of back EMF, making them less effective. The proposed sensorless control strategy addresses this by implementing commutation error correction, enabling full range motor operation. For cost effective EV applications, a simple commutation error compensation technique is developed to detect freewheeling pulses, eliminating the need for low pass filters and streamlining control. Additionally, a zero crossing detection (ZCD) method is introduced, which corrects commutation delay without requiring a separate phase compensator. Any unwanted spikes in the ZCD circuit are removed using a fixed delay digital filter. A modified Landsman converter is used for efficient MPPT control, delivering a smooth current output and reducing the need for a front end ripple filter. The regenerative capability of the BLDC motor drive helps alleviate EV range anxiety, as the renewable energy source ensures a continuous power supply. A PI based Fuzzy Logic Controller is employed, and the effectiveness of the system is validated through MATLAB/Simulink simulations.

International Journal of Advanced Trends in Engineering and ManagementVol. III(09)
Affordable and clean energy
Openalex Percentile: Top 76%
Advanced Battery Technologies Research
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Renewable Energy Resources Integration for EV Applications Using Sensor less Control and Regenerative Braking — V. T., G Shashank, et al. · International Journal of Advanced Trends in Engineering and Management (2024) | TGRS Research Map | TGRS