What question did this study set out to answer?

This research aims to develop a deep learning-based framework for maximum power point tracking in solar-powered electric vehicle charging.

January 18, 2026Open Access

Development and Real‐Time Validation of Deep Learning‐Based LSTM‐Tuned MPPT Control for a 5 kW Solar‐Powered EV Charging System

Key Points

This research aims to develop a deep learning-based framework for maximum power point tracking in solar-powered electric vehicle charging.
Developed a deep learning model using LSTM networks for MPPT control.
Used hourly irradiance and temperature data from NASA/POWER for model training.
Optimized the LSTM model via stochastic gradient descent with momentum.
Compared LSTM-based performance against traditional MPPT algorithms like INC, PSO, and ANN.
Validated the system in MATLAB/Simulink and implemented it on an OPAL‐RT platform.
LSTM-based MPPT achieved a power tracking efficiency of 97.63%.
Demonstrated low current ripple of 0.21%.
Maintained minimal prediction error with RMSE at 0.59%.
Showed superior performance compared to conventional MPPT methods.

Abstract

ABSTRACT The increasing adoption of electric vehicles (EVs) necessitates efficient and eco‐friendly charging solutions. Solar‐powered EV charging offers a sustainable alternative to grid‐dependent systems by reducing carbon emissions. However, the intermittent nature of solar irradiance demands robust maximum power point tracking (MPPT) algorithms to ensure optimal power extraction. Conventional MPPT methods often face challenges like slow convergence and limited tracking accuracy. To address this, the proposed study introduces a deep learning‐based MPPT framework using long short‐term memory (LSTM) networks for intelligent, data‐driven control of a boost converter in a solar‐powered EV charging system. The LSTM model is optimized employing stochastic gradient descent with momentum and trained using irradiance and temperature hourly data obtained from NASA/POWER for Jaipur city, India. The controller's performance is benchmarked against traditional algorithms, INC, PSO and ANN. Results show that the LSTM‐based MPPT achieved superior tracking efficiency (97.63%), low current ripple (0.21%), and minimal prediction error (RMSE: 0.59%). Afterwards, this LSTM‐tuned solar system is employed to charge a 5 kW EV through a boost and a dual active bridge converter. The entire system is validated in MATLAB/Simulink and implemented in real‐time on an OPAL‐RT OP4512 platform, confirming its effectiveness for intelligent and reliable solar‐powered EV charging.

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Cite This Study

Khan et al. (Thu,) studied this question.

synapsesocial.com/papers/696c774feb60fb80d1395995 https://doi.org/https://doi.org/10.1049/pel2.70156

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