基于LSTM调优的深度学习型MPPT控制在5 kW太阳能电动汽车充电系统中的开发与实时验证

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.