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PV-EV Model Predictive Control- A Review
Subject area: Science,Engineering and Technology · Area of research: Battery Management
Abstract
The increasing adoption of electric vehicles (EVs) and the growing reliance on renewable energy sources like photovoltaic (PV) systems are transforming the traditional energy landscape. However, integrating these technologies into a unified system presents significant operational challenges, particularly in terms of real-time energy management and power flow optimization. This paper presents a comprehensive solution for managing power distribution in PV-powered EV charging stations through the application of Model Predictive Control (MPC). The proposed method leverages the predictive capabilities of MPC to anticipate future energy demands and PV generation levels, enabling informed, real-time decisions that optimize charging operations while minimizing reliance on the utility grid. The control framework accounts for dynamic variables such as solar irradiance, state-of-charge of connected EVs, and time-varying electricity tariffs. By continuously solving an optimization problem over a moving time horizon, the system adjusts control inputs to achieve efficient power allocation and cost-effective charging. Simulation studies demonstrate that the MPC-based strategy significantly improves system performance compared to traditional rule-based methods. Key benefits include reduced energy losses, enhanced grid stability, and increased utilization of locally generated solar power. Additionally, the system ensures that EVs are charged within required timeframes without overloading the network. The results confirm that MPC offers a flexible and scalable approach for intelligent energy management in smart charging infrastructures. This method contributes to the development of sustainable, grid-friendly EV charging networks that align with future smart grid and decarbonization goals.
Keywords
Electric vehicles, Battery energy storage, Photovoltaic panel, Grid, MPPT.
How to cite this paper
@article{1708982,
author = {Ganashree C R, Harshini S V, Pooja G S, Sindhu K N, Gopal Chandra Sarkar},
title = {PV-EV Model Predictive Control- A Review},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {93-103},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708982.pdf},
abstract = {The increasing adoption of electric vehicles (EVs) and the growing reliance on renewable energy sources like photovoltaic (PV) systems are transforming the traditional energy landscape. However, integrating these technologies into a unified system presents significant operational challenges, particularly in terms of real-time energy management and power flow optimization. This paper presents a comprehensive solution for managing power distribution in PV-powered EV charging stations through the application of Model Predictive Control (MPC). The proposed method leverages the predictive capabilities of MPC to anticipate future energy demands and PV generation levels, enabling informed, real-time decisions that optimize charging operations while minimizing reliance on the utility grid. The control framework accounts for dynamic variables such as solar irradiance, state-of-charge of connected EVs, and time-varying electricity tariffs. By continuously solving an optimization problem over a moving time horizon, the system adjusts control inputs to achieve efficient power allocation and cost-effective charging. Simulation studies demonstrate that the MPC-based strategy significantly improves system performance compared to traditional rule-based methods. Key benefits include reduced energy losses, enhanced grid stability, and increased utilization of locally generated solar power. Additionally, the system ensures that EVs are charged within required timeframes without overloading the network. The results confirm that MPC offers a flexible and scalable approach for intelligent energy management in smart charging infrastructures. This method contributes to the development of sustainable, grid-friendly EV charging networks that align with future smart grid and decarbonization goals.},
keywords = {Electric vehicles, Battery energy storage, Photovoltaic panel, Grid, MPPT.},
month = {June},
}