Electric Vehicle (EV): PV (FLC), Battery, Load in Microgrid

版本 1.0.0 (2.0 MB) 作者: Sachin Wagh
Zaouche Faika et al., “Supervision and Control Strategy for Photovoltaic Generators with Battery Storage”, Int. J. Hydrogen Energy, 42, 2017
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Electric Vehicle (EV): Supervision, Control, and Power Management of PV (Solar) Generation (Fuzzy Logic based MPPT Control) with Battery Energy Storage for a) EV load and b) Variable Rural Electrification Load.
Abstract
This project proposes an advanced energy management and control strategy for a photovoltaic (PV) and battery-based hybrid energy storage system, with two different loading conditions, one with electric vehicles (EVs) as dynamic and high-demand electrical loads and other with variable rural electrification load. While a conventional variable electrification load is also considered for comparative analysis, the integration of EVs introduces more complex challenges due to their unpredictable charging patterns and substantial power requirements. These characteristics necessitate a more sophisticated approach to power supervision and control.
Given the variability of solar generation caused by changes in irradiance and temperature, relying solely on PV power is often insufficient for ensuring a stable energy supply. To address this limitation, the system integrates a battery bank to store surplus energy and provide backup during periods of low solar output. A dedicated supervisory control unit is designed to coordinate the interaction between the PV source, battery storage, and load in real time. This control unit not only ensures maximum energy extraction from the PV array through Maximum Power Point Tracking (MPPT), but also actively protects the battery system from overcharging and deep discharging.
To optimize energy harvesting, both Perturb and Observe (P&O) and Fuzzy Logic Controller (FLC) methods are employed and compared for MPPT implementation. The FLC approach demonstrates improved responsiveness and adaptability under fluctuating environmental and load conditions, particularly when EVs are present. The power management strategy dynamically allocates available energy, maintains operational stability, and ensures that critical loads are prioritized, allowing the system to respond effectively to sudden variations in both generation and demand.
Simulation and modelling are carried out using MATLAB/Simulink, utilizing Simscape Electrical and Control System Toolbox environments. The results validate that the proposed supervisory and energy management scheme enables the PV-battery hybrid system to maintain efficient, autonomous operation regardless of meteorological or loading uncertainties. This confirms the feasibility and effectiveness of the proposed approach for future smart microgrid applications, especially in scenarios involving significant EV integration.
Keywords: Electric vehicle, Solar, Photovoltaic (PV), Fuzzy Logic Controller (FLC), Battery, Power management, and Microgrid.
Main Reference Paper
1. Faika Zaouche, Djamila Rekioua, Jean-Paul Gaubert, and Zahra Mokrani, “Supervision and control strategy for photovoltaic generators with battery storage”, International Journal of Hydrogen Energy, Volume 42, Issue 30, pp. 19536-19555, 2017.
https://doi.org/10.1016/j.ijhydene.2017.06.107
https://www.sciencedirect.com/science/article/pii/S0360319917324217
Relevant References:
2. Z. Mokrani and D. Rekioua and N. Mebarki and T. Rekioua and S. Bacha, “Proposed energy management strategy in electric vehicle for recovering power excess produced by fuel cells”, International Journal of Hydrogen Energy, vol. 42, no. 30, pp. 19556-19575, 2017.
https://www.sciencedirect.com/science/article/pii/S0360319917324205
3. Zahra Mokrani, Djamila Rekioua, and Toufik Rekioua, “Modeling, control and power management of hybrid photovoltaic fuel cells with battery bank supplying electric vehicle”, International Journal of Hydrogen Energy, Volume 39, Issue 27, pp. 15178-15187, 2014.
https://doi.org/10.1016/j.ijhydene.2014.03.215. https://www.sciencedirect.com/science/article/pii/S0360319914009434.
4. N. Mebarki, T. Rekioua, Z. Mokrani, and D. Rekioua, “Supervisor control for stand-alone photovoltaic/hydrogen/ battery bank system to supply energy to an electric vehicle”, International Journal of Hydrogen Energy, Volume 40, Issue 39, pp. 13777-13788, 2015.
https://doi.org/10.1016/j.ijhydene.2015.03.024 https://www.sciencedirect.com/science/article/pii/S0360319915005923
5. N. Mebarki, T. Rekioua, Z. Mokrani, D. Rekioua, and S. Bacha, “PEM fuel cell/ battery storage system supplying electric vehicle”, International Journal of Hydrogen Energy, Volume 41, Issue 45, pp. 20993-21005, 2016.
https://doi.org/10.1016/j.ijhydene.2016.05.208
https://www.sciencedirect.com/science/article/pii/S0360319916309600
Conclusion
This report presented a comprehensive study on the control and energy management of a photovoltaic (PV) system integrated with battery storage, applied in two distinct applications, electrification and electric vehicle (EV) charging. Key components such as PV generation, Maximum Power Point Tracking (MPPT) algorithms, EV load modelling, inverter control, and Direct Torque Control (DTC) of induction motors were critically addressed. The proposed control strategy ensures optimal operation of the system, maintaining high efficiency of the PV array under varying meteorological conditions with FLC based MPPT control. Simulation results from both applications validated the effectiveness of this control approach, demonstrating reliable power delivery and smooth system operation even in the face of fluctuating environmental and load conditions.
The hybrid system, designed to cater to the needs of both electrification and EV charging, proved capable of effectively managing power flow between solar generation, battery storage, and dynamic loads. This adaptability highlights its potential for deployment in rural and off grid areas where power supply is often unreliable. The smart energy management strategy, which coordinates the interaction between PV generation and battery storage, contributes significantly to the overall efficiency and reliability of the system. The ability to operate smoothly during varying solar irradiance and load profiles showcases the robustness of the system in real-world applications.
Furthermore, the system's scalability positions it as a promising solution for additional applications beyond EV charging, including water pumping in remote areas and hybrid microgrids incorporating other renewable sources such as wind and fuel cells. Future enhancements to the energy management strategy could involve more efficient utilization of surplus energy, such as storing excess power in supercapacitors or redirecting it to secondary loads, thereby maximizing overall system efficiency.
In conclusion, the proposed hybrid PV-battery system, with its intelligent control and management strategy, offers a viable solution for decentralized and sustainable energy systems. The flexibility, reliability, and potential for further optimization of this system make it an important contribution to the development of smart grids and renewable energy applications in diverse settings.
clear;
close all;
% Energy Management Function
function [Mode, K1, K2, K3] = EnergyManage(SOC, Ppv, PLOAD, delPload)
% Define minimum and maximum State of Charge (SOC)
SOCmin = 30;
SOCmax = 90;
% Initialize output variables
Mode = 5;
K1 = 0;
K2 = 1;
K3 = 0;
% Calculate power difference between PV and load
delP = Ppv - PLOAD;
% Handle small deviations in power difference
if delP < 20 && delP > -40
delP = 0;
end
% Case 1: When both PV and Load are zero and SOC is at minimum
if Ppv == 0 && PLOAD == 0
if SOC == SOCmin
Mode = 6;
K1 = 0;
K2 = 0;
K3 = 0;
end
end
% Case 2: When PV is zero and load power difference is negative
if Ppv == 0 && delPload < 0
if SOC == SOCmax
Mode = 6;
K1 = 0;
K2 = 0;
K3 = 0;
end
end
% Case 3: When PV power is greater than 0 but less than load power
if Ppv > 0 && Ppv < PLOAD
if PLOAD == 0 && SOC < SOCmax
Mode = 5;
K1 = 1;
K2 = 0;
K3 = 0;
end
end
% Case 4: When PV power is greater than or equal to load power
if Ppv >= PLOAD
if SOC > SOCmin
Mode = 3;
K1 = 0;
K2 = 1;
K3 = 0;
% If power difference in load is negative, switch to mode 6
if delPload < 0
Mode = 6;
K1 = 0;
K2 = 0;
K3 = 0;
end
end
end
% Case 5: When PV power exceeds load and SOC is less than maximum
if Ppv > PLOAD
if SOC < SOCmax
Mode = 1;
K1 = 1;
K2 = 1;
K3 = 0;
end
end
% Case 6: When SOC is greater than SOCmin and PV is zero
if SOC > SOCmin
if Ppv == 0
Mode = 4;
K1 = 0;
K2 = 0;
K3 = 1;
end
end
% Case 7: When SOC is greater than SOCmin and PV power is less than load
if SOC > SOCmin
if Ppv > 0 && Ppv < PLOAD
Mode = 2;
K1 = 0;
K2 = 1;
K3 = 1;
end
end
% Case 8: When PV power is greater than 0 but less than load, and load power is negative
if Ppv > 0 && Ppv < PLOAD
if delPload < 0 && SOC < SOCmax
Mode = 5;
K1 = 1;
K2 = 0;
K3 = 0;
end
end
end

引用格式

Sachin Wagh (2025). Electric Vehicle (EV): PV (FLC), Battery, Load in Microgrid (https://www.mathworks.com/matlabcentral/fileexchange/181352-electric-vehicle-ev-pv-flc-battery-load-in-microgrid), MATLAB Central File Exchange. 检索时间: .

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