MATLAB Implementation of Energy Management in PV Fuel Cell and Battery System

Hybrid energy systems combining photovoltaic (PV) panels, fuel cells, and batteries offer a highly efficient and reliable way to meet power demands, especially in applications requiring both grid independence and sustainability. This blog post explores the implementation of such a system using MATLAB, highlighting the control strategies and component interactions used to optimize power flow.

Overview of the Hybrid Energy System

The model developed in MATLAB incorporates a PV array, a fuel cell, and a battery storage unit, all connected to a common DC bus. This bus serves as the central point for distributing energy to both DC and AC loads.

The system intelligently manages power based on resource availability and demand conditions, using various control strategies and conversion circuits like boost converters and inverters.

Photovoltaic (PV) Array Configuration

The system includes eight PV panels, each rated at 250 W, connected in series to produce a combined output of 2,000 W. The total voltage output is approximately 245.6 V, suitable for interfacing with the DC bus via a boost converter.

To maximize energy extraction, the PV system uses the Incremental Conductance MPPT (Maximum Power Point Tracking) algorithm. This technique adjusts the duty cycle of the boost converter based on real-time voltage and current readings from the PV panels, ensuring operation at the maximum power point under varying irradiance conditions.

Battery Energy Storage System

A battery unit rated at 300 V and 48 Ah is connected to the DC bus through a bidirectional DC-DC converter. The converter is governed by voltage and current control loops.

• The DC bus voltage is regulated at 400 V using a PI controller.

• The controller generates a reference battery current, which is then compared with the actual current to manage charging and discharging modes.

• This setup ensures that the battery helps stabilize the system during variations in PV output or load demand.

Fuel Cell Integration and Operation Conditions

The fuel cell has a nominal rating of 4 kW and can supply up to 7 kW under maximum load. The fuel cell parameters vary as follows:

• At nominal operation: 220 V and 20 A

• At peak operation: 200 V and 35 A

It connects to the DC bus through a boost converter controlled by a current control strategy.

The fuel cell is only activated under specific conditions:

• PV output drops below 600 W

• Battery State of Charge (SOC) falls below 30%

When these two conditions are met, the fuel cell takes over power supply to the DC and AC loads, and any excess energy is directed to recharge the battery.

AC Load and Inverter Control

An inverter is used to convert DC power to AC for the AC load. This is managed through:

• DQ transformation of AC signals

• Voltage and current PI controllers in the DQ domain

• Modulating signals generated and passed through a sine wave generator to produce switching pulses for the inverter

The AC output is filtered using an LC filter, ensuring a clean and stable waveform for the load. The inverter supplies power as per the combined availability from PV, fuel cell, and battery systems.

Dynamic Simulation of Irradiance Conditions

To test the system’s responsiveness, irradiation levels are dynamically varied during the simulation—from high (1000 W/m²) to low (200 W/m²). As the PV output decreases:

• The battery initially compensates by discharging.

• When PV output is insufficient and battery SOC drops below 30%, the fuel cell activates to supply the load and recharge the battery.

The system continuously adapts to these changes, with control algorithms maintaining stable bus voltage and ensuring uninterrupted power delivery.

Conclusion

This MATLAB simulation effectively demonstrates how a hybrid energy system can intelligently manage multiple energy sources to ensure reliability and efficiency. By integrating MPPT for PV, bidirectional control for batteries, and conditional activation for fuel cells, the model showcases a smart, sustainable approach to hybrid power management.