MATLAB Implementation of Fuel Cell Battery Driven Electric Vehicle

Electric vehicles (EVs) are gaining popularity for their efficiency and eco-friendliness. Among the emerging technologies in this domain is the integration of fuel cells and batteries to enhance performance and energy management. In this post, we’ll explore a MATLAB simulation model of a fuel cell-battery hybrid electric vehicle, highlighting key components, control strategies, and system behavior under varying conditions.

Overview of the Simulation Model

The simulation focuses on a hybrid EV powered by a 24V fuel cell with a rated power output of 1.26 kW. The system is designed and implemented using MATLAB/Simulink. The model simulates real-world conditions to evaluate how the fuel cell and battery work together to supply power to the electric drivetrain.

Fuel Cell Parameters and Operating Points

The fuel cell used in this simulation has the following characteristics:

• Nominal voltage: ~23V at 52A

• Maximum output: 2,000W (20V at 100A)

These parameters determine how much power can be extracted under different operating conditions.

Boost Converter and Voltage Regulation

Since the fuel cell outputs 24V and the battery operates at 48V, a boost converter is used to step up the voltage. This converter helps maintain a stable 48V at the output, which is essential for both battery charging and driving the motor.

MPPT Algorithm for Optimal Power Extraction

To maximize energy extraction from the fuel cell, an MPPT (Maximum Power Point Tracking) algorithm is implemented. This algorithm adjusts the duty cycle of the boost converter using the following parameters:

• Initial, maximum, and minimum duty cycles

• Power and voltage change tracking

The control logic incrementally adjusts the duty cycle based on changes in voltage and power, ensuring the system operates near the maximum power point of the fuel cell.

PWM Control for the Boost Converter

The computed duty cycle is used to generate PWM pulses. These pulses are compared with a triangular wave to produce gate signals that control the MOSFET in the boost converter. This regulation ensures the voltage is effectively boosted from 24V to 48V, allowing maximum power transfer to the battery or motor.

BLDC Motor Drive System

The electric motor used in this model is a BLDC (Brushless DC) motor, driven by a voltage source inverter (VSI). The inverter uses Hall sensor outputs to determine rotor position and generate back EMF signals. These signals are fed into a truth table logic which helps produce switching signals (Q1–Q6) for motor commutation.

This approach ensures accurate and efficient motor control based on rotor position and back EMF analysis.

Real-Time Monitoring and Output Parameters

The simulation provides real-time data on:

• Fuel cell voltage, current, and power

• Battery voltage, current, and state of charge (SOC)

• Motor speed and electromagnetic torque

• Back EMF and stator current of the motor

These outputs help evaluate the performance and behavior of the vehicle under various load and supply conditions.

Response to Pressure Variations

To simulate real-world conditions, the model introduces variations in fuel and air pressure:

• Initially set at 1 atm, the pressure is later reduced to 0.5 atm, and eventually to 0 atm.

• As pressure decreases, fuel cell power output drops, affecting the system’s ability to charge the battery and drive the motor.

When the fuel pressure drops to zero, the system shifts:

• The battery transitions from charging to discharging mode, supplying power to the motor.

• This is reflected in a change in battery current polarity and a decrease in SOC.

System Behavior and Power Balance

Despite fuel pressure drops, the vehicle maintains constant speed and torque, showcasing the power balancing capability between the fuel cell and battery. The battery compensates when the fuel cell is inactive, and vice versa. This hybrid approach ensures consistent performance and reliability in dynamic conditions.

Conclusion

This MATLAB simulation demonstrates a robust and flexible hybrid electric vehicle system, where fuel cells and batteries work in tandem. The integration of boost converters, MPPT algorithms, and precise motor control helps maintain performance even under challenging operating scenarios.

Such simulations are vital for advancing the design and control of next-generation electric vehicles. If you're working on hybrid EV systems or studying renewable energy integration, this model provides a solid foundation for deeper exploration.