Automotive MATLAB Projects - MATLAB Solutions

• Problem & Objective: Automate unmanned railway level crossing gates by detecting approaching/departing trains via track sensors and actuating gate servo motors.
• Key MATLAB Functions: sim, sfnew, writeDigitalPin, plot
• Expected Output/Metrics: State transition diagram for gate open/close timing, train arrival sensor detection latency, and collision hazard prevention logs.

• Problem & Objective: Design a smart vehicle entrance/exit gate system with vehicle detection sensors, automatic barrier opening/closing, and live counter display GUI.
• Key MATLAB Functions: uifigure, readDigitalPin, writeDigitalPin, sim
• Expected Output/Metrics: Real-time vehicle entry/exit count, gate response time (<1.5s), sensor triggering reliability rate, and App Designer control interface.

• Problem & Objective: Design Formula Student race car electronic control units (ECUs) for pneumatic electro-actuated paddle gear shifting and launch control via CAN bus telemetry.
• Key MATLAB Functions: canChannel, receive, transmit, sim
• Expected Output/Metrics: Shift time latency (<50ms), engine RPM synchronization plots during downshifts, CAN bus message load efficiency, and clutch engagement profile.

• Problem & Objective: Model PID automatic train speed regulation for high-speed bullet trains (300+ km/h), tuning gains to balance passenger comfort vs tight schedule tracking.
• Key MATLAB Functions: pidtune, step, bode, sim
• Expected Output/Metrics: Velocity step-response curves, jerk limit evaluation (m/s³), velocity tracking error (<0.5 km/h), and braking distance safety margin.

• Problem & Objective: Mitigate reactive power and voltage sags on AC electrified railway traction substations (25 kV 50 Hz) caused by high-power electric locomotives using STATCOM compensation.
• Key MATLAB Functions: sim, power_fftscope, mean, rms
• Expected Output/Metrics: Traction substation voltage profile stabilization, power factor correction (>0.98), harmonic distortion reduction, and STATCOM rating sizing.

• Problem & Objective: Develop Automatic Train Protection (ATP) and Stop (ATS) control logic using trackside beacon signal verification and radar obstacle distance sensors.
• Key MATLAB Functions: sfnew, sim, stepinfo, plot
• Expected Output/Metrics: Emergency brake deceleration distance profile, speed limit enforcement charts, signal over-speed prevention logs, and ATP response delay.

• Problem & Objective: Formally verify embedded automotive feature interactions (e.g. Adaptive Cruise Control vs Automatic Emergency Braking) by translating Stateflow state charts into SMV logic.
• Key MATLAB Functions: sldvrun, sfnew, parse, export
• Expected Output/Metrics: Model checker counter-example traces, state transition coverage (100%), feature deadlock detection reports, and formal property proof certificates.

• Problem & Objective: Model hybrid fuel cell and supercapacitor energy management strategies under dynamic urban driving cycles to optimize transient power delivery and hydrogen fuel usage.
• Key MATLAB Functions: sim, power_fuelcell, mean, plot
• Expected Output/Metrics: Power split efficiency between fuel cell and supercapacitor, DC bus voltage ripple, hydrogen consumption savings (%), and vehicle acceleration response.

• Problem & Objective: Build a thermodynamic thermodynamic 1.6L spark-ignition IC engine model in MATLAB, calibrating volumetric efficiency maps and Brake Thermal Efficiency against dyno data.
• Key MATLAB Functions: fminsearch, interp2, sim, plot
• Expected Output/Metrics: Engine torque/power curves across RPM range, Brake Specific Fuel Consumption (BSFC) map, experimental verification error (<3%), and BTE estimation.

• Problem & Objective: Model longitudinal bicycle ride dynamics with fuzzy logic automated gear shifting controllers to maintain optimal rider cadence under varying road slopes.
• Key MATLAB Functions: mamfis, evalfis, sim, plot
• Expected Output/Metrics: Rider cadence stability graph (RPM), gear shifting event markers, rider power output vs gradient, and speed response.

Sample MATLAB Implementation: Vehicle Cruise Control PID Controller

Simulating vehicle speed response under cruise control dynamics:

% Vehicle Parameters
m = 1000; % Vehicle mass (kg)
b = 50;   % Damping coefficient (N*s/m)
u = 500;  % Control force input (N)

% Transfer function V(s)/U(s) = 1 / (m*s + b)
P_vehicle = tf(1, [m b]);

% PID Controller Parameters
Kp = 800; Ki = 40; Kd = 0;
C_pid = pid(Kp, Ki, Kd);

% Closed loop transfer function
sys_cl = feedback(C_pid * P_vehicle, 1);
t = 0:0.1:20;
step(sys_cl * 25, t); % Target speed 25 m/s (90 km/h)
title('Vehicle Cruise Control Speed Response');
xlabel('Time (sec)'); ylabel('Speed (m/s)'); grid on;

Frequently Asked Questions (FAQs)