Evaluation

To ensure reliable and accurate performance measurements, each simulation was executed 10 times under identical conditions, with the mean value computed to minimize the effect of outliers. The simulations were conducted using the BouncingBall.fmu model with two step sizes: 0.01 and 0.0001, as detailed in the tables below.

Test Environment

The experiments were performed on three different implementations:

• Plain C Simulator: Executed within a WSL2 environment on a Windows 10 system with an Intel Core i7-10750H CPU.

• MicroPython Unix Port: Also run within WSL2.

• MicroPython ESP32: Executed directly on the embedded ESP32 hardware.

Performance Analysis

• The Plain C simulator was the fastest, benefiting from low-level optimizations and minimal overhead. However, it has significantly higher memory usage since it stores all step results.

• The MicroPython Unix port performed well on smaller simulations but failed to complete the larger simulation (step size = 0.0001) due to a well-known bug.

• The MicroPython ESP32 implementation successfully completed both simulations with very low RAM usage, but is obviously slower due to CPU limitations.

Simulation Code

The following MicroPython script was used to measure execution time:

import ufmu as fmu
import time
import micropython

start_time = 0
end_time = 10
step_size = XX  # Replaced with 0.01 or 0.0001

start = time.ticks_ms()
simInstance = fmu.setup_simulation(start_time, end_time, step_size)
fmu.change_variable_value(simInstance, 'h', 20)

for i in simInstance:
    pass

print("Time taken: ", time.ticks_diff(time.ticks_ms(), start), "ms")
micropython.mem_info()

Results

Performance results are summarized in the tables below.

Performance with step_size = 0.01

Category	Plain C Simulator	MicroPython Unix	MicroPython ESP32
Time (ms)	1	2	139
Memory usage (KB)	1.632	355	21
Executable size (KB)	112	843	1.725

Performance with step_size = 0.0001. X means that the test couldn’t be conducted

Category	Plain C Simulator	MicroPython Unix	MicroPython ESP32
Time (ms)	1.005	X	141.309
Memory usage (KB)	64.032	X	46
Executable size (KB)	112	843	1.725

Conclusion

The results demonstrate that MicroPython provides a flexible, efficient, and easy-to use option to execute FMU simulation in embedded systems like ESP32.