co2mpas-driver 1.3.4

Prerequisites

Python-3.6+ is required and Python-3.7+ recommended.

Download

Download the sources,

• either with git, by giving this command to the terminal:

  git clone https://github.com/JRCSTU/co2mpas_driver --depth=1

Install

From within the project directory, run one of these commands to install it:

• for standard python, installing with pip is enough:

  pip install -e .[path_to_co2mpas_driver]

or from @master branch:

pip install git+https://github.com/JRCSTU/co2mpas_driver.git@master

Uninstall

To uninstall the package, run the following command from the project directory:

pip uninstall co2mpas_driver

Setup

• First, set up python, numpy, matplotlib.

  Set up python environment: numpy for numerical routines, and matplotlib for plotting

  >>> import numpy as np
  >>> import matplotlib.pyplot as plt
  

• co2mpas_driver must be imported as a dispatcher (dsp). The dsp contains functions to process vehicle data and run the com2pas_driver model. Also is necessary to import schedula for selecting and executing functions from the co2mpas_driver. For more information on how to use schedula: https://pypi.org/project/schedula/

  >>> from co2mpas_driver import dsp
  >>> import schedula as sh
  

Load data

• Load vehicle data for a specific vehicle from vehicles database

  >>> db_path = 'EuroSegmentCar.csv'
  

• Load user input parameters from an excel file

  >>> input_path = 'sample.xlsx'
  

• Sample time series

  >>> sim_step = 0.1 #The simulation step in seconds
  >>> duration = 100 #Duration of the simulation in seconds
  >>> times = np.arange(0, duration + sim_step, sim_step)
  

• Load user input parameters directly writing in your sample script

  >>> inputs = {
  'vehicle_id': 35135,  # A sample car id from the database
  'inputs': {'gear_shifting_style': 0.7, #The gear shifting style as
                                          described in the TRR paper
              'starting_speed': 0,
             'desired_velocity': 40,
             'driver_style': 1},  # gear shifting can take value
  # from 0(timid driver) to 1(aggressive driver)
  'time_series': {'times': times}
  }
  

Dispatcher

• Dispatcher will select and execute the proper functions for the given inputs and the requested outputs

  >>> core = dsp(dict(db_path=db_path, input_path=input_path, inputs=inputs),
     outputs=['outputs'], shrink=True)
  

• Plot workflow of the core model from the dispatcher

  >>> core.plot()
  

  This will plot the workflow of the core model on an internet browser (see below). You can click all the rectangular boxes to see in detail the sub-models like load, model, write and plot.

  

  The Load module

  

  Merged vehicle data for the vehicle_id used above

  

• Load outputs of dispatcher and select the chosen dictionary key (outputs) from the given dictionary.

  >>> outputs = sh.selector(['outputs'], sh.selector(['outputs'], core))
  

• Select the desired output

  >>> output = sh.selector(['Curves', 'poly_spline', 'Start', 'Stop', 'gs',
                'discrete_acceleration_curves', 'velocities',
                'accelerations', 'transmission'], outputs['outputs'])
  

  The final acceleration curves, the engine acceleration potential curves (poly_spline), start, stop, gear shift, discrete acceleration curves, velocities, accelerations and transmission, before calculating the resistances and the limitation due to max possible acceleration (friction).

  >>> curves, poly_spline, start, stop, gs, discrete_acceleration_curves,
  velocities, accelerations, transmission =
  output['Curves'], output['poly_spline'], output['Start'], output['Stop'], output['gs'],
  output['discrete_acceleration_curves'], output['velocities'],
  output['accelerations'], output['transmission']
  

Plot

>>> plt.figure('Time-Speed')
>>> plt.plot(times, velocities)
>>> plt.grid()
>>> plt.figure('Speed-Acceleration')
>>> plt.plot(velocities, accelerations)
>>> plt.grid()
>>> plt.figure('Acceleration-Time')
>>> plt.plot(times, accelerations)
>>> plt.grid()

>>> plt.figure('Speed-Acceleration')
>>> for curve in discrete_acceleration_curves:
    sp_bins = list(curve['x'])
    acceleration = list(curve['y'])
    plt.plot(sp_bins, acceleration, 'k')
>>> plt.show()

Results

Figure 1. Speed(m/s) versus time(s) graph over the desired speed range.

Acceleration(m/s*2) versus speed(m/s) graph

Figure 2. Acceleration per gear, the gear-shifting points and final acceleration potential of our selected

vehicle over the desired speed range

Acceleration(m/s*2) versus speed graph(m/s)

Figure 3. The final acceleration potential of our selected vehicle over the desired speed range.