Package: co2mpas-driver
versions:documentation:https://co2mpas-driver.readthedocs.io/en/latest/sources:https://code.europa.eu/jrc-ldv/co2mpas-driverkeywords:automotive, car, cars, driver, MCF, driving, simulation, simulator, standard, vehicle, vehicles, driver characterisation, driver profile, free-flow acceleration model, acceleration dynamics, driver behaviour, fuel/energy consumptionshort name:co2mpas_driverlive-demo:Copyright and License:Â© Copyright (c) 2021 European Union.Licensed under the EUPL, Version 1.2 or â€“ as soon they will be approved by the European Commission â€“ subsequent versions of the EUPL (the “Licence”);
You may not use this work except in compliance with the Licence.
You may obtain a copy of the Licence at:Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an “AS IS” basis, WITHOUT WARRANTIES OR CONDITIONS
OF ANY KIND, either express or implied. See the Licence for the specific language governing permissions and limitations under the Licence.Table of ContentsIntroductionInstallationPrerequisitesDownloadInstallUninstallUsageSetupLoad dataDispatcherPlotResultsIntroductionCo2mpas_driver is a library used to implement the microsimulation free-flow acceleration model (MFC).
The MFC is able to accurately and consistently capture the acceleration dynamics of road vehicles
using a lightweight and parsimonious approach. The model has been developed to be integrated in traffic
simulation environments to enhance the realism of vehicles movements, to explicitly take into account
driver behaviour during the vehicle acceleration phases, and to improve the estimation of fuel/energy
consumptions and emissions, without significantly increasing their computational complexity. The proposed model
is valid for both internal combustion engine and battery electric vehicles. The MFC has been developed by the Joint
Research Centre of the European Commission in the framework of the Proof of Concept programme 2018/2019.
For more details on the model please refer to Makridis et al.(2019)and He et al.(2020).InstallationPrerequisitesPython-3.6+is required andPython-3.7+recommended.TipOnWindows, it is preferable to use theAnacondadistribution.
To avoid possible incompatibilities with other projects, it is recommended to install the package in a dedicated environment.DownloadDownload the sources,either withgit, by giving this command to the terminal:git clone https://github.com/JRCSTU/co2mpas_driver --depth=1InstallFrom within the project directory, run one of these commands to install it:for standard python, installing withpipis enough:pip install -e .[path_to_co2mpas_driver]or from @master branch:pip install git+https://github.com/JRCSTU/co2mpas_driver.git@masterUninstallTo uninstall the package, run the following command from the project directory:pip uninstall co2mpas_driverUsageIn this example we will use co2mpas_driver model in order to extract the drivers
acceleration behavior as approaching the target speed.SetupFirst, set up python,numpy,matplotlib.Set up python environment:numpyfor numerical routines, andmatplotlibfor plotting>>> import numpy as np
>>> import matplotlib.pyplot as pltco2mpas_drivermust be imported as a dispatcher (dsp). The dsp contains
functions to process vehicle data and run thecom2pas_drivermodel. Also is necessary
to importschedulafor selecting and executing functions from theco2mpas_driver.
For more information on how to useschedula:https://pypi.org/project/schedula/>>> from co2mpas_driver import dsp
>>> import schedula as shLoad dataLoad 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}
}DispatcherDispatcherwill 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 likeload,model,writeandplot.The Load moduleMerged vehicle data for the vehicle_id used aboveLoad 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()ResultsFigure 1.Speed(m/s) versus time(s) graph over the desired speed range.Acceleration(m/s*2) versus speed(m/s) graphFigure 2.Acceleration per gear, the gear-shifting points and final acceleration potential of our selectedvehicle over the desired speed rangeAcceleration(m/s*2) versus speed graph(m/s)Figure 3.The final acceleration potential of our selected vehicle over the desired speed range.