fuefit 0.0.6

*fuefit* fits engine-maps on physical parameters

Release:

x.x.x

Documentation:

https://fuefit.readthedocs.org/

Source:

https://github.com/ankostis/fuefit

PyPI repo:

https://pypi.python.org/pypi/fuefit

Keywords:

automotive, car, cars, consumption, engine, engine-map, fitting, fuel, vehicle, vehicles

Copyright:

2014 European Commission (JRC-IET)

License:

EUPL 1.1+

Fuefit is a python package that calculates fitted fuel-maps from measured engine data-points based on coefficients with physical meaning.

Introduction

Overview

The Fuefit calculator was developed to apply a statistical fit on measured engine fuel consumption data (engine map). This allows the reduction of the information necessary to describe an engine fuel map from several hundred points to seven statistically calculated parameters, with limited loss of information.

More specifically this software works like that:

1. Accepts engine data as input, constituting of triplets of RPM, Power and Fuel-Consumption or equivalent quantities eg mean piston speed (CM), brake mean effective pressure (BMEP) or Torque, fuel mean effective pressure (PMF).

2. Fits the provided input to the following formula [1] [2] [3]:

\begin{equation*} \mathbf{BMEP} = (a + b\times{\mathbf{CM}} + c\times{\mathbf{CM^2}})\times{\mathbf{PMF}} + (a2 + b2\times{\mathbf{CM}})\times{\mathbf{PMF^2}} + loss0 + loss2\times{\mathbf{CM^2}} \end{equation*}

3. Recalculates and (optionally) plots engine-maps based on the coefficients that describe the fit:

   \begin{equation*} a, b, c, a2, b2, loss0, loss2 \end{equation*}

An “execution” or a “run” of a calculation along with the most important pieces of data are depicted in the following diagram:

              .----------------------------.                    .-----------------------------.
             /        Input-Model         /                    /    Output(Fitted)-Model     /
            /----------------------------/                    /-----------------------------/
           / +--engine                  /                    / +--engine                   /
          /  |  +--...                 /                    /  |  +--fc_map_coeffs        /
         /   +--params                /  ____________      /   +--measured_eng_points    /
        /    |  +--...               /  |            |    /    |    n   p  fc  bmep ... /
       /     +--measured_eng_points /==>| Calculator |==>/     |  ... ... ...  ...     /
      /          n    p    fc      /    |____________|  /      +--fitted_eng_points   /
     /          --  ----  ---     /                    /       |    n    p   fc      /
    /            0   0.0    0    /                    /        |  ...  ...  ...     /
   /           600  42.5   25   /                    /         +--mesh_eng_points  /
  /           ...    ...  ...  /                    /               n    p   fc   /
 /                            /                    /              ...  ...  ...  /
'----------------------------'                    '-----------------------------'

Apart from various engine-characteristics under /engine the table-columns such as capacity and p_rated, the table under /measured_eng_points must contain at least one column from each of the following categories (column-headers are case-insensitive):

1. Engine-speed:

   N        [1/min]
   N_norm   [-]        : where N_norm = (N – N_idle) / (N_rated-N_idle)
   CM       [m/sec]

2. Load-Power-capability:

   P        [kW]
   P_norm   [-]        : where P_norm = P/P_MAX
   T        [Nm]
   BMEP     [bar]

3. Fuel-consumption:

   FC       [g/h]
   FC_norm  [g/KWh]    : where FC_norm = FC[g/h] / P_MAX [kW]
   PMF      [bar]

The Input & fitted data-model described above are trees of strings and numbers, assembled with:

• sequences,

• dictionaries,

• class(pandas.DataFrame),

• class(pandas.Series).

[1]

Bastiaan Zuurendonk, Maarten Steinbuch(2005): “Advanced Fuel Consumption and Emission Modeling using Willans line scaling techniques for engines”, Technische Universiteit Eindhoven, 2005, Department Mechanical Engineering, Dynamics and Control Technology Group, http://alexandria.tue.nl/repository/books/612441.pdf

[2]

Yuan Zou, Dong-ge Li, and Xiao-song Hu (2012): “Optimal Sizing and Control Strategy Design for Heavy Hybrid Electric Truck”, Mathematical Problems in Engineering Volume 2012, Article ID 404073, 15 pages doi:10.1155/2012/404073

[3]

Xi Wei (2004): “Modeling and control of a hybrid electric drivetrain for optimum fuel economy, performance and driveability”, Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Quick-start

The program runs on Python-3.3+ and requires numpy/scipy, pandas and win32 libraries along with their native backends to be installed.

On Windows/OS X, it is recommended to use one of the following “scientific” python-distributions, as they already include the native libraries and can install without administrative priviledges:

• WinPython (Windows only),

• Anaconda,

• Canopy,

Assuming you have a working python-environment, open a command-shell (in Windows use program(cmd.exe) BUT ensure program(python.exe) is in its envvar(PATH)) and try the following console-commands:

Install:

$ pip install fuefit
$ fuefit --winmenus                         ## Adds StartMenu-items, Windows only.

See: doc(install)

Cmd-line:

$ fuefit --version
0.0.6

$ fuefit --help
...

## Change-directory into the `fuefit/test/` folder in the  *sources*.
$ fuefit -I FuelFit_real.csv header+=0 \
    -I ./FuelFit.xlsx sheetname+=0 header@=None names:='["p","n","fc"]' \
    -I ./engine.csv file_frmt=SERIES model_path=/engine header@=None \
    -m /engine/fuel=petrol \
    -m /params/plot_maps@=True \
    -O full_results_model.json \
    -O fit_coeffs.csv model_path=/engine/fc_map_coeffs   index?=false \
    -O t1.csv model_path=/measured_eng_points   index?=false \
    -O t2.csv model_path=/mesh_eng_points       index?=false \

See: cmd-line-usage

Excel:

$ fuefit --excelrun                                             ## Windows & OS X only

See: excel-usage

Python-code:

code-block:

>>> import pandas as pd
>>> from fuefit import datamodel, processor, test

>>> inp_model = datamodel.base_model()
>>> inp_model.update({...})                                     ## See "Python Usage" below.        # doctest: +SKIP
>>> inp_model['engine_points'] = pd.read_csv('measured.csv')    ## Pandas can read Excel, matlab, ... # doctest: +SKIP
>>> datamodel.set_jsonpointer(inp_model, '/params/plot_maps', True)

>>> datamodel.validade_model(inp_model, additional_properties=False)            # doctest: +SKIP

>>> out_model = processor.run(inp_model)                                        # doctest: +SKIP

>>> print(datamodel.resolve_jsonpointer(out_model, '/engine/fc_map_coeffs'))    # doctest: +SKIP
a            164.110667
b           7051.867419
c          63015.519469
a2             0.121139
b2          -493.301306
loss0      -1637.894603
loss2   -1047463.140758
dtype: float64

See: python-usage

Tip

The commands beginning with $, above, imply a Unix like operating system with a POSIX shell (Linux, OS X). Although the commands are simple and easy to translate in its Windows counterparts, it would be worthwile to install Cygwin to get the same environment on Windows. If you choose to do that, include also the following packages in the Cygwin’s installation wizard:

* git, git-completion
* make, zip, unzip, bzip2
* openssh, curl, wget

But do not install/rely on cygwin’s outdated python environment.

Install

Fuefit-x.x.x runs on Python-3.3+, and it is distributed on Wheels.

Note

This project depends on the numpy/scipy, pandas and win32 python-packages that themselfs require the use of C and Fortran compilers to build from sources. To avoid this hussle, you can choose instead a self-wrapped python distribution like Anaconda/minoconda, Winpython, or Canopy.

Tip

• Under Windows you can try the self-wrapped WinPython distribution, a higly active project, that can even compile native libraries using an installations of Visual Studio, if available (required for instance when upgrading numpy/scipy, pandas or matplotlib with command(pip)).

  Just remember to Register your WinPython installation after installation and add your installation into envvar(PATH) (see doc(faq)):

  • To register it, go to Start menu --> All Programs --> WinPython --> WinPython ControlPanel, and then Options --> Register Distribution .

  • For the path, add or modify the registry string-key [HKEY_CURRENT_USER\Environment] "PATH".

• An alternative scientific python-environment is the Anaconda cross-platform distribution (Windows, Linux and OS X), or its lighter-weight alternative, miniconda.

  On this environment you will need to install this project’s dependencies manually using a combination of program(conda) and program(pip) commands. See file(requirements/miniconda.txt), and peek at the example script commands in file(.travis.yaml).

• Check for alternative installation instructions on the various python environments and platforms at the pandas site.

See doc(install) for more details

Before installing it, make sure that there are no older versions left over. So run this console-command (using program(cmd.exe) in windows) until you cannot find any project installed:

$ pip uninstall fuefit                                      ## Use `pip3` if both python-2 & 3 are in PATH.

You can install the project directly from the PyPi repo the “standard” way, by typing the command(pip) in the console:

$ pip install fuefit

• If you want to install a pre-release version (the version-string is not plain numbers, but ends with alpha, beta.2 or something else), use additionally option(--pre).

• If you want to upgrade an existing installation along with all its dependencies, add also option(--upgrade) (or option(-U) equivalently), but then the build might take some considerable time to finish. Also there is the possibility the upgraded libraries might break existing programs(!) so use it with caution, or from within a virtualenv (isolated Python environment).

• To install an older version issue the console-command:

  $ pip install fuefit=1.1.1                    ## Use `--pre` if version-string has a build-suffix.

• To install it for different Python environments, repeat the procedure using the appropriate program(python.exe) interpreter for each environment.

• Tip

  To debug installation problems, you can export a non-empty envvar(DISTUTILS_DEBUG) and distutils will print detailed information about what it is doing and/or print the whole command line when an external program (like a C compiler) fails.

After a successful installation, it is important that you check which version is visible in your envvar(PATH), so type this console-command:

$ fuefit --version
0.0.6

Installing from sources (for advanced users familiar with git)

If you download the sources you have more options for installation. There are various methods to get hold of them:

• Download and extract a release-snapshot from github.

• Download and extract a sdist source distribution from PyPi repo.

• Clone the git-repository at github. Assuming you have a working installation of git you can fetch and install the latest version of the project with the following series of commands:

  $ git clone "https://github.com/ankostis/fuefit.git" fuefit.git
  $ cd fuefit.git
  $ python setup.py install                                 ## Use `python3` if both python-2 & 3 installed.

When working with sources, you need to have installed all libraries that the project depends on. Particularly for the latest WinPython environments (Windows / OS X) you can install the necessary dependencies with:

$ pip install -r requirements/execution.txt .

The previous command installs a “snapshot” of the project as it is found in the sources. If you wish to link the project’s sources with your python environment, install the project in development mode:

$ python setup.py develop

Note

This last command installs any missing dependencies inside the project-folder.

Anaconda install

The installation to Anaconda (ie OS X) works without any differences from the pip procedure described so far.

To install it on miniconda environment, you need to install first the project’s native dependencies (numpy/scipy), so you need to download the sources (see above). Then open a bash-shell inside them and type the following commands:

$ coda install `cat requirements/miniconda.txt`
$ pip install lmfit             ## Workaround lmfit-py#149
$ python setup.py install
$ fuefit --version
0.0.6

Usage

Excel usage

Attention!

Excel-integration requires Python 3 and Windows or OS X!

In Windows and OS X you may utilize the xlwings library to use Excel files for providing input and output to the program.

To create the necessary template-files in your current-directory, type this console-command:

$ fuefit --excel

Type fuefit --excel {file_path} if you want to specify a different destination path.

In windows/OS X you can type fuefit --excelrun and the files will be created in your home-directory and the Excel will immediately open them.

What the above commands do is to create 2 files:

file(FuefitExcelRunner{#}.xlsm)

The python-enabled excel-file where input and output data are written, as seen in the screenshot below:

After opening it the first tie, enable the macros on the workbook, select the python-code at the left and click the Run Selection as Pyhon button; one sheet per vehicle should be created.

The excel-file contains additionally appropriate VBA modules allowing you to invoke Python code present in selected cells with a click of a button, and python-functions declared in the python-script, below, using the mypy namespace.

To add more input-columns, you need to set as column Headers the json-pointers path of the desired model item (see python-usage below,).

file(FuefitExcelRunner{#}.py)

Python functions used by the above xls-file for running a batch of experiments.

The particular functions included reads multiple vehicles from the input table with various vehicle characteristics and/or experiment coefficients, and then it adds a new worksheet containing the cycle-run of each vehicle . Of course you can edit it to further fit your needs.

Note

You may reverse the procedure described above and run the python-script instead:

$ python FuefitExcelRunner.py

The script will open the excel-file, run the experiments and add the new sheets, but in case any errors occur, this time you can debug them, if you had executed the script through LiClipse, or IPython!

Some general notes regarding the python-code from excel-cells:

• An elaborate syntax to reference excel cells, rows, columns or tables from python code, and to read them as class(pandas.DataFrame) is utilized by the Excel . Read its syntax at func(~fuefit.excel.FuefitExcelRunner.resolve_excel_ref).

• On each invocation, the predefined VBA module pandalon executes a dynamically generated python-script file in the same folder where the excel-file resides, which, among others, imports the “sister” python-script file. You can read & modify the sister python-script to import libraries such as ‘numpy’ and ‘pandas’, or pre-define utility python functions.

• The name of the sister python-script is automatically calculated from the name of the Excel-file, and it must be valid as a python module-name. Therefore: * Do not use non-alphanumeric characters such as spaces(` ), dashes(-) and dots(.`) on the Excel-file. * If you rename the excel-file, rename also the python-file, or add this python import <old_py_file> as mypy`

• On errors, a log-file is written in the same folder where the excel-file resides, for as long as the message-box is visible, and it is deleted automatically after you click ‘ok’!

• Read http://docs.xlwings.org/quickstart.html

Cmd-line usage

Example command:

fuefit -v\
  -I fuefit/test/FuelFit.xlsx sheetname+=0 header@=None names:='["p","rpm","fc"]' \
  -I fuefit/test/engine.csv file_frmt=SERIES model_path=/engine header@=None \
  -m /engine/fuel=petrol \
  -O ~t2.csv model_path=/fitted_eng_points    index?=false \
  -O ~t2.csv model_path=/mesh_eng_points      index?=false \
  -O ~t.csv model_path= -m /params/plot_maps@=True

Python usage

The most powerful way to interact with the project is through a python REPL (Read-Eval-Print Loop). So fire-up a command(python) or command(ipython) shell and first try to import the project just to check its version:

code-block:

>>> import fuefit

>>> fuefit.__version__                ## Check version once more.
'0.0.6'

>>> fuefit.__file__                   ## To check where it was installed.         # doctest: +SKIP
/usr/local/lib/site-package/fuefit-...

If the version was as expected, take the base-model and extend it with your engine-data (strings and numbers):

>>> from fuefit import datamodel, processor

>>> inp_model = datamodel.base_model()
>>> inp_model.update({
...     "engine": {
...         "fuel":     "diesel",
...         "p_max":    95,
...         "n_idle":   850,
...         "n_rated":  6500,
...         "stroke":   94.2,
...         "capacity": 2000,
...         "bore":     None,       ##You do not have to include these,
...         "cylinders": None,      ##  they are just for displaying some more engine properties.
...     }
... })

>>> import pandas as pd
>>> df = pd.read_excel('fuefit/test/FuelFit.xlsx', 0, header=None, names=["n","p","fc"])
>>> inp_model['measured_eng_points'] = df

For information on the accepted model-data, check both its JSON-schema at func(~fuefit.datamodel.model_schema), and the func(~fuefit.datamodel.base_model):

Next you have to validate it against its JSON-schema:

>>> datamodel.validate_model(inp_model, additional_properties=False)

If validation is successful, you may then feed this model-tree to the mod(fuefit.processor), to get back the results:

>>> out_model = processor.run(inp_model)

>>> print(datamodel.resolve_jsonpointer(out_model, '/engine/fc_map_coeffs'))
a            164.110667
b           7051.867419
c          63015.519469
a2             0.121139
b2          -493.301306
loss0      -1637.894603
loss2   -1047463.140758
dtype: float64

>>> print(out_model['fitted_eng_points'].shape)
(262, 11)

Hint

You can always check the sample code at the Test-cases and in the cmdline tool mod(fuefit.__main__).

Fitting Parameterization

The ‘lmfit’ fitting library can be parameterized by setting/modifying various input-model properties under /params/fitting/.

In particular under /params/fitting/coeffs/ you can set a dictionary of coefficient-name –> class(lmfit.parameters.Parameter) such as min/max/value, as defined by the lmfit library (check the default props under func(fuefit.datamodel.base_model()) and the example columns in the ExcelRunner).

info::

http://lmfit.github.io/lmfit-py/parameters.html#Parameters

Contribute

This project is hosted in github. To provide feedback about bugs and errors or questions and requests for enhancements, use github’s Issue-tracker.

Sources & Dependencies

To get involved with development, you need a POSIX environment to fully build it (Linux, OSX, or Cygwin on Windows).

Liclipse IDE

Within the sources there are two sample files for the comprehensive LiClipse IDE:

• file(eclipse.project)

• file(eclipse.pydevproject)

Remove the eclipse prefix, (but leave the dot(.)) and import it as “existing project” from Eclipse’s File menu.

Another issue is due to the fact that LiClipse contains its own implementation of Git, EGit, which badly interacts with unix symbolic-links, such as the file(docs/docs), and it detects working-directory changes even after a fresh checkout. To workaround this, Right-click on the above file Properties --> Team --> Advanced --> Assume Unchanged

Development team

• Kostis Anagnostopoulos (software design & implementation)

• Georgios Fontaras (methodology inception, engineering support & validation)

Contributing Authors

• Stefanos Tsiakmakis

• Biagio Ciuffo

• Alessandro Marotta

Authors would like to thank experts of the SGS group for providing useful feedback.

Indices

rubric:

CM
    `Mean Piston Speed <https://en.wikipedia.org/wiki/Mean_piston_speed>`_,
    a measure for the engines operating speed [m/sec]

BMEP
    `Brake Mean Effective Pressure <https://en.wikipedia.org/wiki/Mean_effective_pressure>`_,
    a valuable measure of an engine's capacity to do work that is independent of engine displacement) [bar]

PMF
    *Available Mean Effective Pressure*, the maximum mean effective pressure calculated based on
    the energy content of the fuel [bar]

JSON-schema
    The `JSON schema <http://json-schema.org/>`_ is an `IETF draft <http://tools.ietf.org/html/draft-zyp-json-schema-03>`_
    that provides a *contract* for what JSON-data is required for a given application and how to interact
    with it.  JSON Schema is intended to define validation, documentation, hyperlink navigation, and
    interaction control of JSON data.
    You can learn more about it from this `excellent guide <http://spacetelescope.github.io/understanding-json-schema/>`_,
    and experiment with this `on-line validator <http://www.jsonschema.net/>`_.

JSON-pointer
    JSON Pointer(rfc(``6901``)) defines a string syntax for identifying a specific value within
    a JavaScript Object Notation (JSON) document. It aims to serve the same purpose as *XPath* from the XML world,
    but it is much simpler.