fuefit fits engine-maps on physical parameters

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]:

\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}}

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

   a, b, c, a2, b2, loss0, loss2
   

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`.

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:

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

$ fuefit --version
0.0.7-alpha.1

$ 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 \

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

>>> 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

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

Install

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

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
0.0.7-alpha.1

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

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
0.0.7-alpha.1

Usage

Excel usage

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 :samp:`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 :menuselection:`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 :ref:`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.

$ python FuefitExcelRunner.py

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 :samp:`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 :abbr:`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:

>>> import fuefit

>>> fuefit.__version__                ## Check version once more.
'0.0.7-alpha.1'

>>> 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 :term:`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)

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).

.. Seealso::
    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).

Development team

• Kostis Anagnostopoulos (software design & implementation)
• Georgios Fontaras (methodology inception, engineering support & validation)

Contributing Authors

• Stefanos Tsiakmakis
• Biagio Ciuffo

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

Indices

.. glossary::

    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.