PyBaMM | Fast and flexible physics-based battery models in Python

by pybamm-team Python Version: 24.1rc2 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PyBaMM Summary

PyBaMM is a Python library typically used in Simulation applications. PyBaMM has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install PyBaMM' or download it from GitHub, PyPI.

PyBaMM (Python Battery Mathematical Modelling) solves physics-based electrochemical DAE models by using state-of-the-art automatic differentiation and numerical solvers. The Doyle-Fuller-Newman model can be solved in under 0.1 seconds, while the reduced-order Single Particle Model and Single Particle Model with electrolyte can be solved in just a few milliseconds. Additional physics can easily be included such as thermal effects, fast particle diffusion, 3D effects, and more. All models are implemented in a flexible manner, and a wide range of models and parameter sets (NCA, NMC, LiCoO2, ...) are available. There is also functionality to simulate any set of experimental instructions, such as CCCV or GITT, or specify drive cycles.

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PyBaMM has a low active ecosystem.

It has 638 star(s) with 332 fork(s). There are 24 watchers for this library.

There were 6 major release(s) in the last 6 months.

There are 84 open issues and 1030 have been closed. On average issues are closed in 136 days. There are 22 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyBaMM is 24.1rc2

Quality

PyBaMM has 0 bugs and 0 code smells.

Security

PyBaMM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PyBaMM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyBaMM is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

PyBaMM releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

PyBaMM saves you 24847 person hours of effort in developing the same functionality from scratch.

It has 58622 lines of code, 3525 functions and 657 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed PyBaMM and discovered the below as its top functions. This is intended to give you an instant insight into PyBaMM implemented functionality, and help decide if they suit your requirements.

Solve the model
Return the maximum of child elements
Builds the model
Return the value of key
Generate a JVM model from a model
Convert symbol id to julia variable name
Convert a symbol to a Julian formula
Converts a variable to a string
Return LaTeX representation of the model
R Calculate the dimensional parameters
Returns a dictionary of variables
Calculate a dictionary of variables
Integrate model
Set up the experiment
Processes the given unprocessed model
Simple division function
Calculate the variables for the given variables
Simplify multiplication
Discretise a model
Integrate a model
Get a julia function from a symbol
Solve a model
Integrate a differential equation
Set dimensionless parameters
Integrate the model with the given parameters
Performs a step on the model

Get all kandi verified functions for this library.

PyBaMM Key Features

No Key Features are available at this moment for PyBaMM.

PyBaMM Examples and Code Snippets

No Code Snippets are available at this moment for PyBaMM.

Community Discussions

Trending Discussions on PyBaMM

Use Dask to run PyBaMM battery simulations in parallel

QUESTION

Use Dask to run PyBaMM battery simulations in parallel

Asked 2021-Oct-14 at 18:06

I'm using the PyBaMM package to model battery cells and I would like to use Dask to run several simulations in parallel. The example below is my attempt to use dask.delayed. The Dask approach is slower than the non-Dask approach. Is there a better way to use Dask for this example? Should I setup a Dask Client() to run the simulations in parallel? I'm running this example on my local machine but I would eventually like to run a similar example on a cluster.

Elapsed times from running the example on an 8-core MacBook Pro are given below. Comment out the appropriate section in main() to run with or without Dask.

Example Elapsed time No Dask 8.02 seconds Dask 8.74 seconds ...

ANSWER

Answered 2021-Oct-14 at 18:06

Based on the suggestions from @rrpelgrim, I implemented a Client() object which appears to improve the parallel execution of my example code by using the distributed scheduler. The modified example is shown below. You can compare the elapsed time with and without Dask by commenting out the appropriate section in main(). Elapsed times using an 8-core CPU are given in the table.

Example Elapsed time No Dask 8.57 seconds Dask 3.83 seconds

Source https://stackoverflow.com/questions/69562400

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install PyBaMM

You can install using 'pip install PyBaMM' or download it from GitHub, PyPI.
You can use PyBaMM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

If you'd like to help us develop PyBaMM by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at these guidelines first.

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