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README.md

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+# Model Predictive Control (MPC) for battery operation
+
+This repository contains code for running Li-Ion battery simulation with ageing effects, and its control for optimal charging and load peak shaving.
+Battery model is implemented in Modelica, thus it achieves high perfomance. 
+For easier prototyping, battery model can be exported into Functional Mockup Unit (FMU) and used inside any Functional Mockup Inteface (FMI)-compliant software. In our case, it is
+used in Python for battery operation using MPC via PyFMI.
+
+# Project structure
+- **ModelicaSrc/BatteryWithFullCycle.mo** - contains implementation of Thevenin-based battery model with ageing effects ([paper](10.1109/MELECON53508.2022.9842961))  
+- **python_libs/mpc_optimization/** - contains implementation of Model Predictive Control, where model is used as FMU ([UML architecture](https://github.com/Midren/MPC_for_battery_operation/blob/master/docs/mpc_optimization_diagram.png))  
+- **python_libs/battery_optmizations/** - contains code for optimal battery charging and load peak shaving (WIP)  
+- **notebooks/** - jupyter notebooks with experiments
+
+# Usage
+
+All the work is done under the docker (JModelica is supported only under Linux), thus the simplest way is just to use it.
+Dockerfile contains necessary environment for running the code, and Dockerfile-dev contains the environment with my neovim/zsh configuration.  
+Makefile has commands for build and run the docker:
+```sh
+make docker-build # or make docker-build-dev
+make jupyter # or make vim 
+```
+
+# Citing
+
+If you use this repo, please cite:
+
+```
+@article{milishchuk_bogodorova_2022, 
+    title={Thevenin-based battery model with ageing effects in Modelica}, 
+    DOI={10.1109/melecon53508.2022.9842961}, 
+    journal={2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)}, 
+    author={Milishchuk, Roman and Bogodorova, Tetiana}, 
+    year={2022}
+} 
+```