Skip to content

Using machine learning to estimate the state of charge of lithium ion batteries for electric vehicles

Notifications You must be signed in to change notification settings

kanocarra/smart-battery-management-system

Repository files navigation

smart-battery-management-system

Using machine learning to estimate the state of charge of lithium ion batteries for electric vehicles

Code is for the STM32 F334 module.

Programming has only been tested in Mac OSX. It will mostly likely work on Linux with no problems (yay for Unix based OS) but sorry Windows people, you're on your own.

Steps to build Makefile

  1. Generate the make file using the STM32 Cube generator for the STM32 F334 chip

  2. Install gcc-arm-embedded library

brew install Caskroom/cask/gcc-arm-embedded
  1. Edit the Makefile generated to ensure BINPATH points to the location of gcc-arm-embedded

  2. Compile the files with

make
  1. Install st-link
brew install stlink
  1. Add a flash command to Makefile that programs STM32
flash: $(BUILD_DIR)/$(TARGET).bin
	st-flash --reset write $< 0x8000000
  1. Program the STM32 with
make flash 

If all goes well then everything is set up correctly!

To program STM32f334

  1. First clean up any old build files with
make clean
  1. Then run the build
make
  1. Then you can program the chip using
make flash

Then all should work, yay!

About

Using machine learning to estimate the state of charge of lithium ion batteries for electric vehicles

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages