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OVMS (Open Vehicles) is a great hardware module that connects to my Nissan LEAF and allows me to perform remote functions like turning on the climate control, checking charge status, range, etc. I’m also a big fan of Home Assistant, and have almost everything in my house hooked up to it. There is no official (or unofficial) integration for OVMS to Home Assistant. However, OVMS has a HTTP API, and Home Assistant supports generic RESTful sensors.

Read on to find out how to hook up your OVMS module to Home Assistant!

Getting Started

Firstly, you’ll need your OVMS module to be hooked up, configured correctly, and working with the default OVMS app. Once your app is connected to your OVMS module and you can see live data coming through, it’s time to move on.

Generating an API Token

You’ll need to generate an API Token from the openvehicles.com API. To do this, you’ll need to open up the Terminal on your computer. Once there, type the command below and hit enter, replacing <USERNAME> and <PASSWORD> with your OVMS username/password that you use to login to openvehicles.com.

curl --location --request GET 'http://api.openvehicles.com:6868/api/token?username=<USERNAME>&password=<PASSWORD>'

After you run that command, you will see an output on your screen similar to the one below. You’ll need to copy your API Token (highlighted in bold) to a safe place.

{"application":"notspecified","owner":"<YOUR_USERNAME>","permit":"auth","purpose":"notspecified","token":"RiVINShnbS0wNG5tJUlNYUZJbUNeR1NcYSdwM0l7aDpWOyE2QkQxSCwrLWh8Ow"}

Find your list of metrics

Now you need to find a list of all the metrics that you want available in home assistant. There are 3 main collections of metrics that the OVMS API makes available. These are Status, Charging, and Location. If you have a 2012 era Nissan LEAF like I do, skip below and copy my config file. Otherwise, read on.

To find all of the available metrics, run the following commands in your terminal. Make a note of all the metrics that you want available in Home Assistant. Be sure to replace <USERNAME> with your username, <VEHICLE_ID> with your OVMS vehicle ID, and <YOUR_API_TOKEN> with the token you retrieved earlier.

Status

curl --location --request GET 'http://api.openvehicles.com:6868/api/status/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_TOKEN>'

Charging

curl --location --request GET 'http://api.openvehicles.com:6868/api/charge/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_TOKEN>'

Location

curl --location --request GET 'http://api.openvehicles.com:6868/api/location/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_TOKEN>'

Home Assistant Configuration

Now you’ll need to configure Home Assistant to retrieve data from the OVMS API and pull out the metrics that you want. Add a configuration to your configuration.yaml in the sensor section like below. You can update the scan_interval to whatever you’d like, but be considerate and don’t go lower than when your OVMS sends updates, or at a minimum every 60 seconds.

sensor:
  - platform: rest
    scan_interval: 120
    name: car_status
    resource: http://api.openvehicles.com:6868/api/status/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
    value_template: "{{ value_json.soc }}"
    json_attributes:
      - soh
      - soc
      - etc...

  - platform: rest
    scan_interval: 120
    name: car_location
    resource: http://api.openvehicles.com:6868/api/location/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
    value_template: "{{ value_json.longitude }},{{ value_json.latitude }}"
    json_attributes:
      - longitude
      - latitude
      - etc...

  - platform: rest
    scan_interval: 60
    name: car_charging
    resource: http://api.openvehicles.com:6868/api/status/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
    value_template: "{{ value_json.chargestate }}"
    json_attributes:
      - battvoltage
      - cac100
      - carawake
      - caron
      - etc...

Save and validate

Save the new configuration, and use the handy “Check Configuration” button on the Configuration > Server Controls page. If there are no errors, then restart your home assistant server.

When the server starts back up, you should see some new entities called sensor.car_status, sensor.car_location, and sensor.car_charging. Use this in your automations or expose them via HomeKit like I did! Check below for my full configuration, including HomeKit friendly template sensors (although HomeKit does not support this very well as it doesn’t have native EV support).

HomeKit Example

You can see what this looks like in the screenshot from my iPhone below. I’ve configured the SoC as a humidity sensor so it reads as a percentage, and the range as an illuminance sensor. Unfortunately, HomeKit lacks an EV entity type, so this is the best I could come up with.

If you name the sensors something appropriate, you can even ask Siri to tell you the state of charge or range, if you can deal with the annoying response as it thinks they’re different types of sensors. Hopefully Apple adds an EV entity to HomeKit in the future, so a proper integration can be made!

Example config for a 2012 Nissan LEAF

- sensor:
    - name: car_soc_homekit
      state: "{{ state_attr('sensor.car_status', 'soc') }}"
      icon: "mdi:car-electric"
      device_class: humidity
      unit_of_measurement: "%"

    - name: car_range_homekit
      state: '{{ (float(state_attr("sensor.car_status", "estimatedrange")) * 1.25) | int }}'
      icon: "mdi:speedometer-slow"
      device_class: illuminance

    - name: car_soc
      state: "{[ state_attr('sensor.car_status', 'soc') }}"
      icon: "mdi:car-electric"
      device_class: battery

    - name: car_range
      state: '{{ (float(state_attr("sensor.car_status", "estimatedrange")) * 1.25) | int }}'
      icon: "mdi:speedometer-slow"

- binary_sensor:
    - name: car_charging
      state: "{{ state_attr('sensor.car_status', 'charging') }}"
      icon: "mdi:ev-station"

- platform: rest
  scan_interval: 60
  name: car_status
  resource: http://api.openvehicles.com:6868/api/status/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
  value_template: "{{ value_json.soc }}"
  json_attributes:
    - alarmsounding
    - bt_open
    - cac100
    - carawake
    - carlocked
    - caron
    - chargestate
    - charging
    - charging_12v
    - cooldown_active
    - cp_dooropen
    - estimatedrange
    - fl_dooropen
    - fr_dooropen
    - handbrake
    - idealrange
    - idealrange_max
    - mode
    - odometer
    - parkingtimer
    - pilotpresent
    - soc
    - soh
    - speed
    - staleambient
    - staletemps
    - temperature_ambient
    - temperature_battery
    - temperature_charger
    - temperature_motor
    - temperature_pem
    - tr_open
    - tripmeter
    - units
    - valetmode
    - vehicle12v
    - vehicle12v_current
    - vehicle12v_ref

- platform: rest
  scan_interval: 60
  name: car_location
  resource: http://api.openvehicles.com:6868/api/location/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
  value_template: "{{ value_json.longitude }},{{ value_json.latitude }}"
  json_attributes:
    - altitude
    - direction
    - drivemode
    - energyrecd
    - energyused
    - gpslock
    - invefficiency
    - invpower
    - latitude
    - longitude
    - power
    - speed
    - stalegps
    - tripmeter

- platform: rest
  scan_interval: 30
  name: car_charging
  resource: http://api.openvehicles.com:6868/api/status/<VEHICLE_ID>?username=<USERNAME>&password=<YOUR_API_KEY>
  value_template: "{{ value_json.chargestate }}"
  json_attributes:
    - battvoltage
    - cac100
    - carawake
    - caron
    - charge_estimate
    - charge_etr_full
    - charge_etr_limit
    - charge_etr_range
    - charge_etr_soc
    - charge_limit_range
    - charge_limit_soc
    - chargeb4
    - chargecurrent
    - chargeduration
    - chargekwh
    - chargelimit
    - chargepower
    - chargepowerinput
    - chargerefficiency
    - chargestarttime
    - chargestate
    - chargesubstate
    - chargetimermode
    - chargetimerstale
    - chargetype
    - charging
    - charging_12v
    - cooldown_active
    - cooldown_tbattery
    - cooldown_timelimit
    - cp_dooropen
    - estimatedrange
    - idealrange
    - idealrange_max
    - linevoltage
    - mode
    - pilotpresent
    - soc
    - soh
    - staleambient
    - staletemps
    - temperature_ambient
    - temperature_battery
    - temperature_charger
    - temperature_motor
    - temperature_pem
    - units
    - vehicle12v
    - vehicle12v_current
    - vehicle12v_ref

I’ve been enjoying my hardware related projects recently. I’ve even had a few people ask about buying some of them so I decided to launch an online store. Being a sucker for cute animals and a proud Australian, I wanted to name my store after a cute, native Australian animal. Hence, the name Koala Creations was born. You can access my online store by clicking here.

I’ve also setup a store on Tindie if you’d prefer to purchase things through a marketplace style site (like Etsy). Click here to go my Tindie store.

I don’t have a huge range of products available at the moment, but stay tuned as my awesome 18650 battery tester is in it’s final stages of development, and my 12v power distribution board for electric vehicle (EV) conversions is coming along nicely.

Please check it out and let me know if there’s anything else you’d like to see me make! 😄

For those who don’t know, I’ve been blogging about my electric motorcycle conversion over at https://ebandit.bike. I’ve gotten back into the swing of things now that a lot of places are opening back up after COVID mandated shut downs. I’ve put together a really detailed post explaining all about the BMS, the motor I selected and the ELV system. You can read all about it on my other blog right here.

I’ve been an eager smart home (or home automation) enthusiast for a number of years now. My end goal is always changing, but it’s generally been to automate as many things as possible and make it as convenient as possible to control all of my lighting and appliances. My smart home system has grown to be quite complex so I’ve started documenting it.

To start with, I’ve put together a system diagram showcasing all of the different components and how everything is connected.

Here is a quick summary of all the different protocols and the different components that rely on them.

ZigBee – CC2531 Dongle (zigbee2mqtt)

Zigbee2mqtt is a fantastic open source project. It aims to bring together all the products from various companies so they can all use a single hub. Currently most vendors have a proprietary hub and there’s little compatibility between. This is surprising given ZigBee is an open standard just like WiFi. Zigbee2mqtt has a set of converters that allow you to add support for almost any device and expose a control/status API over MQTT.

ZigBee is “created on IEEE’s 802.15.4 using the 2.4GHz band and a self-healing true mesh network”. It’s especially ideal for sensor and IoT networks as it is a true mesh network that re-organises itself and relays messages between nodes. It’s also extremely low powered which makes it great for tiny battery powered sensors.

I’m slowly moving all of my ZigBee devices onto this network. This allows me to benefit from having less hubs and a bigger, more reliable ZigBee network. Most of my fixed LED downlights have ZigBee light switches that act as repeaters as they’re always powered.

ZigBee – Phillips Hue

Although they’re great, I’m migrating away from the Phillips hue lineup to the IKEA range for consistency reasons. Otherwise, the hue range is the best quality and functioning smart bulbs I’ve used.

ZigBee – IKEA TRÅDFRI

IKEA’s range of smart lighting products is absolutely fantastic. They are incredibly good value, great quality and work well. You can get a dimmable smart bulb for about $15 AUD!

IPv4 – WiFi/Ethernet

All of the ZigBee hubs connect back to hass.io (or home assistant) over a standard IP network using WiFi or Ethernet. There are also various other devices like my air purifier, robot vacuum cleaner, smart thermostat and a couple of WiFi based relays (sonoff). I try to avoid adding WiFi based IoT devices as a lot of them have serious security vulnerabilities. ZigBee is generally much more secure as a breach from any ZigBee device generally can’t give access to the entire IP network.

Raspberry Pi 4

Home Assistant (or hass.io) runs on my raspberry 4 and exposes all of the devices in my smart home system to HomeBridge. This makes everything available to the Apple ecosystem via HomeKit. This allows me to use Siri or the Home app on my watch, iPhone, MacBook, iPad or HomePod to control everything that’s part of my smart home system. This is really convenient and Siri is now the primary way I interact with my smart home system and control lights/other devices.

The Raspberry Pi 4 also hosts several services such as a plex media server and download server. It’s mapped to our NAS which has 8tb of network accessible storage for media, backups and other files.

Conclusion

My smart home system is a lot of fun to build and maintain but it’s not for everyone. Hopefully this post has given you some ideas on how to get started or improve your own smart home.

The sonoff WiFi relays have arrived.  I ended up buying ten of them and 3 motion sensors.  My first impression is that they’re tiny and solid.  They’re much smaller than I thought, which is a good thing!  The case they come in is perfect for mounting inline with something and neatly hides the exposed wires.  For comparison, you can see my old LG G4 phone next to it.

On the inside

On the inside, they look pretty good.  The soldering is done well and the gaps between the mains traces is reassuring.  As you can also see from the picture below there are a few header pins.  These are the programming pins.  Itead has been nice enough to breakout the programming pins into headers to make it easier to reprogram with your own code.

 

Reliability

I’ve currently had one set up on my desk lamp for the last couple of days.  It has been rock solid and hasn’t experienced any drop outs or glitches.  This was running their stock firmware which allowed me to connect it to their app.  Although I have no intention of continuing to use their app it is miles ahead of the Belkin system.  For example, switching it on or off happens via the internet almost instantaneously.  However the Belkin’s system sometimes takes 10 seconds!

I’ve just finished a recent side project with my friend Kendrick. (his GitHub)  We built an autonomous car that you can teach how to drive, then it drives around by itself.  I did all of the hardware/arduino software and Kendrick did all of the machine learning software.  He called his project Suiron and it’s available on GitHub here.  The code running on the arduino is called car-controller and is available on my GitHub here.

Now that you’ve got the links feel free to go and have a look.  Work through the code and try to figure out how it works.  I’ll try to briefly cover the software here but my main focus will be on the hardware.  One thing to note are the open source licenses, all my stuff is GPL and Kendrick’s Suiron project is MIT.

This post is more intended as an overview of how the whole thing works.  If I get time I might turn it into a tutorial on how to get it working yourself.

Before we begin here is a short video of it in action.

Now onto the fun stuff!  How does it work?

The Hardware

These are the main components used.

1) Remote Control Car – we used this car (link) but anything of similar size will work.  As long as it has a standard ESC and Steering Servo.  It comes with a remote control, battery and charger to start with.  I recommend buying a new remote control system. (link 5 below)

2) Inten NUC – The raspberry pi doesn’t really have enough power and is arm based.  An x86 based processor like the i5 in our NUC is much easier to use for machine learning purposes.  The exact one you use doesn’t matter.

3) Battery for NUC – A standard laptop battery bank was used to power it.  This one from kogan gives about 6-10 hours of runtime.  (link)

4) Logitech C920 Webcam – Buy from any reputable retailer.  You could probably use a cheaper model but we had better luck with a C920. (link)

4) Lens filters – if you are operating in any sunlight, you will want a Polarising and ND (Neutral Density) filter.  The camera just can’t cope with the harsh sunlight and shadows so these filters help bring the conditions into something much better.  A variable ND is great as it let’s you adjust the “darkness” level.

5) Radio control system – if you intend on doing lots of this stuff then get an FrSky TARANIS.  You won’t be disappointed.  Otherwise, a turnigy 9XR will work just as good.  Make sure you get a receiver too if it isn’t included.

6) You’ll also need an arduino.  I like the Arduino Nano’s because they are super cheap and have on board USB.

If you want some specific help choosing components just leave a comment.

I won’t go into details on how to wire everything as this isn’t a tutorial.  However, If you need some help drop a comment below.  I suggest you learn how an ESC (electronic speed controller) works together with a motor, receiver, servo and battery.  This is a standard setup on normal remote control cars.  Once you understand that you should look at arduino’s and how to use them to blink lights and read inputs.  Read through the arduino code and the wiring should be pretty self explanatory.

How it all fits together

It’s up to you how you put everything together.  I recommend trying to keep everything as low as possible for better stability when driving.  The webcam needs to be mounted up high so it has a better chance of seeing the lane that it’s in.  I just used a square bit of balsa wood as it’s really light and strong, then glued the webcam to it.  Instead of explaining exactly how I mounted everything I’ll dump a few pictures here.  All the white things are 3D printed, but you could easily do it without a 3D printer.

Polarising/ND Filter

The importance of a polarising filter cannot be underestimated.  It reduces reflections and the harsh glare sometimes encountered.  In the image below (credit)  you can see how much of a difference a polarising filter can make.  Now water is a bit of an extreme example, but I chose that picture so it’s easier to demonstrate the difference.  In realty, where we’re operating the difference won’t be so obvious.

The neutral density filter is equally or more important than the polarising filter.  The ND filter is basically like sun glasses for the webcam.  The webcam doesn’t like really harsh light so it reduces the intensity of it without interfering with the image to much.  The picture below (credit wikipedia) shows how much better the right ND filter can make an image in harsh light.

 

I suggest making the lens filters removable as it will make the image to dark in lower lighting situations.  For example, it was perfect mid day but much to dark a few hours later just before dusk.  I made a simple mount that just uses an alligator clip to hold the filters in place.  The filters are both glued together then onto a small 3D printed right angle mount.

The Arduino

The diagram below shows how everything is hooked up.  Basically the arduino is the “brains of the hardware”.  It reads in the values from the R/C receiver (bottom left) and then decides what to do based on the mode channel.  Dig through the arduino code (link) and see exactly how.  Basically there are 3 modes, manual, autonomous and emergency stop.

In manual mode the arduino reads in the steering and motor values and passes it straight to the motor and steering servo.  In this mode with the right flag enabled, it also sends back over UART what those values are every time it receives a character.  (every time it receives prevents the serial buffer getting full and “lagging”) In autonomous mode the arduino reads inputs over UART from the NUC.  In this mode it receives two messages; steer,x and motor,x where x is the value you want to set it to.  It then writes those outputs to the steering servo or motor.  Finally, emergency stop kills the motor output and straightens the steering servo.  This emergency stop overrides any sort of manual or autonomous control.

The Machine Learning Part

This isn’t my expertise so I’ll briefly summarise what it’s doing.  (not really how it’s doing it, no one really knows)  We used a library called Tensor Flow.  It’s an Open Source machine learning library published by Google.  It’s open source and released under an Apache license.  It has a nice python and a “no nonsense” C++ api.

Collecting data

This is a really short summary of the whole process.  Each time a video frame is recorded Suiron (software on the NUC) asks car-controller (software on arduino) what the human operator is doing.  Remember, in manual mode the human operator is driving the car around.  Car-controller responds by sending the current steering and motor values back to Suiron.  Suiron takes these values and saves them along with a processed version of the frame.

This process happens at about 30Hz (or 30 times per second) for as long as you record data.  In the final model, we used about 20 minutes worth of training data.  That is 20 minutes of continuously driving around the track.  It may not seem like a lot but it’s repetitive very quickly. 😉  In reality, 20 minutes is no where near enough data.  It works great on this particular track with similar lighting conditions but would likely fail if the conditions changed to much.

Training data

Again, I’m not an exert at this but I’ll try to briefly explain how the training works.  Convolutional Neural Networks (CNNs) are weird in the way they work.  It’s impossible to know exactly how or why a CNN works.  Basically, we’re giving Tensor Flow the frame and two numbers. (steering and motor)  Then we’re asking it to work out how the frame relates to those two numbers.  After giving it hundreds of thousands of examples (frames) it can try to generalise a model.

Because of the amount of computing power required it takes a very long time to train a good model.  Due to the type of calculations it has to do, Tensor Flow runs much faster on a dedicated GPU.  With only 20 minutes of data our model took half a day to train properly.  The training took place in a desktop with a borrowed GTX980, a GPU that’s towards the higher end of consumer graphics cards.

Using the model

You can see it in action in the gif below.  The blue line is what the model thinks it should do, the green line is what I actually did when I was steering it.  Note that this data was not included in the training set, this is to ensure the model works with other data.

Once it has been trained we can then use the model.  Basically, what happens is we collect just a frame from the webcam.  Then we pass it to Tensor Flow and ask it to run it through the model.  The model then spits out what it thinks our two values should be, one for steering and one for throttle.  At the moment the throttle is unused and it runs at a constant speed.  However we thought we’d include it just in case we wanted to use it in the future.

Update: Clive from hobbyhelp.com reached out to me after seeing this. He’s got a pretty cool “Ultimate beginners guide to RC cars” article on his website here. I recommend checking it out if you want to get started doing something similar to this project.