tesla_charger.py

from dateutil import parser, tz
from datetime import datetime, timezone

MAX_TIME_RANGE = 12 * 60  # how many minutes in the future to look at forecasts
BATTERY_CAPACITY = 82  # kWh - Tesla Model 3 Performance (2021)
CHARGE_RATE = 11  # kWh - Gen 3 Wall Connector (3 Phase 16A)
LOCAL_TZ = tz.gettz('Australia/Sydney')


def get_price_forecast():
    # initialise prices with current price
    prices = [state.getattr(sensor.amber_general_price)]
    forecasts = sorted(sensor.amber_general_forecast.forecasts, key=lambda x: parser.parse(x['start_time']))
    times_seen = 0
    while len(forecasts) and (times_seen < MAX_TIME_RANGE):
        forecast = forecasts.pop(0)
        times_seen += forecast['duration']
        prices.append(forecast)

    return prices


def format_charge_spots(charge_spots):
    spots = []
    for spot in charge_spots:
        spots.append({
            'start_time': parser.parse(spot['start_time']).astimezone(LOCAL_TZ),
            'end_time': parser.parse(spot['end_time']).astimezone(LOCAL_TZ),
            'per_kwh': spot['per_kwh'],
        })
    return spots


@state_trigger("sensor.amber_general_forecast", "binary_sensor.tesla_plugged_in == 'on'") # trigger: if our price forecast changes
@state_active("input_boolean.tesla_optimal_charging == 'on' and device_tracker.venom == 'home'") # condition: car is at home
def cheapest_charging_strategy(**kwargs):
    charge_time_remaining = 0.0
    # is the car drawing power from the charger?
    if float(sensor.tesla_power) == 0.0:
        # need to calculate remaining charge time ourselves.
        current_battery = float(sensor.tesla_battery_level)
        desired_battery = float(sensor.tesla_charge_limit_soc)
        charge_time_remaining = (((((desired_battery - current_battery) / 100) * BATTERY_CAPACITY) / CHARGE_RATE) * 60)
    else:
        charge_time_remaining = float(sensor.tesla_time_to_full_charge) * 60

    # do we actually need to charge the car?
    if charge_time_remaining <= 0:
        state.set('sensor.tesla_charging_strategy', value='stopped', new_attributes={
            'next_start_time': None,
            'next_end_time': None,
            'reason': 'fully charged',
        })
        return

    # sort prices by lowest $/kwh, prioritising earlier slots over later slots
    prices = sorted(get_price_forecast(), key=lambda x: (float(x['per_kwh']), parser.parse(x['start_time'])))
    charge_spots = []
    allocated_charge_time = 0.0

    for price in prices:
        allocated_charge_time += price['duration']
        charge_spots.append(price)
        # we have enough slots, stop.
        if allocated_charge_time >= charge_time_remaining:
            break

    # sort charge spots by earliest time
    charge_spots = sorted(charge_spots, key=lambda x: parser.parse(x['start_time']))
    start_time = parser.parse(charge_spots[0]['start_time'])
    end_time = parser.parse(charge_spots[0]['end_time'])
    kwh = charge_spots[0]['per_kwh']

    # are we currently in a selected slot?
    if start_time < datetime.now(timezone.utc) <= end_time:
        switch.venom_charger_switch.turn_on()
        if sensor.tesla_charging_strategy == 'stopped':
            notify.phones(title="Tesla Charger",
                          message="Charging Started. Cost {}c/kWh.".format(kwh))
        state.set('sensor.tesla_charging_strategy', value='started', new_attributes={
            'charge_time_remaining': charge_time_remaining,
            'next_start_time': start_time.astimezone(LOCAL_TZ),
            'next_end_time': end_time.astimezone(LOCAL_TZ),
            'price_kwh': kwh,
            'reason': 'cheap',
            'slots': format_charge_spots(charge_spots),
        })
    else:
        switch.venom_charger_switch.turn_off()
        if sensor.tesla_charging_strategy == 'started':
            notify.phones(title="Tesla Charger",
                          message="Charging Stopped. Cost {}c/kWh. Starting again at {}".format(kwh, start_time.astimezone(LOCAL_TZ).strftime("%-I:%M%p").lower()))

        state.set('sensor.tesla_charging_strategy', value='stopped', new_attributes={
            'charge_time_remaining': charge_time_remaining,
            'next_start_time': start_time.astimezone(LOCAL_TZ),
            'next_end_time': end_time.astimezone(LOCAL_TZ),
            'price_kwh': sensor.amber_general_price,
            'reason': 'too expensive',
            'slots': format_charge_spots(charge_spots),
        })

Hey Steve. It's using pyscript which is a custom component of the fantastic HomeAssistant project.

Feel free to fork it and have a play. This does what I need it to for the time being, but I was hoping to extend out the configuration parameters to be more user configurable through the HomeAssistant. e.g. the globals at the top of this file could be input_numbers instead, which would allow me to change thresholds, min / max charging times etc.

Hi Adam

Trying to set up something similar for my model 3. Are you controlling the on/off through the charger or the car itself? I can see the venom_charger_switch reference but not sure what this is.

Thanks
Jarrad

Hi @J-Rod-16

This is a python script that uses HomeAssistant’s pyscript integration as an execution environment. It’s not intended to be used as a standalone script.

The charger switch is an entity that gets exposed when you setup the Tesla Custom Integration (https://github.com/alandtse/tesla) that controls the state of charging through Tesla’s vehicle API

Thanks for the quick reply. I hadn’t see that Tesla Custom Integration before. I’ll try it out once I get my Home Assistant set up on a raspberry pi.