Trip Logging Automations for PHEV (easily adaptable to BEV/ICE)

[IceRoG]

🚗 PHEV Trip Logging with Home Assistant Automations

🤔 Why PHEV Trip Logging Is Harder Than BEV or ICE

Trip logging for Battery Electric Vehicles (BEV) or pure petrol/diesel cars is relatively straightforward:

• BEV: energy consumption is purely electric → kWh/100km
• ICE: energy consumption is purely fuel-based → L/100km

Plug-in Hybrid Electric Vehicles (PHEV) combine both worlds — and that’s where things get complicated.

The FordPass limitation

The FordPass Home Assistant integration does not expose:

• raw electric consumption per trip (kWh), or
• raw fuel consumption per trip (liters)

Instead, it exposes a single metric:

• tripFuelEconomy

This value is already a vehicle-internal combination of:

• electric energy usage (kWh/100km)
• fuel usage (L/100km)

The exact weighting is done by the car itself and is not transparent.

Why this matters

Because of this limitation:

• You cannot directly separate electric vs fuel usage
• You cannot aggregate trips accurately over time
• You cannot fairly compare drivers or driving styles

The workaround used here

This setup reconstructs meaningful trip data by:

1. Capturing battery energy at trip start and end
2. Calculating:
   • kWh/100km from the battery energy difference over the trip distance
   • L/100km from the tripFuelEconomy over the trip distance, adjusted using kWh contribution
3. Using Ford’s tripFuelEconomy only as a reference for baseline fuel efficiency
4. Writing the calculated and normalized trip data into a CSV file for further analysis

This makes it possible to:

• Log trips reliably
• Compute distance-weighted averages
• Separate electric and fuel efficiency
• Build dashboards and long-term statistics

---

1️⃣ Set up FordPass

First, make sure the FordPass integration is configured and connected to your vehicle.
All following steps rely on FordPass sensors such as:

• wheel torque status
• trip summary length
• fuel percentage
• battery energy remaining

---

2️⃣ Create Required Helper & Template Sensors

🔹 Trip baseline sensors

These template sensors extract attributes from FordPass entities and expose them as standalone sensors.

sensor:
  - platform: template
    sensors:
      kuga_trip_fuel_economy:
        value_template: >
          {{ state_attr('sensor.fordpass_[VIN]_speed', 'tripFuelEconomy') }}

      kuga_verbleibende_energie:
        value_template: >
          {{ state_attr('sensor.fordpass_[VIN]_elveh', 'batteryEnergyRemaining') }}

---

2️⃣b Driver Detection (Who is Driving?)

Driver detection is based on:

• Home Assistant Companion App
• Android Auto state
• Bluetooth connected devices

PersonOne

{% set bt = state_attr('sensor.s24_fe_bluetooth_connection', 'connected_paired_devices') | string | lower %}
{% set aa = is_state('binary_sensor.s24_fe_android_auto', 'on') %}
{{ 'kuga' in bt and aa }}

Entity: sensor.triplog_PersonOne_connected

PersonTwo

{% set bt = state_attr('sensor.sm_a546b_bluetooth_connection', 'connected_paired_devices') | string | lower %}
{% set aa = is_state('binary_sensor.sm_a546b_android_auto', 'on') %}
{{ 'kuga' in bt and aa }}

Entity: sensor.triplog_PersonTwo_connected

---

2️⃣c Helpers for Trip Start

Create the following helpers:

• input_number.triplog_start_battery
• input_number.triplog_start_fuel

⚠️ Important:
Set the maximum value of input_number.triplog_start_fuel to a value above 100 (e.g. 120), as Ford fuel percentages can exceed 100%.

---

3️⃣ Configure CSV Logging (File Integration)

Trip data is persisted in a CSV file.

3.1 Allow file access

Add the following to configuration.yaml:

homeassistant:
  allowlist_external_dirs:
    - "/config"

3.2 Create the file notification

• Add the File notification integration
• File path: /config/triplogs.csv
• Rename the entity ID to:

notify.triplogs

---

4️⃣ Import the Automations

• Add TripLog Start and TripLog End automations
• Replace [VIN] with your vehicle VIN everywhere
• Adjust entity IDs if your FordPass sensor names differ

---

5️⃣ Set up Pyscript

Install and configure the Pyscript integration.

This is required to:

• Read the CSV file
• Recreate entities in Home Assistant
• Calculate per-driver statistics

---

6️⃣ Create Sensors from the CSV

Use the update_triplog_sensors Pyscript service to:

• Create last trip sensors per driver

• Create aggregated statistics per driver

  • total km
  • average kWh / 100 km (distance-weighted)
  • average L / 100 km (distance-weighted)

⚠️ Note:
Currently, the script must be re-run after every Home Assistant restart and of course after every new data entry.

---

7️⃣ Visualize & Analyze

With all sensors available, you can:

• Build Lovelace dashboards
• Feed the data into Grafana
• Analyze efficiency trends per driver

---

✅ Result

A fully automated PHEV trip logging pipeline:

FordPass → Automations → CSV → Pyscript → Sensors → Dashboards

🔹 Automation 1: TripLog Start

This automation triggers when the vehicle’s wheel torque status changes from OFF to ON (i.e., the car starts moving).

It stores the initial state of the trip:

• Battery energy
• Odometer
• Fuel percentage
• Fuel range

These values are saved into input_number helpers for later comparison.

🧠 Purpose

Capture a clean baseline at trip start so that consumption can be calculated reliably at trip end.

📄 YAML

alias: TripLog Start
description: ""
triggers:
  - trigger: state
    entity_id:
      - sensor.fordpass_[VIN]_wheeltorquestatus
    from:
      - "OFF"
    to:
      - "ON"
    for:
      seconds: 10
conditions: []
actions:
  - action: input_number.set_value
    data:
      value: "{{ states('sensor.kuga_verbleibende_energie') }}"
    target:
      entity_id: input_number.triplog_start_battery

  - action: input_number.set_value
    data:
      value: "{{ states('sensor.fordpass_[VIN]_fuel') }}"
    target:
      entity_id: input_number.triplog_start_fuel

  - action: system_log.write
    data:
      message: "TripLog: Trip start values saved."
mode: single

🔹 Automation 2: TripLog End

This automation triggers when the wheel torque status changes from ON to OFF (vehicle stops).

It:

• Calculates distance, electric usage, and fuel usage
• Converts fuel percentage to liters
• Calculates:
  • kWh / 100 km
  • L / 100 km (adjusted for electric contribution)
• Identifies the driver (based on presence sensors)
• Logs the trip
• Sends notifications for manual validation
• Updates custom triplog sensors via pyscript

📄 YAML

alias: TripLog End
description: ""
triggers:
  - trigger: state
    entity_id:
      - sensor.fordpass_[VIN]_wheeltorquestatus
    from:
      - "ON"
    to:
      - "OFF"
    for:
      seconds: 2
conditions: []
actions:
  - variables:
      start_batt: "{{ states('input_number.triplog_start_battery') | float(0) }}"
      start_fuel_pct: "{{ states('input_number.triplog_start_fuel') | float(0) }}"
      end_batt: "{{ states('sensor.kuga_verbleibende_energie') | float(0) }}"
      end_fuel_pct: "{{ states('sensor.fordpass_[VIN]_fuel') | float(0) }}"
      delta_km: "{{ states('sensor.ford_trip_summary_length') | float(0) }}"
      fuel_tank_liters: 42.7

      delta_kwh: "{{ start_batt - end_batt }}"
      delta_fuel_pct: "{{ start_fuel_pct - end_fuel_pct }}"
      delta_fuel_l: "{{ (delta_fuel_pct / 100) * fuel_tank_liters }}"

      cons_l_100km_raw: >
        {% if delta_km > 0 %}
          {{ (delta_fuel_l / delta_km) * 100 }}
        {% else %} 0 {% endif %}

      cons_kwh_100km_raw: >
        {% if delta_km > 0 %}
          {{ (delta_kwh / delta_km) * 100 }}
        {% else %} 0 {% endif %}

      baseline_fuel: "{{ states('sensor.kuga_trip_fuel_economy') | float(999) }}"
      baseline_elec: "{{ states('sensor.kuga_trip_electrical_efficiency') | float(999) }}"

      cons_l_100km: >
        {% set calc = baseline_fuel - (cons_kwh_100km_raw | float * 0.12) %}
        {{ 0 if calc < 0 else calc | round(2) }}

      cons_kwh_100km: "{{ cons_kwh_100km_raw | float }}"

      delta_km_r: "{{ delta_km | round(2) }}"
      delta_kwh_r: "{{ delta_kwh | round(2) }}"
      delta_fuel_l_r: "{{ delta_fuel_l | round(2) }}"
      cons_kwh_100km_r: "{{ cons_kwh_100km | round(2) }}"
      cons_l_100km_r: "{{ cons_l_100km | round(2) }}"

      PersonOne_connected: "{{ is_state('sensor.triplog_PersonOne_connected', 'True') }}"
      PersonTwo_connected: "{{ is_state('sensor.triplog_PersonTwo_connected', 'True') }}"
      driver: >
        {% if PersonOne_connected %} PersonOne
        {% elif PersonTwo_connected %} PersonTwo
        {% else %} Unknown {% endif %}

      timestamp: "{{ now().isoformat() }}"

  - action: notify.send_message
    target:
      entity_id: notify.triplogs
    data:
      message: >
        {{ timestamp }},{{ delta_km_r }},{{ delta_kwh_r }},{{ delta_fuel_l_r }},
        {{ cons_kwh_100km_r }},{{ cons_l_100km_r }},{{ driver }}

  - action: system_log.write
    data:
      message: >
        TripLog saved: {{ delta_km_r }} km, {{ delta_kwh_r }} kWh,
        {{ delta_fuel_l_r }} L, {{ cons_kwh_100km_r }} kWh/100km,
        {{ cons_l_100km_r }} L/100km, Fahrer: {{ driver }}

  - action: pyscript.update_triplog_sensors
mode: single

🔹 Part 3: Reading the CSV and Creating Trip Sensors with Pyscript

To make the trip data usable again inside Home Assistant, I use a Pyscript service that reads the CSV file and recreates entities for:

• Last trip per driver
• Aggregated statistics per driver
  • Total km
  • Average kWh / 100 km
  • Average L / 100 km (weighted)

🧠 Design decisions

• Data source: /config/triplogs.csv
• Aggregation is done per driver
• Averages are distance-weighted:
  • avg = Σ(consumption_per_100km × km) / Σ(km)
• Raw kWh or liters are not used for aggregation to avoid rounding drift
• Supports CSV rows with 7 or more columns (extra columns are ignored)

The service can be triggered manually or automatically at the end of each trip.

---

📄 Pyscript Service

import io
import csv
from io import StringIO

@service
def update_triplog_sensors(action=None):
    """
    Reads /config/triplogs.csv and updates per-driver sensors.
    Computes weighted averages using ONLY kWh/100km and L/100km
    multiplied by km driven — without using raw kWh or liters.
    """
    csv_file = "/config/triplogs.csv"
    stats = {}

    try:
        with io.open(csv_file, "r", encoding="utf-8") as f:
            csv_content = f.read()

        reader = csv.reader(StringIO(csv_content))
        next(reader, None)  # Skip header

        for row in reader:
            if not row:
                continue

            # Ignore header accidentally inside file
            if row[0].strip().lower() == "timestamp":
                continue

            # Accept 7 or more columns (ignore extras)
            if len(row) < 7:
                continue

            row = row[:7]
            timestamp, km, kwh, fuel_l, kwh_100km, l_100km, driver = row

            km = float(km)
            kwh_100km = float(kwh_100km)
            l_100km = float(l_100km)

            # Init stats per driver
            if driver not in stats:
                stats[driver] = {
                    "total_km": 0,
                    "weighted_kwh": 0,
                    "weighted_l": 0,
                    "trips": 0,
                    "last_trip_time": "",
                    "last_trip_km": 0,
                    "last_trip_kwh_100km": 0,
                    "last_trip_l_100km": 0
                }

            s = stats[driver]

            # Accumulate totals
            s["total_km"] += km
            s["weighted_kwh"] += kwh_100km * km
            s["weighted_l"] += l_100km * km
            s["trips"] += 1

            # Store last trip
            s["last_trip_time"] = timestamp
            s["last_trip_km"] = km
            s["last_trip_kwh_100km"] = kwh_100km
            s["last_trip_l_100km"] = l_100km

        # Update Home Assistant sensors
        for driver, s in stats.items():
            driver_slug = driver.lower().replace(" ", "_")

            if s["total_km"] > 0:
                avg_kwh_100km = s["weighted_kwh"] / s["total_km"]
                avg_l_100km = s["weighted_l"] / s["total_km"]
            else:
                avg_kwh_100km = 0
                avg_l_100km = 0

            state.set(f"sensor.triplog_{driver_slug}_total_km", round(s["total_km"], 1))
            state.set(f"sensor.triplog_{driver_slug}_avg_kwh_100km", round(avg_kwh_100km, 2))
            state.set(f"sensor.triplog_{driver_slug}_avg_l_100km", round(avg_l_100km, 2))
            state.set(f"sensor.triplog_{driver_slug}_last_trip_km", s["last_trip_km"])
            state.set(f"sensor.triplog_{driver_slug}_last_trip_kwh_100km", s["last_trip_kwh_100km"])
            state.set(f"sensor.triplog_{driver_slug}_last_trip_l_100km", s["last_trip_l_100km"])
            state.set(f"sensor.triplog_{driver_slug}_last_trip_time", s["last_trip_time"])

        log.info("Triplog sensors updated successfully.")

    except Exception as e:
        log.error(f"Error reading or parsing triplogs.csv: {e}")