We create consumption related driving scores enabling our customers to gain insights into eco-efficiency. Our analysis is based on the evolution of trip variables to obtain a trip eco-assessment. By combining individual trip analytics for each driver, complete driving style assessments are produced giving insights into the eco-performance of each and every individual driver or an entire fleet.

Trip eco-assessment

Based on state-of-the-art eco-efficiency algorithms, the evolution of fuel consumption, electricity consumption, and electricity recovery is derived. Moreover, thanks to augmented data, trip paths are analysed to include in the assessment information about EV chargers that might be used by the driver.

Requirements

Customers need to specify a REST endpoint so that Motion-S can send the result of trip assessments as soon as they are obtained.

Trip assessment details

The assessment submitted to the customer’s endpoint contains information about fuel and electric components. This information is submitted in a JSON containing the following information:

Name Description
fuel_consumed_litre_per_100_km Average consumption during trip in L/100km
eco_score Eco driving score based on fuel consumption.
co2_emissions_kg_per_litre CO2 emitted in the trip, measured in kg/L.
net_electric_consumption_kwh Electricity consumed (taking into account electricity recovered).
electricity_recovered_kwh Electricity recovered during trip (due to slope…)
electricity_consumed_kwh Electricity consumed (electric_consumption without taking into account electricity recovered)
nb_ev_chargers_passed_by Number of EV chargers along the trip (and within ~ 1.5km around).
meta_data Additional information of the trip related to eco components.

The following is an example of the response for a trip submitted to an eco-assessment:

{
  "uid": "<motion-s-unique-trip-identifier>",
  "origin": "<customer-name>-<fleet-name>-<fleet-number>",
  "context": { ... },
  "eco": {
        "fuel_consumed_litre_per_100_km": 6.4,
        "meta_data": {},
        "eco_score": 80,
        "co2_emissions_kg_per_litre": 2.6,
        "net_electric_consumption_kwh": 3.3,
        "electricity_recovered_kwh": 0.5,
        "electricity_consumed_kwh": 4.7,
        "nb_ev_chargers_passed_by": 1,
  },
  ...
}

Driver electromobility assessment

An electromobility assessment report has the aim to assess the driver’s eligibility to switch to an electric vehicle (EV). The main concept is linked to the three main limitations of the EV transition, and gives as such a response to them:

  • Total cost of ownership;
  • Range anxiety and long-trip issues;
  • Hassle of EV charging networks.

Below we describe relevant information about this API.

Request

URL

https://<customer-name>.motion-s.com/mobility_profiling/eco_assessment/drivers

Method

GET

Query parameters

  • ref_driver_ids (separated by “;”);
  • ref_from_date (“YYYY-MM-DD” format);
  • ref_to_date (“YYYY-MM-DD” format);
  • token: provided by Motion-S
Example
curl -i -H "Accept: application/json" \
        -H "Content-Type: application/json" \
        -H "Authorization: Basic token" \
        -X GET https://<customer-name>.motion-s.com/mobility_profiling/eco_assessment/ drivers?ref_driver_ids=a793d2a99b632f0a;04893637cd6844e9

Response

Successful response

HTTP status code

200

Response values

Metric Description
devices_ids A semi-colon separated list of device ids associated to a given user.
ev_score Global EV score between 0 and 100, where 100 represents full eligibility to switch to EV and 0 represents that the driver’s trips characteristics made it not eligible for this switch
m1_cost_reduction Estimation of the cost reduction based on the potential for the driver to use free chargers, home electricity and the e-Chargers network. This potential is measured from the probability that the driver uses any of those types of chargers and then these probabilities are combined with their related costs. The result is a value between -1 and 1, where 1 represents a 100% cost reduction compared to non-EV vehicle mode. Then, negative values represent that the driver will not save money by switching to EV.
m2_trips_without_charging Considering that the driver has a battery with an average capacity (55 KWh), this is the average number of trips that can be done without charging.
m3_charging_period This metric points out the autonomy in terms of days, considering that the driver has a battery with an average capacity (55 KWh).
m4_proximity_to_e_chargers This metric points out the average number of EV chargers passed by (less than ~ 1.5km away from trip path) per trip.
m5_carbon_reduction The amount of CO2 emissions that would be reduced by a switch to EV, given the fuel consumption and the type of energy of the vehicle model. Unit: g/km
m6_required_battery The battery type that the driver should choose to charge every two days, according to his/her driving habits. Unit: kWh
Response example
{
  "data": [
    {
      "devices_ids": "a53bceb86e2150d1016e2156fdd90083;a53bceb86e2150d1016e215731a700ad",
      "ev_score": 95,
      "m1_cost_reduction": 0.3526,
      "m2_trips_without_charging": 22.9452,
      "m3_charging_period": 5.6506,
      "m4_proximity_to_e_chargers": 7.7239,
      "m5_carbon_reduction": 151.5554,
      "m6_required_battery": 19.2909
    }
  ]
}