Electric Vehicle (EV) Charging Load Profiles
This dataset provides detailed electric vehicle charging session records, including charger type, power rating, session timing, energy delivered, and grid impact metrics for both managed and unmanaged charging. With location and user-level fields, it enables comprehensive analysis of charging patterns, grid demand, and optimization strategies for residential and public EV infrastructure.
Sample rows
preview · 8 of 110 rows · all 15 columns| session_idstring | location_typestring | grid_impact_scorefloat | managed_chargingboolean | location_countrystring | charger_typestring | charger_power_kwfloat | session_start_timedatetime | session_end_timedatetime | energy_delivered_kwhfloat | user_idstring | location_street_addressstring | location_citystring | location_statestring | location_postal_codestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S001 | home | 0.8 | true | USA | Level 2 | 7 | 2024-05-02T19:30:00Z | 2024-05-03T02:15:00Z | 22.6 | U1001 | 521 Pinecrest Rd | Denver | CO | 80203 |
| S002 | public | 7.2 | false | USA | DC Fast | 150 | 2024-06-21T13:10:00Z | 2024-06-21T13:42:00Z | 26.3 | U1002 | 1081 Market St | San Francisco | CA | 94103 |
| S003 | home | 1.2 | true | USA | Level 2 | 7 | 2024-07-05T22:55:00Z | 2024-07-06T05:30:00Z | 31.9 | U1003 | 83 Maplewood Ave | Portland | OR | 97205 |
| S004 | public | 8.6 | false | Canada | DC Fast | 200 | 2024-05-18T09:20:00Z | 2024-05-18T09:34:00Z | 19.7 | blank | 452 Queen St | Toronto | ON | M5V 2B6 |
| S005 | home | 0.6 | true | USA | Level 2 | 7 | 2024-06-14T18:05:00Z | 2024-06-15T01:45:00Z | 17.8 | U1004 | 741 Willow Ln | Dallas | TX | 75204 |
| S006 | public | 10.2 | false | Germany | DC Fast | 300 | 2024-05-05T07:47:00Z | 2024-05-05T08:12:00Z | 29.1 | blank | 297 Kurfürstendamm | Berlin | BE | 10719 |
| S007 | home | 0.9 | true | USA | Level 2 | 7 | 2024-07-12T23:40:00Z | 2024-07-13T06:05:00Z | 35.4 | U1005 | 120 Cedar St | Boston | MA | 02116 |
| S008 | public | 6.5 | false | Australia | DC Fast | 80 | 2024-06-19T21:50:00Z | 2024-06-19T22:12:00Z | 18.5 | blank | 9 George St | Sydney | NSW | 2000 |
| S009 | home | 1.1 | true | USA | Level 2 | 7 | 2024-06-21T04:15:00Z | 2024-06-21T08:50:00Z | 24.8 | U1006 | 1625 Spruce Dr | Seattle | WA | 98101 |
| S010 | public | 5.8 | false | UK | DC Fast | 60 | 2024-07-02T16:35:00Z | 2024-07-02T17:07:00Z | 21.2 | U1007 | 2235 Abbey Rd | London | ENG | NW8 9AY |
| S011 | home | 1.3 | true | USA | Level 2 | 7 | 2024-05-30T20:30:00Z | 2024-05-31T03:20:00Z | 28.1 | U1008 | 3418 Elm St | Phoenix | AZ | 85004 |
| S012 | home | 2.4 | false | USA | Level 2 | 7 | 2024-06-09T21:45:00Z | 2024-06-10T04:15:00Z | 19.6 | U1009 | 789 Forest Ave | Charlotte | NC | 28202 |
| S013 | public | 11.6 | false | Norway | DC Fast | 350 | 2024-06-18T10:55:00Z | 2024-06-18T11:17:00Z | 32.4 | U1010 | 2 Stortorvet | Oslo | 03 | 0155 |
| S014 | home | 1 | true | USA | Level 2 | 7 | 2024-05-20T17:15:00Z | 2024-05-21T00:30:00Z | 23.1 | U1011 | 2101 Ash St | Houston | TX | 77002 |
| S015 | workplace | 1.7 | true | India | DC Fast | 100 | 2024-07-07T07:20:00Z | 2024-07-07T07:55:00Z | 20.9 | U1012 | 17 Bannerghatta Rd | Bangalore | KA | 560029 |
| S016 | public | 4.4 | false | Japan | DC Fast | 50 | 2024-07-01T15:35:00Z | 2024-07-01T15:49:00Z | 12.2 | blank | 601 Shibuya Crossing | Tokyo | 13 | 150-0002 |
| S017 | home | 1.1 | true | USA | Level 2 | 7 | 2024-06-23T18:10:00Z | 2024-06-24T02:00:00Z | 21.5 | U1013 | 54 Oakridge Dr | Atlanta | GA | 30303 |
| S018 | workplace | 5.7 | false | USA | DC Fast | 60 | 2024-05-29T20:05:00Z | 2024-05-29T20:31:00Z | 14.4 | U1014 | 9911 Commerce Dr | Chicago | IL | 60601 |
| S019 | home | 0.7 | true | USA | Level 2 | 7 | 2024-06-04T21:00:00Z | 2024-06-05T05:50:00Z | 27.6 | U1015 | 3822 River Rd | Minneapolis | MN | 55401 |
| S020 | public | 9.7 | false | Australia | DC Fast | 200 | 2024-07-10T11:35:00Z | 2024-07-10T12:05:00Z | 31.1 | blank | 23 King St | Melbourne | VIC | 3000 |
What the 110 rows show
from the 110-row samplePublic (location type) stands out: mean grid_
- 48%managed_
charging = true - 2.5median grid_
impact_ score - 2charger types
- 7median charger_
power_ kw - 22.7median energy_
delivered_ kwh
Every row with grid_
- string 9
- float 3
- datetime 2
- boolean 1
Columns
15 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
session_id | string | Unique identifier for each EV charging sessionunique | S001 |
charger_type | string | Type of charger used for the session (Level 2 or DC Fast)Level 2 · DC Fast | Level 2 |
user_id | string | Unique identifier for the user or household associated with the charging sessionoptional | U1001 |
location_type | string | Type of location where charging occurred (e.g., home, public, workplace)home · public · workplace · optional | home |
location_street_address | string | Street address of the charging locationoptional | 521 Pinecrest Rd |
location_city | string | City of the charging locationoptional | Denver |
location_state | string | State or province of the charging locationoptional | CO |
location_postal_code | string | Postal code of the charging locationoptional | 80203 |
location_country | string | Country of the charging locationoptional | USA |
| Numbers 3 columns | |||
charger_power_kw | float | Rated power output of the charger in kilowatts (kW)7 to 350 | 7 |
energy_delivered_kwh | float | Total energy delivered during the session in kilowatt-hours (kWh)0 or more | 22.6 |
grid_impact_score | float | Quantitative measure of the session's impact on the grid (e.g., peak demand contribution, 0 = none, higher = greater impact)0 or more · optional | 0.8 |
| Dates and times 2 columns | |||
session_start_time | datetime | Timestamp when the charging session started | 2024-05-02T19:30:00Z |
session_end_time | datetime | Timestamp when the charging session ended | 2024-05-03T02:15:00Z |
| True or false 1 column | |||
managed_charging | boolean | Indicates whether the charging session was managed (load shifted or optimized) or unmanaged | true |
Use it for
An energy dashboard
The managed_
charging rate, grid_ impact_ score by location_ type and a breakdown of location_ country. Excel, Power BI or Tableau. Why do the 41 public rows have a mean grid_
impact_ score of 7.7? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Sessions110S0010.8homeS0027.2publicS0031.2home
A software demo
Believable sessions with charger_
type, charger_ power_ kw and session_ start_ time to fill a screen in front of a buyer.
This dataset110 rows15 columns
Yours1,000 rows15 columnslocation_type: UK only
blueprint · electric-vehicle-ev-charging-load-profiles
Behind this dataset
Same schema. As many rows as you need.
These 110 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Charging session ID unique per event
- Meter ID or EVSE (charging station) ID
- Start and end timestamp of charging session
- Energy delivered in kWh (session total)
- Peak kW demand during session (typically 7kW for L2, 50-350kW for DCFC)
- Managed charging: smart charging schedule to avoid peak periods
- Unmanaged charging: plug-in and charge immediately
- Grid impact: coincident peak contribution, transformer loading
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- electric-vehicle-ev-charging-load-profiles