# VED (Vehicle Energy Dataset)
A novel large-scale database for fuel and energy use of diverse vehicles in real-world.

VED captures GPS trajectories of vehicles along with their timeseries data of fuel, energy, speed, and auxiliary power usage, and the data was collected through onboard OBD-II loggers from Nov, 2017 to Nov, 2018.
The fleet consists of total 383 personal cars (264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs) in Ann Arbor, Michigan, USA.
Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons.
In total, VED accumulates approximately 374,000 miles.

A number of examples were presented in the paper to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars.

Link to the paper:
[Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research](https://doi.org/10.1109/TITS.2020.3035596)\
**Geunseob (GS) Oh**, David J. LeBlanc, Huei Peng\
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2020 (In press).\
The paper is also available on [Arxiv](https://arxiv.org/pdf/1905.02081.pdf).

Contact: [e-mail].

GS Oh, Ph.D. Candidate, University of Michigan.

## Files
VED consists of Dynamic Data (time-stamped naturalistic driving records of 383 vehicles) and Static Data (Vehicle parameters for the 383 vehicles)

Dynamic Data: "VED_DynamicData.7z" contains a number of "VED_mmddyy_week.csv" files
- Includes a week worth dynamic data, for mmddyy ~ (mmddyy + 7 days)
- Columns represent:
DayNum, VehId, Trip, Timestamp(ms), Latitude[deg], Longitude[deg], Vehicle Speed[km/h], MAF[g/sec], Engine RPM[RPM], Absolute Load[%], Outside Air Temperature[DegC], Fuel Rate[L/hr], Air Conditioning Power[kW], Air Conditioning Power[Watts], Heater Power[Watts], HV Battery Current[A], HV Battery SOC[%], HV Battery Voltage[V], Short Term Fuel Trim Bank 1[%], Short Term Fuel Trim Bank 2[%], Long Term Fuel Trim Bank 1[%], Long Term Fuel Trim Bank 2[%]
- Notes:
Each combination of VehID, Trip is unique.
DayNum represents elapsed days since a reference date. (DayNum 1 = Nov, 1st, 2017, 00:00:00, DayNum 1.5 = Nov, 1st, 2017, 12:00:00)
For the details, refer to [the VED paper](https://arxiv.org/abs/1905.02081)
- Estimation of Fuel Consumption Rate (FCR)
**Input**: FuelRate, MAF, AbsLoad, Displacementeng, RPMeng, ST FT, LT FT, AFR, ρair
**Output**: FCR
1. correction = (1 + ST FT/100 + LT FT/100)/AFR
2. **if** FuelRate is available **then**
3. **return** FuelRate
4. **else if** MAF is available **then**
5. **return** MAF * correction
6. **else if** AbsLoad and RPMeng are available **then**
7. MAF = AbsLoad/100*ρair*Displacementeng*RPMeng/120
8. **return** MAF * correction
9. **else**
10. **return** NaN

Static Data: "VED_Static_Data_ICE&HEV.xlsx", and "VED_Static_Data_PHEV&EV.xlsx"
- Includes parameters of all 383 vehicles
- Columns represent:
VehId, EngineType, Vehicle Class, Engine Configuration & Displacement Transmission, Drive Wheels, Generalized_Weight[lb]