Shared Electric Vehicles (SEVs) are one of the most recent trends in the field of sustainable transportation. They have the potential to solve many urban mobility problems- such as congestion, urban accessibility, high cost of transport, air pollution, etc. As their penetration grows, however, so will the amount of electricity they consume. The main challenge in this field is then to better understand SEV spatiotemporal driving and charging patterns in order to reduce total electricity consumption. To address this issue, the following research question is here posed: “What is the best SEV fleet configuration of electric vehicle technologies to reduce SEV electricity consumption in San Francisco? How do those charging and driving patterns vary over space and time?” In order to answer this question, an agent-based, city-level transportation simulation model will be built in AnyLogic. That would be a GIS model where large SEV fleets will attempt to meet current transportation demand (given real world origins and destinations). Total SEV electricity consumption would be dependent on charging station location and speed, vehicle class, battery size, distance travelled, number of vehicles on the road, etc. The goal of the study would then be to identify the scenario with the most optimal technical specifications: i.e. the variables that reduce electricity consumption the most. In effect, this study will re-position shared electric vehicle research to the urban and geospatial studies.

1. Stefi Mitova
PhD Candidate
University of Colorado Boulder
September 2018

2. 1 • Problem framing
2 • Literature review
3 • Research question
4 • Methods and data
5 • Preliminary results
6 • Discussion and conclusion

3. High cost
Emissions
Inaccurate
forecasts
Spatial
perspective
Traffic
Reduce transportation:
-Time
-Traffic
-Energy consumption
-Emissions
-Cost

4. Gaps Literature
Spatial perspective
Solar and
wind
Charging
stations
and Smart
charging
Electric
cars

5. Considering the spatiotemporal, environmental, and technical
characteristics of a city fleet, what is the most optimal and cheapest
combination of:
▪ Shared/ Electric cars
▪ Charging stations
▪ Renewable energy technologies
▪ Smart charging algorithms
to reduce shared EV electricity consumption in San Francisco?

6. EV energy
consumption
Spatiotemporal
Environmental
Technical
-Road slope
-Location of the charging
stations
-Location of the vehicles
-Location of the passengers
-Traffic
-Solar irradiation
-Wind speed
-Temperature
-Humidity
-Air density
Technical specifications of the:
-Electric vehicles
-Charging stations
-Solar panels
-Wind turbines
-Storage

7. • Internal combustion vehicles and gas
stationsBaseline
• Varying the location
• Varying the speed of the station
Charging
stations
• Different classes and models/ battery
sizesVehicles
• Optimizing for electricity price or
electricity source (solar, wind, etc)Smart charging

8. San Francisco TAZ’s
1
2
3

9. (1) Agent-based (2) System dynamics (3) Discrete event
Map-based Interactive Java-based

11. 2 x O
5 x O
4 x O
2 x D
6 x D
3 x D
O = origin
D= destination
Total number of orders in a radius of 5 miles
is 11 orders → send a mini-bus.
Transportation dispatch
center
Origin
Destination
One Zone
DYNAMICALLY GENERATING VEHICLE SIZE DEPENDING ON
TRAVEL DEMAND VOLUME

12. 1) Spatiotemporal
2) Environmental
3) Technical

13. Topography in San Francisco
Distance between two people
Distance between a person and a car
Origin- destination distance
Charging station location

14. Solar irradiation
Wind speed
Seasonal and Hourly data on
• Temperature
• Humidity
• Air density
• Solar irradiation
• Wind speed

18. 0
5
10
15
20
25
30
35
69
73
77
81
85
89
93
97
101
105
109
113
117
121
125
129
133
137
141
145
149
153
157
161
165
kWh
Hours
Weekly Vehicle Battery State Data
0
5
10
15
20
25
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
151
157
163
kW Hours
Weekly Vehicle Charge Data

21. EV/SHARED
▪ Trade-off between wait time reduction and an increase in vehicles/VMT/energy
increase
▪ The fewer people per car, the higher VMT is
Charging
▪ Solar + Grid is the second yet not the most feasible option
▪ Current charging station locations are not where demand is
▪ Optimization of solar overlaps with highest transportation demand
▪ Assuming people are willing to share a ride
▪ Deciding on the route depends on Open Street Maps

22. Thank you!
Questions?
Stefi Mitova
PhD Candidate
University of Colorado Boulder
September 2018
stefi.mitova@gmail.com