1.
Chair for Electrochemical Energy Conversion
and Storage Systems
Battery Ageing • Battery Models • Battery Diagnostics • Battery Pack Design • Electromobility • Stationary Energy Storage • Energy System Analysis
Public charging infrastructure usage in Germany
EV Charging Infrastructure
Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
22.11.2022
1

2.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Institute for Power Electronics and Electrical Drives (ISEA)
■ Univ.-Prof. Dr. ir. Dr. h. c. Rik De Doncker
□ Chair for Power Electronics and Electrical Drives
■ Univ.-Prof. Dr. rer. nat. Dirk Uwe Sauer
□ Chair for Electrochemical Energy Conversion and Storage Systems
■ Univ.-Prof. Dr. rer. nat. Egbert Figgemeier
□ Chair for Ageing and Lifetime Prediction of Batteries
22.11.2022 ISEA Confidential
2

3.
|
Chair for Electrochemical Energy Conversion and Storage Systems
At a Glance – Institute for Power Electronics and Electrical Drives (ISEA)
22.11.2022
3 ISEA Confidential
Professors
Undergraduate
Students
120+ 3
Graduate
Students
Staff (Full-Time
Equivalent)
90+ 150+
2021 Completed
Doctorates
2021 Total Budget
in Million €
19 10+
2021 Journals 70 43
2021 Conference
Contributions

4.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Motivation – Electric vehicles have an enormous battery energy capacity
■ If the goal of the current government of 15
million battery electric vehicles by 2030 is
achieved, hundreds of GWh of battery capacity
will be built into cars
■ Since users purchase the vehicle for driving, the
battery is already paid for
■ Passenger vehicles are stationary most of the
day
■ Fluctuating, distributed renewable energy
sources will require distributed storages
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
0.6
2.7
22.5
13.9
0.3
0
5
10
15
20
25
30
35
40
45
50
55
2021*
till
2017
till
2017
2018
2019
2020
2021*
2021*
till
2017
2018
4.1
2020
2019
2019
2020
1.0
2018
11.1
2.3 2.1 0.6
4.1
31.2
0.6
2.2
Battery capacity in
GWh
DC charging power in
GW
AC charging power in
GW
Power and energy capacity of the German passenger vehicle fleet
© ISEA, RWTH Aachen
* 2021-values do not include decommissioned vehicles, source: Hecht & Figgener, KBA, ADAC
4

5.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Motivation – New vehicles have larger energy and power rating than needed
on a daily basis
■ New vehicles are sold with increasing average
battery capacities, both in terms of energy and
power
■ Assuming daily trips of 40 km and energy
consumption of 18 kWh / 100 km, only 7.2 kWh
are needed per day. The battery, therefore, has
an unused capacity of ~45 kWh for the average
vehicle sold in 2021
■ Strongly increasing DC power ratings would
theoretically allow for high-power, fast-reacting
buffer storages
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
19.1
42.1
71.9
50.7
8.6
53.5
88.8
8.7
0
10
20
30
40
50
60
70
80
90
Average DC charging
power in kW
Average AC charging
power in kW
Average battery
capacity in kWh
6.3
27.3
76.0
8.2
2021
2018 2019 2020
Properties of new battery electric passenger vehicles in Germany
© ISEA, RWTH Aachen
Source: Hecht & Figgener, KBA, ADAC
5

6.
|
Chair for Electrochemical Energy Conversion and Storage Systems
How can we use this capacity without reducing user comfort?
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
6

7.
|
Chair for Electrochemical Energy Conversion and Storage Systems
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Agenda
Charging vs Parking
Profitability
Machine learning for predicting availability
Mid-term prediction
Short-term prediction
To understand what flexibility is available, we first need to understand how much time is needed for recharging,
the primary purpose of connecting to a charging station!
7

8.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Data and Methodology
1) Status changes of 27.8k charging stations in
Germany between 2019 and 2021
2) 9 million charge detail records
■ Matching of EVSE-ID to connector power, area
type, etc.
■ Aggregation of results
□ Results using energy based on charge detail
records
□ All other are based on status changes
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Data Methodology
EVSE-ID Date and time Connector Status
+49*809*010*150000 2019-01-01 15:20:26 TYPE2 AVAILABLE
+49*809*010*150000 2019-01-02 04:53:25 TYPE2 UNKNOWN
+49*809*010*150000 2019-01-02 05:13:28 TYPE2 AVAILABLE
+49*809*010*150000 2019-01-02 06:00:27 TYPE2 OCCUPIED
+49*809*010*150000 2019-01-02 12:13:30 TYPE2 AVAILABLE
Start End Energy EVSE-ID
2021-10-18 08:40:18 2021-10-18 09:25:53 5.116 DE*EBW*E904756*1
2021-05-08 10:46:44 2021-05-08 12:43:13 20.942 DE*SLB*E749052001*1
2020-07-25 13:09:59 2020-07-25 19:09:24 12.009 DE*666*E1000359
2021-05-27 20:00:00 2021-05-27 20:03:45 0.258 DE*DES*E*BMW*0336*2
8

9.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Amount of energy recharged depending on how long the vehicle was
connected at an 11 kW AC charger
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Start End Energy EVSE-ID
2021-05-08 10:46:44 2021-05-08 12:43:13 20.942 DE*SLB*E749052001*1
2021-10-18 08:40:18 2021-10-18 09:25:53 5.116 DE*EBW*E904756*1
Energy
in
kWh
for
11
kW
chargers
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
9

10.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Amount of energy recharged depending on how long the vehicle was
connected at an 11 kW AC charger
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Energy
in
kWh
for
11
kW
chargers
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
10

11.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Amount of energy recharged depending on how long the vehicle was
connected at an 11 kW AC charger
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Energy
in
kWh
for
11
kW
chargers
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
11

12.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Charging processes terminate after about 4 hours at 11 kW AC chargers
■ The graph on the left shows the amount of
energy that was transferred during 210,000
charging events at public 11 kW AC chargers
in Germany in 2021
■ Only about 10% of the charging processes
actually utilize the available 11 kW
■ After 2 hours, the median charging events do
not show a strong increase in energy
transferred anymore and after 4 hours, virtually
no increase can be observed
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Energy
in
kWh
for
11
kW
chargers
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
12

13.
|
Chair for Electrochemical Energy Conversion and Storage Systems
3.7 kW charger is used in a similar way as 11 kW chargers
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
Energy
in
kWh
for
3.7
kW
chargers
13

14.
|
Chair for Electrochemical Energy Conversion and Storage Systems
22 kW charger is used in a similar way as 11 kW chargers
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
© ISEA, RWTH Aachen
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
Energy
in
kWh
for
22
kW
chargers
14

15.
|
Chair for Electrochemical Energy Conversion and Storage Systems
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Agenda
Charging vs Parking
Profitability
Machine learning for predicting availability
Mid-term prediction
Short-term prediction
Many charging stations are not profitable while some provide a lot of energy
15

16.
|
Chair for Electrochemical Energy Conversion and Storage Systems
■ The table shows our estimate of how many
public charging stations are profitable from
energy sales alone and including revenue from
greenhouse gas emission trading
■ For fast-chargers, a large portion can be
operated profitably
■ Slow-chargers largely cannot operate profitably
solely through the sale of electricity
■ For both, the greenhouse gas emission trading
is an essential pillar
P < 4
kW
4 kW
≤ P <
12 kW
12 kW
≤ P <
25 kW
25 kW
≤ P <
100 kW
100 kW
≤ P <
200 kW
P ≥
200 kW
Share
profitable
by
sales
margin
in
€ct/kWh
5 21.29% 1.77% 1.04% 0.08% 0.08% 0.11%
10 37.72% 11.63% 9.09% 1.04% 0.42% 0.11%
15 47.23% 23.15% 20.88% 3.07% 6.23% 0.11%
20 52.83% 33.04% 31.12% 6.93% 14.99% 0.47%
30 63.12% 46.51% 46.62% 15.57% 33.13% 6.26%
40 68.99% 55.39% 56.81% 24.46% 47.77% 17.53%
50 73.50% 61.40% 64.05% 31.60% 58.92% 29.10%
60 77.06% 66.47% 69.45% 38.20% 67.34% 40.79%
Estimated profitability share across all public charging stations
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
16

17.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Annualized cash flow analysis for 22 kW chargers
■ Stations in an urban environment are most
profitable
■ There are large differences between the
stations
■ The more profitable stations could refinance the
non-profitable ones
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
-1000
-500
0
500
1000
1500
2000
0% 20% 40% 60% 80% 100%
Annualized
cash-flow
in
€/(a·EVSE)
12 kW ≤ P < 25 kW
Urban Suburban Industrial Uninhabited
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
17
22 kW chargers

18.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Agenda
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Charging vs Parking
Profitability
Machine learning for predicting availability
Mid-term prediction
Short-term prediction
How can we predict how much capacity is available when and where?
18

19.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Two types of predictions are necessary for optimised usage
■ Goal: Estimate how the charging demand will
be many hours, days, or weeks into the future
■ Current station utilization irrelevant
■ Goal: Estimate waiting times while on the route
for live-optimisation
■ Current utilization and traffic patterns relevant
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Short-term prediction
Mid-term prediction
Both goals can be achieved using machine learning
19

20.
|
Chair for Electrochemical Energy Conversion and Storage Systems
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Agenda
Charging vs Parking
Profitability
Machine learning for predicting availability
Mid-term prediction
Short-term prediction
20

21.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Strong differences exist between station power, weekday, hour of the day, and
location of the station
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
■ The two graphs are based on data of 27k public charging stations in Germany in 2019 - 2021.
■ The shown value is the share of occupied stations, which does not necessarily imply charging.
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
21

22.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Site-specific information is required
■ We trained three different types of models: Random forest without site-specific information, Average
week, and random forest with site-specific information
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
 Included characteristics are
vacations, public holidays, weather,
and nearby traffic
 The prediction performance of the
model is weak with an accuracy of
79% and an MCC1 of 0.019.
RF without site information
 The RF is supplied with the value
calculated in the average week
model
 The prediction performance is
strong with an accuracy of 95% and
an MCC1 of 0.838.
RF with site information
 For each charging station, we
calculate what the average
utilization on a weekly level is and
use this as a prediction
 The accuracy is 88% and the MCC1
is 0.599.
Average week
1: Mathew’s Choice Coefficient. 1 = perfect prediction, 0 = no predictive value, -1 = perfect inverted prediction ; Source: Hecht et al. https://doi.org/10.3390/en14237834
22

23.
|
Chair for Electrochemical Energy Conversion and Storage Systems
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
Agenda
Charging vs Parking
Profitability
Machine learning for predicting availability
Mid-term prediction
Short-term prediction
23

24.
|
Chair for Electrochemical Energy Conversion and Storage Systems
The charging duration varies according to similar criteria as for the
occupation
■ Charging durations at fast-chargers is typically
limited to 30 minutes
■ Slower AC chargers are used much longer and
typically also much longer than the actual
charging process requires.
■ Using national average durations, however, is
not accurate enough since site-specific
behaviour cannot be considered.
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
40%
0%
10%
100%
20%
90%
50%
30%
60%
70%
80%
12 kW ≤ P < 25 kW
P ≥ 200 kW
4 kW ≤ P < 12 kW
25 kW ≤ P < 100 kW
P < 4 kW
100 kW ≤ P < 200 kW
Charge event duration in hours
Number
of
event
relative
to
peak
Source: Hecht et al. https://doi.org/10.48550/arXiv.2206.09582
24

25.
|
Chair for Electrochemical Energy Conversion and Storage Systems
Executive Summary
■ Hundreds of GWh of battery capacity will be available in electric vehicles by 2030
■ With average battery capacity around 40 to 60 kWh per vehicle, <20% are used for daily commuting
■ The vehicle charging mainly occurs during the first few hours of the charging event. Thus, a time
optimization is desired to increase the profitability of the charging stations.
■ The charging station availability can be predicted for future or in real time using data-driven
machine learning methods.
22.11.2022 Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
25

26.
Thank you
for your attention
Contact
Chair for Electrochemical Energy Conversion
and Storage Systems
Univ.-Prof. Dr. rer. nat. Dirk Uwe Sauer
RWTH Aachen University
Jaegerstrasse 17/19
52066 Aachen
GERMANY
www.isea.rwth-aachen.de
We thank
Tel.: +49 241 80-49310
PayasDinesh.Vartak@isea.rwth-aachen.de
batteries@isea.rwth-Aachen.de
26
Payas Vartak

27.
Any questions?
Contact
Chair for Electrochemical Energy Conversion
and Storage Systems
Univ.-Prof. Dr. rer. nat. Dirk Uwe Sauer
RWTH Aachen University
Jaegerstrasse 17/19
52066 Aachen
GERMANY
www.isea.rwth-aachen.de
We thank
27
Tel.: +49 241 80-49366
Christopher.Hecht@isea.rwth-aachen.de
https://benutzlasa.de/
Christopher Hecht

28.
Chair for Electrochemical Energy Conversion
and Storage Systems
Battery Ageing • Battery Models • Battery Diagnostics • Battery Pack Design • Electromobility • Stationary Energy Storage • Energy System Analysis
Public charging infrastructure usage in Germany
EV Charging Infrastructure
Payas Vartak, Christopher Hecht, Jan Figgener, Dirk Uwe Sauer
22.11.2022
28