Diese Präsentation wurde erfolgreich gemeldet.
Die SlideShare-Präsentation wird heruntergeladen. ×

PUBLIC CHARGING INFRASTRUCTURE USAGE IN GERMANY

Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Wird geladen in …3
×

Hier ansehen

1 von 28 Anzeige
Anzeige

Weitere Verwandte Inhalte

Weitere von iQHub (20)

Aktuellste (20)

Anzeige

PUBLIC CHARGING INFRASTRUCTURE USAGE IN GERMANY

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

×