To contribute to the realization of Aachen's ambitious climate goals by ensuring a sustainable and efficient expansion of electric vehicle (EV) infrastructure. Our AI-driven solution will leverage existing research to strategically identify optimal locations for new EV charging stations, enhancing accessibility, usability, and environmental impact while advancing Aachen's transition to a Smart City.

Climate and Energy Goals

1. European Union (EU): Achieve 42.5% renewable energy usage by 2030, supporting the transition to a low-carbon economy【1】.

2. Germany: Climate-neutral by 2045, driven by the rapid adoption of renewable energy and electrification of transport【2】.

3. North Rhine-Westphalia (NRW): A 38% increase in EVs in 2024 compared to the previous year demonstrates rapid growth potential in EV adoption【3】.

4. Aachen: Plans for 2,400 public EV charging stations by 2030 (from the current 500), highlighting the city’s commitment to supporting sustainable mobility【4】.

The Role of EVs

• Road transport accounts for a significant share of CO2 emissions. The shift to EVs is pivotal in reducing emissions【5】.

• Research shows that the availability and location of EV charging stations are directly correlated with EV adoption rates【6】.

Objective

To develop and deploy an AI-based system that predicts the optimal locations for new EV charging stations in Aachen.



Approach

Our solution integrates advanced machine learning algorithms and real-world data to:

1. Analyze Traffic Patterns: Utilize Aachen's mobility dashboard to understand vehicle flow and demand hotspots【7】.

2. Evaluate Points of Interest (POIs): Consider parking areas, shopping malls, residential zones, and workplaces to ensure user convenience.

3. Assess Current Utilization: Use data on existing charging station usage to identify gaps in the current infrastructure.

4. Simulate Future Demand: Predict the impact of socio-economic factors and city growth on future EV adoption.

This is on the basis of existing research on the topic 【8】【9】.



Data Sources

• Utilize sources from Aachen Mobility Dashboard【7】such as traffic and charging station utilization data.

• Regional EV adoption and charging infrastructure data from local partners (e.g., Smartlab, IT.NRW, Stadt Aachen, RWTH Aachen).

• Additional mobility and geographic information datasets.

Key Features

1. Dynamic Optimization: The algorithm will continuously update recommendations based on new data.

2. Scalability: Designed to scale for future extensions, such as incorporating real-time traffic feeds.

3. Sustainability: Supports resource-efficient deployment of EV infrastructure, minimizing costs while maximizing environmental benefits.

4. Optimal Placement: Ensures that new charging locations are strategically positioned where they are needed most, maximizing accessibility, utilization, and impact on EV adoption.

Benefits for Aachen

1. Enhanced EV Adoption: Strategic placement of charging stations will drive higher EV adoption rates.

2. Climate Neutrality: Accelerates progress toward Aachen’s climate-neutral target by 2030.

3. Improved Mobility: Supports smarter urban mobility and reduces dependency on fossil fuels.

Broader Implications

Our project lays the groundwork for a replicable model applicable to other cities, reinforcing Aachen’s position as a leader in smart, sustainable urban planning.

1. City of Aachen: Policy and infrastructure planning.

2. STAWAG/Regionetz: Data on existing infrastructure and future plans.

3. RWTH Aachen University: Research support and expertise in AI and mobility.

Our proposal directly aligns with the Smart City Aachen vision by leveraging AI to deliver sustainable, scalable, and impactful solutions. Together, we can pave the way for a greener and smarter future for Aachen.

1.  E  U Renewable Energy Targets 

2.  Germany's Climate Goals 

3.  EV Growth in NRW 

4.  Aachen’s EV Plans 

5.  CO2 Emissions and Transport 

6.  Impact of EV Infrastructure on Adoption 

7.  Aachen Mobility Dashboard 

8.  Research on Placement of EV Charging Stations 

9.  Research on Planning of EV Charging Stations