At SynergyBoat, we recognize the need to address the complexities of planning electric vehicle (EV) charging infrastructure. As EV adoption surges globally, cities, utilities, and businesses face significant hurdles in determining optimal locations for charging stations. Poorly placed chargers can lead to underutilization, grid strain, or inequitable access, slowing the transition to sustainable transportation. In this blog, we at SynergyBoat outline the core challenges of EV charging infrastructure planning, explore various approaches to solve them, and share a glimpse of the solution we think will transform this process.

We have explored the critical challenges surrounding EV charging infrastructure and planning in the blog below. Read it to gain a clear understanding of the issues shaping the future of e-mobility.

The Problem: Why EV charging infrastructure planning is so tough

The rapid rise in EV usage has outpaced the development of charging infrastructure, creating a pressing challenge for stakeholders.

The EV charging market is witnessing exponential growth, fueled by the global push for sustainable mobility and stricter emissions regulations. As consumer demand for electric vehicles continues to surge, supported by technological advancements and substantial government incentives, the need for reliable and accessible charging infrastructure has become paramount.

Electric vehicle supply equipment: a $100 billion market by 2040, led by charge point operators, according to a PwC analysis.
Source: PwC Analysis

Cities need chargers that are accessible and serve high-demand areas without overwhelming urban planning resources. Utility providers must ensure chargers align with grid capacity to avoid costly upgrades, overloading, and downtime. Businesses, such as retail centers or fleet operators, want stations that attract customers or optimize operations. However, several obstacles complicate this process:

• Balancing Multiple Factors: Planners must consider accessibility (e.g., proximity to highways), demand (e.g., population density and EV adoption), and infrastructure (e.g., grid capacity), often with conflicting priorities. For instance, a high-traffic area might lack sufficient power supply.
• Data Overload: Planners deal with vast amounts of location data—roads, population, and traffic patterns—which can be time-consuming to analyze manually.
• Equity Concerns: Discussions on public platforms highlight the need for chargers in underserved areas, such as rural regions, to ensure fair access. Poor planning can exacerbate disparities.
• Cost and Efficiency: Incorrect placements lead to underused chargers or grid overloads, wasting resources and frustrating drivers.

These challenges result in lengthy planning cycles, inefficient investments, and infrastructure that fails to meet user needs. We at SynergyBoat believe a smarter, data-driven approach can address these issues, streamlining planning and enhancing outcomes.

Approaches to solve EV Charging Infrastructure planning challenges

Several strategies can help tackle these challenges, each with strengths and limitations. Below, we explore key approaches that can improve EV infrastructure planning:

Manual Analysis with GIS Tools:

• Description: Planners use Geographic Information Systems (GIS) tools like QGIS or ArcGIS to manually analyze spatial data, such as road networks or population density, to identify potential charger sites.
• Strengths: Offers precise control over data analysis, allowing planners to visualize locations and assess factors like proximity to highways.
• Limitations: Time-intensive and reliant on expert knowledge. Manual processes struggle to balance multiple criteria or handle large datasets efficiently.
• Example: A planner might use QGIS to map sites near high-traffic roads but spend weeks integrating population or grid data.

Heuristic-Based Decision Models:

• Description: Simple rule-based models prioritize sites based on predefined criteria, such as “choose locations within 1 km of major roads.”
• Strengths: Quick to implement and easy to understand, suitable for small-scale projects.
• Limitations: Oversimplifies complex decisions, often ignoring trade-offs between factors like demand and infrastructure. Lacks flexibility for user-defined priorities.
• Example: A city might prioritize sites near commercial zones but miss high-demand residential areas.

Multi-Criteria Decision Analysis (MCDA):

• Description: Methods like the Analytic Hierarchy Process (AHP) or Weighted Sum Model allow planners to assign weights to criteria (e.g., 40% accessibility, 30% demand, 20% grid capacity, 10% cost) and rank sites systematically.

• Strengths: Balances multiple factors and incorporates stakeholder priorities, ensuring consistent decisions. A 2024 study from Istanbul used AHP to optimize charger placement, demonstrating its effectiveness.
• Limitations: Requires structured data inputs and can be complex to implement without software support.
• Example: AHP could rank sites by weighing accessibility higher than cost, producing a clear list of top locations.

Diagrammatic representation of the MCDA process

Predictive Modeling and Machine Learning:

• Description: Machine learning models, such as regression or clustering, predict demand based on factors like population density or traffic patterns, enhancing decision-making.
• Strengths: Adds predictive power, identifying high-demand areas proactively. Can integrate with MCDA for better rankings.
• Limitations: Needs quality data and expertise to research and develop models, train, and deploy them. Simple models may lack precision for complex scenarios.
• Example: A regression model could forecast EV charging demand in busy urban zones, guiding site selection.

Integrated Software Solutions:

• Description: Web-based platforms combine GIS, MCDA, and predictive models into a user-friendly interface, automating data analysis and decision-making.
• Strengths: Streamlines planning, reduces manual effort, and supports diverse stakeholders. Can incorporate real-time data or user inputs for flexibility.
• Limitations: Development requires significant upfront investment, and integration of multiple technologies can be complex and demand significant engineering effort.
• Example: A platform could let planners input priorities, visualize sites on a map, and get ranked recommendations instantly.

Each approach has merit, but combining GIS for spatial analysis, MCDA for structured decisions, and AI for demand forecasting offers the most promise. This integrated approach can address data complexity, balance priorities, and predict future needs, making planning faster and more effective.

How Leading Cities Are Powering the EV Future

The Oslo model

Oslo has been a global leader in EV adoption, with over 50% of new cars sold in 2017 being either fully battery electric (37.5%) or plug-in hybrids (14.1%). Currently, electric vehicles constitute 16% of the city’s total cars, with 9% fully electric and 7% plug-in hybrids. However, the rapid rise in EV usage has reduced the charger-to-vehicle ratio from 1 charger per 4 cars to 1 per 10 cars, straining infrastructure.

Approach:

• Strategic Charger Deployment: Oslo is addressing this shortfall by installing fast chargers along key corridors entering and exiting the city, in partnership with private companies. Additionally, a network of semi-fast chargers (7.4–22 kW) is being rolled out to increase charging speed and vehicle turnover, optimizing the use of limited public space.
• Public-Private Collaboration: The city leverages partnerships with companies like Fortum, which has deployed over 1,200 public charging points, to enhance infrastructure efficiency.
• Policy Support: Subsidies covering up to 60% of installation costs and free access to city-owned locations via public tenders incentivize private investment.

Challenges:

• Insufficient Charger Density: The declining charger-to-vehicle ratio highlights the need for rapid infrastructure expansion to meet demand.
• Urban Space Constraints: Limited public space requires precise placement to avoid disrupting other urban functions.
• Grid Capacity: Fast chargers demand significant power, necessitating coordination with utilities to avoid grid strain.

A smart spatial decision support system could enhance Oslo’s planning by:

• Using GIS to identify high-traffic corridors and zones with high EV density for optimal charger placement.
• Applying Multi-Criteria Decision Analysis (MCDA) to balance accessibility, grid capacity, and cost, ensuring efficient use of public space.
• Incorporating predictive models to forecast EV growth, enabling proactive infrastructure scaling.

Source: World Economic Forum, 2018; C40 Cities EV Charging Models Report, Pages 40–41

The Barcelona Model

Barcelona, a dense and compact city, has prioritized equitable and strategic EV charging infrastructure (EVCI) deployment to support its Electromobility Strategy (2018–2024). The city aims to reduce transport emissions and achieve 1,000 public charging points by 2023, with 711 points already installed (7.1 points/km²).

According to recent updates, Endolla Barcelona now operates 1,027 public charging points across the city, making it Spain’s largest public electric mobility network. (Barcelona de Serveis Municipals, S.A.)

With more than 1,000 charging points scattered all over the city, Endolla Barcelona, promoted by the Barcelona City Council via BSM, is the most extensive electromobility network in southern Europe

— Barcelona de Serveis Municipals, S.A.

Approach:

• Public-Led Deployment: Barcelona Municipal Services (B:SM) operates as the Charging Point Operator (CPO) under the Endolla brand, focusing on territorial equity. Slow chargers (Level 2) are placed in city-managed parking lots for long-term use (e.g., workplaces), while fast chargers (Level 3) are on-street for high-turnover areas.
• Interoperability: The SMOU app integrates chargers from B:SM and private partners, ensuring seamless access across providers.
• Funding: The city invested €17 million, primarily from municipal and EU funds, to proactively build infrastructure ahead of EV demand, which remains low (20% charger utilization).

Challenges:

• Low Demand: Spain’s low EV adoption rate means chargers are underutilized, complicating financial sustainability.
• Urban Space Constraints: Limited street space in a dense city requires careful site selection to balance charging needs with other urban functions.
• Grid Integration: Ensuring sufficient power supply for fast chargers in high-traffic areas is complex and costly.

A smart spatial decision support system could streamline Barcelona’s planning by:

• Using GIS to map optimal charger locations based on population density, traffic patterns, and grid capacity.
• Applying Multi-Criteria Decision Analysis (MCDA) to prioritize sites that balance accessibility, equity, and cost, ensuring chargers are placed in underserved neighborhoods.
• Incorporating predictive models to forecast future EV demand, reducing the risk of overbuilding infrastructure.

Source: C40 Cities EV Charging Models Report, Pages 19–20

The Shenzhen model

Shenzhen, a pioneer in EV adoption, has achieved 57% new energy vehicle (NEV) penetration in 2023. The city has a dense network of public chargers, supported by a robust public-private partnership model and significant subsidies.

Approach:

• Utility-Led Model: China Southern Power Grid partners with private Charge Point Operators (CPOs) to deploy chargers, with the public sector defining suitable locations.
• Incentive Structure: From 2010 to 2020, subsidies ranged from 30% of upfront charger costs to ¥600/kW for DC chargers, driving rapid deployment.
• Data-Driven Placement: Shenzhen collects data on existing charger usage and EV patterns to inform new site selections, though land scarcity remains a challenge.

Challenges:

• Land Scarcity: Dense urban areas make it hard to secure central locations, leading to long queues at popular chargers and underutilized suburban stations.
• Interoperability Issues: Multiple CPOs with varying standards complicate user experience, requiring multiple accounts.
• Balancing Demand and Supply: High EV penetration strains charger availability in central areas, necessitating precise placement.

A smart spatial decision support system could optimize Shenzhen’s EVCI planning by:

• Leveraging GIS to identify underutilized land parcels or semi-public spaces (e.g., parking lots) for charger installation.
• Using AHP to weigh criteria like proximity to commercial hubs, grid capacity, and user convenience, addressing queueing issues.
• Enhancing interoperability by recommending standardized connectors and protocols, improving user experience across CPOs.

Source: C40 Cities EV Charging Models Report, Pages 35–36, 43

A Glimpse of Our Vision

We at SynergyBoat are exploring solutions that builds on the strengths of these approaches to transform EV charging infrastructure planning. A web-based platform that integrates GIS, AHP, and a predictive model to streamline site selection. Our goal is to create a tool that allows users, such as city planners or utility managers, to define priorities, visualize potential sites on an interactive map, and receive data-driven recommendations for optimal charger locations.

Here’s a sneak peek:

• GIS Integration: We’re using tools like PostgreSQL with PostGIS to analyze spatial data, such as road networks or population density, to identify suitable sites.
• AHP Decision-Making: We let users assign weights to criteria (e.g., accessibility or demand) and rank sites systematically, ensuring decisions align with their goals.
• Predictive Insights: We’re incorporating machine learning models to forecast demand, enhancing the accuracy of our rankings.
• User-Friendly Interface: A web interface will make it easy to input priorities and view results on a map.

We’re testing this concept on a small scale, using sample data for a 100 km² city, to validate its feasibility. By combining these technologies, we aim to create a solution that reduces planning time, optimizes charger placement, and supports equitable access, potentially changing how stakeholders approach EV infrastructure.

Why This Matters?

Our solution has the potential to deliver significant value to stakeholders. For municipalities, it could streamline urban planning, enabling faster decisions with fewer resources. Utility providers could align chargers with grid capacity, avoiding costly upgrades. Businesses, such as retail centers or fleet operators, could strategically place chargers to attract customers or improve efficiency. By addressing equity concerns, our solution could also ensure chargers reach underserved areas, aligning with growing calls for inclusive infrastructure seen in discussions on public forums and platforms.

From a business perspective, we aspire to lead in a critical market. A successful solution could evolve into a scalable platform, offering solutions to cities, utilities, and private organizations. This approach not only addresses a pressing need but also supports sustainability goals by facilitating EV adoption, contributing to a greener future.

Let’s Team Up to Build the Future of EV Charging

Here at SynergyBoat, we’re fired up about making EV charging infrastructure planning easier, smarter, and fairer. We’re working on a platform that blends GIS mapping, AHP decision-making, and predictive models to help place chargers where they’ll do the most good. We need partners who share our excitement for a sustainable future. That’s where you come in! We’re inviting cities, utilities, businesses, and organizations to join us on this journey.

Why Work with Us?

• Lead the Charge: Help us create a tool that could change how the world plans EV charging, putting your organization at the forefront.
• Tackle Real Problems: Whether it’s making chargers easier to access or keeping the grid humming, your input can help solve big challenges.
• Push for a Greener Tomorrow: Be part of a solution that makes EVs more practical for everyone, supporting a cleaner planet.
• Shape the Solution: Get in early to test our platform and help us make it work for your specific needs.

Who We Want to Join Us

We’re looking for folks who get as excited as we do about better EV infrastructure. Are you a city planner wanting to make your streets EV-friendly? A utility company figuring out how to balance the grid? Or maybe a business looking to draw in EV-driving customers with smart charger locations? Whoever you are, if you’re ready to make a difference, we’d love to team up.

How to Get Started

Want to jump in? Drop us a line at ahoy@synergyboat.com to talk about how we can work together. Let’s build something that makes EV charging accessible, efficient, and fair for everyone. Come on board, and let’s power up the future together!

Conclusion

As electric vehicles become an integral part of our daily lives, getting EV charging infrastructure right is more important than ever. Poorly planned networks can lead to underused chargers, grid strain, and inequitable access, challenges that slow the transition to sustainable mobility.

At SynergyBoat, we’ve explored these complexities and drawn lessons from leading cities around the world. We’ve seen that success requires balancing demand, accessibility, and grid capacity, all while ensuring fair and inclusive access for all communities.

That’s why we’re building a next-generation EV charging infrastructure planning platform: a tool that combines GIS-powered spatial analysis, structured decision-making through MCDA, and predictive insights through machine learning. Our goal is simple: to help planners, utilities, and businesses place chargers where they’ll have the greatest impact, today and in the future.

This isn’t just about putting more chargers on the map. It’s about enabling resilient, adaptable, and equitable infrastructure that supports the exponential growth of EV adoption worldwide.

We’re excited about this vision, but we can’t do it alone. That’s why we’re inviting cities, utilities, businesses, and innovators to collaborate with us and help shape this solution.

If you’d like to explore how we can work together, reach out at

ahoy@synergyboat.com

Let’s build a future where EV charging is accessible, intelligent, and built to serve everyone. Let’s power it, together.