Why Data-Driven Planning is Key to EV Charging Deployment 

Nationwide, $5 billion in federal funding is available to expand electric vehicle (EV) charging. To make the most of this investment through the National Electric Vehicle Infrastructure (NEVI) Formula Program, states need planning tools that reflect how people will actually travel and charge in the years ahead.

Relying on static spreadsheets to plan statewide charging networks, manually merging datasets, updating assumptions and trying to visualize corridor gaps, cannot keep up with the scale or complexity of today’s deployment needs. EV infrastructure planning requires iterative, geospatial analysis that evolves as technology and demand change.

Experts at the Center for Sustainable Energy (CSE) outline how states can use advanced, data-driven planning to build charging networks aligned with real-world needs.

Why data-driven design matters 

Federal guidance and early NEVI planning efforts consistently emphasize forecasting, analysis and grid readiness as core inputs to successful deployment. Before chargers are installed, planners need to understand where people drive, where they park, how the grid can support new demand and which communities are currently underserved. 

A data-driven process helps agencies:  

• Forecast vehicle adoption by region, community type and income level. 
• Identify charging gaps that limit access for drivers without home or workplace charging. 
• Model future utilization and grid impacts to avoid underused or overloaded sites. 
• Track outcomes over time to keep programs on target.  

Most importantly, software-enabled planning tools can replace intuition and guesswork with repeatable, evidence-based decision-making. 

From spreadsheets to geospatial planning 

Traditional planning approaches make it difficult to see the interactions between travel patterns, grid constraints and community needs. Static spreadsheets also limit scenario testing, which is a critical requirement for NEVI planning.  

Advanced geospatial planning platforms, including tools such as CSE’s Caret®, bring these elements together by integrating:  

• Traffic and travel-demand modeling. 
• Substation and hosting-capacity data. 
• Demographic and land-use indicators. 
• Existing infrastructure and reliability metrics.  

Planners can run multiple siting scenarios, test different priority weightings and see how each decision affects corridor continuity, usage and access in a single geospatial environment.

Top Questions for EV Infrastructure Planning

State and local agencies consistently return to a core set of questions when evaluating potential charging sites:  

1. How much charging will be required over time, by region and use case (corridor, community, workplace, fleet, multifamily)? 

2. Which areas are likely to be underserved by private investment without public intervention (low-income, rural, disadvantaged communities)? 

3. Where will charging load concentrate and what does that imply for distribution, substation and feeder planning? 

4. What mix of charger types and power levels is needed to meet expected dwell times and operational requirements (DCFC vs Level 2, medium/heavy-duty needs)? 

Forecast demand and identify gaps

Once priorities are set, agencies can use iterative, data-driven planning to understand where and when chargers are needed. Platforms like Caret bring together data that reveals current conditions and future needs, such as:  

• Vehicle registrations and adoption projections to estimate future charging demand. 
• Traffic and land-use data to identify travel corridors and destinations where drivers park long enough to charge. 
• Equity indicators such as proximity to disadvantaged communities, multifamily housing and employment centers. 
• Grid and site readiness information to assess where infrastructure can be expanded efficiently. 
• Utilization data from existing chargers to validate current network performance and pinpoint gaps to guide near-term upgrades.  

Layering these datasets reveals where charging demand will outpace supply and where investments will make the biggest impact, while allowing agencies to evaluate access (see U.S. Access Board recommendations), cost and equity outcomes before investments are made.  

In Louisiana, the statewide NEVI planning process used Caret EV Infrastructure Planner to evaluate potential charging sites along more than 3,500 miles of highway. The model allowed state officials and Louisiana Clean Fuels to weigh priorities such as proximity to tribal lands, military bases and disadvantaged communities. The process produced a statewide roadmap used to guide more than $75 million in NEVI funding. 

Visualize options and compare scenarios 

Interactive geospatial mapping turns planning from a static exercise into a transparent, collaborative process. With Caret and similar tools, planners can compare investment scenarios, identify gaps and produce clear, sharable maps for decision-makers and the public.   

Visualization also enables rapid scenario testing. Planners can model “what-if” situations, such as shifting priorities toward multifamily housing, and immediately see how siting recommendations would change. As virtual chargers are added, Caret automatically recalculates where the next installation would deliver the most benefit.  

These capabilities shorten planning cycles and build shared understanding across agencies, utilities and community partners.  

The County of San Diego used this approach with Caret to produce detailed maps showing high-priority charging areas and phased installation plans tailored to different community types. Using the iterative mapping process, as top sites were selected, suitability for remaining areas was recalculated to optimize equity outcomes and cost efficiency. 

Build a culture of transparent data reporting 

Data collection and reporting practices should be established upfront, long before chargers are deployed.   

Data points to monitor include:  

• Charger uptime and reliability to validate network dependability. 
• Utilization rates and dwell times to understand how, when and where drivers charge. 
• Equity and access metrics such as usage in disadvantaged communities or multiunit housing areas. 
• Energy and cost data to evaluate grid impacts and operational efficiency.  

For example, the California Electric Vehicle Infrastructure Project (CALeVIP), the largest statewide EVI project in the nation, uses aggregated data to track charger reliability and usage. This data helps identify trends, measure accessibility in disadvantaged communities and guide future funding rounds.  

State and local agencies can use shared or open-access dashboards to visualize charger locations, utilization and reliability, helping allocate funding more effectively and build public trust. 

Turn data into direction 

The road to accelerated EV charger deployment is built on data. As NEVI implementation moves from initial corridor coverage to long-term network optimization, agencies that invest early in robust forecasting, mapping and transparent reporting will be better positioned to adapt, scale and course-correct over time.  

• State DOTs can get CSE’s NEVI Playbook for more detailed guidance based on CSE’s experience administering the largest state EV infrastructure program in the nation. 
• Want to see how data can power your state’s NEVI plan? Contact CSE at consult@energycenter.org.  
• Read more articles in this series