Identifying Optimal Locations for Truck Charging through a Spatiotemporal Analysis of Freight Demand and Renewable Electricity Availability and Pricing

Research Lead: Miguel Jaller

UC Campus(es): UC Davis

Problem Statement: Freight transportation significantly contributes to pollutant and greenhouse gas emissions. Medium‐ and heavy‐duty trucks have lagged behind light‐duty vehicles in decarbonization efforts, necessitating more ambitious goals to reduce their emissions. Battery electric trucks have been considered an important decarbonization option in freight transportation. However, a major limitation to electrifying the long‐haul segment is the need for large batteries to complete long routes, reducing cargo capacity and increasing ownership costs. Electrification decisions are also influenced by factors such as electricity pricing, investments in and availability of charging infrastructure, vehicle characteristics (e.g., vehicle type, range), and driving patterns (daily mileage, trip timing, dwell time). These factors lead to varying charging costs and increased total cost uncertainty, which can hamper fleets’ willingness to adopt battery electric trucks. Additional challenges hindering battery electric truck deployment include lacking comprehensive data on truck activity patterns, diverse operational requirements, and charging needs across fleet segments.

Project Description: This project aims to analyze intra-day truck and cargo activity patterns along with renewable electricity availability and pricing to pinpoint optimal locations for charging infrastructure. The study will integrate a large sample of load/cargo data (e.g., origins, destinations, pick-up and drop-off times, and weights), a sample of fleet data (e.g., location, number, and types of vehicles, daily activity patterns, return to depot patterns, and other characteristics), wholesale intra-day marginal price electricity data, spatial generation of renewable energy and availability, and other data sources (e.g., existing and planned infrastructure). By analyzing these datasets, the project will generate charging models for different fleet segments, such as long-haul and middle-mile, considering their specific operational patterns and constraints. Additionally, the models will help identify critical hotspots locations for establishing charging stations.

Status: In Progress

Budget: $100,000