eTranSym: A Tool for Projecting Demand for and Identifying Gaps in Public Electric Vehicle Charging Infrastructure

Research Team: Jiaqi Ma (lead), Ning Zhang, Yueshuai He, Qinhua Jiang, and Changju Lee

UC Campus(es): UCLA

Problem Statement: The market for electric passenger vehicles and trucks heavily relies on the installation of public chargers, but the current distribution of electric vehicle (EV) charging facilities are not meeting the needs of disadvantaged communities. Existing studies discuss needs for public chargers at the state, county, and city level; however, the current inequitable allocation suggests a need to identify local infrastructure gaps especially for low-income disadvantaged communities.

Project Description: The researchers developed a tool, named eTranSym, to project the demand for and gaps in public charging infrastructure for both passenger EVs and electric trucks. Los Angeles County serves as the test bed for the project. First, they reviewed existing studies and performance tools together with public charger operation techniques and California transportation electrification policies. Second, they developed the eTranSym tool for use with small scale transportation analysis zones (TAZs), to build a model of the current transportation system in the county. The key advantage of this tool is its capability to simulate individual travel behavior across different socio-economic groups with special attention to disadvantaged areas. Based on the simulation, eTranSym assesses charging performance and gaps in and across communities under different scenarios, and then considers the growth of the EV market, transportation system electrification targets, and individual charging habits to identify each community’s future charging demand. Smart charging strategies that provide system charging information to EV users can help them manage their charging behavior. The researchers tested the eTranSym model by simulating a smart charging strategy using variable time-of-day electricity prices to determine how well it can improve the charging decisions of EV users by encouraging them to charge their vehicles when electricity rates are low thus reducing energy consumption during peak periods.

Status: Completed

Budget: $80,000

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