Search for: All records

The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.