ArticleiScience2026
A framework for siting public EV charging during renewable-aligned hours.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Utilities must incorporate electric vehicle (EV) charging demand alongside variable generation. Early EV adopters relied on home charging, creating evening loads poorly aligned with solar. Existing demand-side management strategies often rely on time-of-use pricing or automated control, which can limit equitable access. As public charging is deployed, location selection may support charging when solar is abundant. This study develops a replicable, data-driven framework to identify candidate locations for public EV charging based on the overlap between observed visitation and grid-favorable charging hours. The preferred time period for charging in Texas is from 10 a.m. to 3 p.m. Using mobile phone location data for a case study in San Antonio, the framework identified over 100 candidate locations with visitation patterns aligned with this window. Promising locations include retail centers, financial institutions, and lunch-oriented restaurants. Rather than optimizing charger operation after deployment, the framework provides a siting approach to align public charging with demand-side management goals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.