A team of researchers at the University of California, San Diego (UCSD) has developed a computational model to assist organizations in designing efficient workplace charging networks for electric vehicles (EVs). The study, published in the April issue of Renewable Energy, aims to support firms in accommodating employees transitioning to EVs.
The researchers utilized data from over 800 EV drivers at UCSD, which boasts 439 charging stations—the largest network among English-speaking universities. They discovered that drivers tend to charge their vehicles more frequently than previously assumed, often preferring not to let their charge drop below 60%.
“We have demonstrated that using data from real EV drivers—rather than relying on idealized or regionally-averaged assumptions—can have a significant impact on the optimal design of a charging network,” said Jeff Myers, co-first author of the paper and a research associate with the UCSD Deep Decarbonization Initiative.
The team plans to make the model publicly available, enabling organizations to input data such as annual driving mileage, commuting distance, and home charging availability to tailor their charging infrastructure effectively. This approach aims to create cost-effective, efficient, and environmentally friendly charging solutions, supporting the broader adoption of electric vehicles.
