PFI:AIR - TT: Optimal adaptive charging system

California Institute Of Technology, Pasadena CA

Investigators

Abstract

This PFI: AIR Technology Translation project develops software for adaptive electric vehicle (EV) charging networks and transitions it to the marketplace. We are at the cusp of a historic transformation of our energy system into a more sustainable form in the coming decades. Electrification of our transportation system will be an important component because, today, vehicles consume more than a quarter of our energy and emit more than a quarter of our energy-related carbon dioxide (CO2). Electrification will not only greatly reduce CO2 emission, but EVs can also be critical resources to help integrate renewable sources, such as wind and solar power, into our electric grid. One of the key enablers to mass EV adoption is the availability of smart charging networks. This project will design a set of novel and sophisticated algorithms that optimize a network of EV chargers, implement them in software, and pilot them in a Caltech garage where the researchers have already installed a network of programmable EV chargers. It will serve as a prototype that validates the technology and business potential of next-generation adaptive charging network (ACN). Compared with state of the art in the EV charging industry, ACN will enable massive deployment of smart chargers and provide the same charging capacity at a fraction of required infrastructure costs. This project addresses the following technology gaps as it translates from research discovery toward commercial application. At many workplaces in top EV cities in the US, there is a severe shortage of chargers relative to EVs, e.g., there is a charger for every 2-5 EVs. In the future, this ratio should be closer to 1:1. The bottleneck to a large-scale charging facility is, however, not the cost of electricity or chargers, but the limited capacity of electricity distribution system, as well as, in city centers, the real estate. The leading chargers in the current marketplace charge at their peak rates whenever EVs are plugged in. They cannot be deployed at scale without a prohibitively expensive upgrade of the electricity distribution system. The technologies to be developed in this project will optimally schedule the charging process of a network of adaptive chargers to satisfy energy requirements of all EVs within their deadlines without exceeding the capacity of the electricity distribution system whenever possible, and optimally and fairly allocate the available capacity among competing EVs otherwise. The ACN therefore maximally utilizes the most expensive resources in a charging ecosystem to provide a target charging capacity at a much lower infrastructure cost, creating a compelling value proposition. The project will apply tools from optimization theory, control and dynamical systems, and algorithm design. The focus is to develop optimization software that will be ready for commercialization at the end of the project. The project will involve undergraduate and graduate students. In addition to research and software development, the project participants will be exposed to entrepreneurship and technology transfer.