PFI:AIR - TT: Optimal adaptive charging system

该PFI：AIR技术翻译项目为自适应电动汽车（EV）充电网络开发软件，并将其推向市场。 在未来几十年里，我们正处于能源系统向更可持续的形式进行历史性转变的风口浪尖。交通系统的电气化将是一个重要组成部分，因为今天，汽车消耗了我们四分之一以上的能源，排放了四分之一以上的与能源有关的二氧化碳（CO2）。 电气化不仅可以大大减少二氧化碳排放，而且电动汽车也可以成为帮助将风能和太阳能等可再生能源整合到电网中的关键资源。 大规模采用电动汽车的关键推动因素之一是智能充电网络的可用性。 该项目将设计一套新颖而复杂的算法，优化电动汽车充电器网络，在软件中实现它们，并在加州理工学院的车库中进行试点，研究人员已经在那里安装了一个可编程电动汽车充电器网络。 它将作为验证下一代自适应充电网络（ACN）技术和商业潜力的原型。 与电动汽车充电行业的最新技术相比，ACN将实现智能充电器的大规模部署，并以所需基础设施成本的一小部分提供相同的充电容量。该项目解决了从研究发现到商业应用的以下技术差距。 在美国顶级电动汽车城市的许多工作场所，相对于电动汽车，充电器严重短缺，例如，每2-5辆电动汽车有一个充电器。 未来，这一比例应该接近1：1。 然而，大规模充电设施的瓶颈不是电力或充电器的成本，而是配电系统的有限容量，以及城市中心的真实的房地产。当前市场上领先的充电器在插入电动汽车时以其峰值费率充电。如果不对配电系统进行昂贵的升级，它们就无法大规模部署。该项目开发的技术将优化自适应充电器网络的充电过程，以满足所有电动汽车在其截止日期内的能源需求，而不会超过配电系统的容量，否则将在竞争电动汽车之间优化和公平地分配可用容量。 因此，ACN最大限度地利用了充电生态系统中最昂贵的资源，以更低的基础设施成本提供目标充电容量，创造了引人注目的价值主张。该项目将应用优化理论，控制和动力系统以及算法设计的工具。 重点是开发优化软件，在项目结束时准备商业化。 该项目将涉及本科生和研究生。 除了研究和软件开发，项目参与者将接触到创业和技术转让。

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.