As battery technology advances and battery system capacity expands, its penetration in buildings enables building consumers to be energy prosumers, interact with the grid to provide services, and facilitate grid-interactive efficient buildings (GEB). As such, these Battery Energy Storage Systems (BESS) are expected to operate for a prolonged time without significant degradation, be efficient and reliable, meet the changing needs of the building and grid’s requirements, enhance grid resilience, and be fault tolerant. These novel features can be met with the application of Reconfigurable Battery Systems (RBS) technology. However, research and development of RBS integration with the built environment is limited and is the focus of this ongoing research project. This research focuses on developing versatile scalable multi-port switching topology, proposing novel sensors to improve the life span of each battery cell in the RBS system, and applying Reinforcement Learning (RL) to operate the building and its RBS as a dispatchable resource. A literature review, background information, preliminary results, and future work are presented.

Accurate assessment of the state of charge (SoC) and state of health (SoH) of batteries provides critical information for effective operation in various battery applications. Such information optimizes charging, discharging, fault isolation, and maintenance decisions. Conventional techniques have shortcomings, including accuracy, cost, speed, and invasiveness. Recent research has introduced novel techniques, many exploiting magnetic field changes that occur during battery operation. This literature review summarizes conventional and novel measurement methodologies, highlighting any limitations of each. While magnetic field and X-ray approaches can overcome many of the shortcomings of conventional methods, further research is necessary to reduce costs, enhance safety, and enable real-time monitoring. The development of accurate, quick, and cost-effective measurement techniques will accelerate the integration of batteries into a rapidly increasing range of applications, including battery energy storage systems for behind the meter in buildings, facilitating electrification goals and the expansion of renewable energy.

Using data from Omaha, Nebraska, this conference paper examines the aggregate impacts and predictability of residential electric vehicle (EV) charging behavior in a local area. The study highlights patterns in collective charging behavior by examining temporal patterns in total energy demand and simultaneous connections to charging infrastructure. This analysis informs decision-making on energy infrastructure, load management, charge scheduling, and urban planning, contributing valuable knowledge to integrating EVs into existing energy frameworks. The dataset tracks a fixed number of regularly charging users over a year to identify trends scalable to future penetration levels. Both the average demand and variability are analyzed across various timeframes to assess the stability and predictability of residential charging.