The objective of this research is to develop a novel adaptive configurable multi-cell battery system for power-aware computing. Tasks include: (1) a study of adaptive configurable multi-cell battery discharge behaviors under a time-varying load through modeling and simulation; (2) investigation and development of an adaptive control and optimization framework to dynamically select the optimal cell topology configuration for the current load based on each cell's state of health and state of capacity; and (3) establishment of an integrated power-aware embedded platform for testing and evaluating the proposed adaptive configurable multi-cell battery with a wide range of benchmark load applications.
Intellectual Merit: The project investigates a novel design concept for multi-cell batteries with the goal of increased utilization of battery capacity. The research also investigates a power management system with simple direct current-to-direct current conversion circuitry that is intended to increase the efficiency of multi-cell battery discharge. Finally, the project seeks to develop a fundamental understanding of multi-cell battery behavior, which is currently not well understood. This project seeks to provide solutions for a variety of domain-specific applications.
Broader Impacts. By improving the discharge efficiency and life span of multi-cell batteries, the broader impacts of this project are to increase a system's operating time with the same battery capacity and to reduce environmental impact of discarded batteries. Results, outcomes, software tools, benchmarks, and educational materials will be disseminated through a project web site, as well as through journal and conference publications. A new course on power-aware computing is being jointly developed and taught at the investigators' univer
PI: Prof. Song Ci Institution: University of Nebraska-Lincoln Award Number: 0801736 Goal: Dynamically reconfigure the internal connections among the different battery cells to maximize the remaining usable capacity of a given battery and to avoid single-point failure and safety issues in battery packs. Motivation: Battery pack operating time is among the most important performance parameters for the networked embedded systems. Enhancing the battery pack operating time is a big challenge due to: 1) the capacity and health difference of each batter cell in a given pack; 2) the nonlinear charge/discharge behaviors of a given battery chemistry; and 3) the existing fixed series-parallel battery pack technology has been changed for more than 100 years, which has largely ignore the fact that each battery should be managed differently. Outcomes: In this project, we have successfully 1) built the first battery testbed facility at the University of Nebraska-Lincoln; 2) carried out extensive battery testing and modeling research and derived the first theoretical SOC (state of charge) model for arbitary battery topologies; 3) introduced key concepts from network and intelligent system domain into battery management systems to achieve significant performance gain in the proposed system; 4) integrated the research outcomes into teaching and community outresearch for STEM education.
