Large battery systems with 100s/1000s cells are being used to power various physical platforms. For example, automobiles are transitioning from conventional powertrains to (plug-in) hybrid and electric vehicles (EVs). To achieve the desired efficiency of EVs, significant improvements are needed in the architecture and algorithms of battery management. This project will develop a new comprehensive battery management architecture, called Smart Battery Management System (SBMS). The research is expected to bridge the wide gap existing between cyber-physical system (CPS) research and electrification industry communities, provide environment-friendly solutions, increase the awareness of CPS, and develop skilled human resources. This project will incorporate and enhance a battery management system (BMS) by including battery state-of-charge (SoC) and state-of-health (SoH) algorithms as well as power management strategies on both pack and cell levels. Specifically, it consists of five main research tasks: (i) design a dynamically reconfigurable energy storage system to tolerate harsh internal and external stresses; (ii) develop cell-level thermal management algorithms; (iii) develop efficient, dependable charge and discharge scheduling algorithms in hybrid energy storage systems; (iv) develop a comprehensive, diagnostic/prognostic (P/D) algorithm with system parameters adjusted for making optimal decisions; and (v) build a testbed and evaluate the proposed architecture and algorithms on the testbed. This research will advance the state-of-the-art in the management of large-scale energy storage systems, extending their life and operation-time significantly, which is key to a wide range of battery-powered physical platforms. That is, SBMS will enable batteries to withstand excessive stresses and power physical platforms for a much longer time, all at low costs. SBMS will also serve as a basic framework for various aspects of CPS research, integrating (cyber) dynamic control and P/D mechanisms, and (physical) energy storage system dynamics.
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University of Michigan Ann Arbor
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National Science Foundation
Kang Shin Submitted by Kang Shin on December 21st, 2015
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