CPS: Synergy: Adaptive Management of Large Energy Storage Systems for Vehicle Electrification (CNS-1446117)

 

Project description

Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. In particular, and of special importance to the automotive industry, is the transition from conventional powertrains to (plug-in) hybrid and electric vehicles (EVs), all of which are subject to environmental and operational variations. Current state-of-the-art still needs significant improvements in the architecture and algorithms of battery management before achieving the desired levels of efficiency and performance. To meet this need, we propose to develop — independently of any specific platform — a new comprehensive battery management architecture, called Smart Battery Management System (SBMS). The architecture will incorporate and enhance a BMS that includes battery state-of-charge (SoC) and state-of-health (SoH) algorithms as well as battery power management strategies on both pack and cell levels.The main research tasks of this project are to (i) design a dynamically reconfigurable energy storage system to withstand harsh internal and external stresses; (ii) develop cell-level thermal management algorithms; (iii) develop efficient, dependable charge and discharge scheduling algorithms; (iv) develop comprehensive, diagnostic/prognostic algorithms with system parameters adjusted for making optimal decisions; and (v) build a testbed, implement and evaluate the proposed architecture and algorithms on the testbed. This project is supported by NSF: CNS-1446117.


People

Faculty

  • Kang G. Shin, Professor/Principal Investigator. Email: kgshin at eecs.umich.edu
  • Wei Lu, Professor, Email: weilu at umich.edu

Research Fellows

  • Liang He

Students

  • Eugene Kim, Grad. Student.
  • Bin Wu, Grad. Student.

Reports

  • Liang He, Zhe Yang, Yu Gu, Tian He, and Kang G. Shin. SoH-Aware Reconfiguration in Battery Packs. IEEE Transactions on Smart Grid, in press. PDFpdf
  • Liang He, Yu-Chih Tung, and Kang G. Shin. iCharge: User-Interactive Charging of Mobile Devices. ACM MobiSys’17, 2017. PDFpdf
  • Liang He, Sunmin Kim, Kang G. Shin, Guozhu Meng, and Tian He. Battery State-of-Health Estimation for Mobile Devices. ACM/IEEE ICCPS’17, Pittsburgh, PA, 2017. PDFpdf
  • Liang He, Guozhu Meng, Yu Gu, Cong Liu, Jun Sun, Ting Zhu, Yang Liu, and Kang G. Shin. Battery-Aware Mobile Data Service. IEEE Transactions on Mobile Computing, Vol. 16, No. 6, pp. 1544-1558, 2017. PDFpdf
  • Liang He, Sunmin Kim, and Kang G. Shin. A Case Study on Improving Capacity Delivery of Battery Packs via Reconfiguration. ACM Transactions on Cyber-Physical Systems, Vol. 1, No.2, 2017. PDFpdf
  • Eugene Kim, Jinkyu Lee, Liang He, Youngmoon Lee, and Kang G. Shin. Offline Guarantee and Online Management of Power Demand and Supply in Cyber-Physical Systems. IEEE RTSS’16, Porto, Portugal, 2016.PDF pdf PDF supplement
  • Liang He, Sunmin Kim, and Kang G. Shin. Resting Weak Cells to Improve Battery Packs’ Capacity Delivery via Reconfiguration. ACM e-Energy’16, Waterloo, Canada, 2016. PDF pdf
  • Liang He, Sunmin Kim, and Kang G. Shin. *-Aware Charging of Lithium-ion Battery Cells. ACM/IEEE ICCPS’16, Vienna, Austria, 2016. PDFpdf
  • Liang He, Yu Gu, Cong Liu, Ting Zhu, and Kang G. Shin. SHARE: SoH-Aware Reconfiguration to Enhance Deliverable Capacity of Large-Scale Battery Packs. ACM/IEEE ICCPS’15, Seattle, USA, 2015. PDF pdf
  • Eugene Kim, Jinkyu Lee, and Kang G. Shin. Modeling and Real-time Scheduling of Large-Scale Batteries for Maximizing Performance. IEEE RTSS’15, San Antonio, Texas, USA, 2015. PDF pdf PDF supplement